Amir H. Gandomi is a Professor of Data Science at the Faculty of Engineering & Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA and a distinguished research fellow in BEACON center, Michigan State University, USA. Prof. Gandomi has published over one hundred and sixty journal papers and five books which collectively have been cited more than 13,000 times (H-index = 56). He has been named as one of the most influential scientific mind and Highly Cited Researcher (top 1%) for three consecutive years, 2017 to 2019. He also ranked 19th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO and IEEE TBD. Prof Gandomi is active in delivering keynote and invited talks. His research interests are global optimization and (big) data mining using machine learning and evolutionary computations in particular.
Can supervise: YES
- Data Analytics
- Evolutionary Computation
- Genetic Programming
- Operation Research
- Artificial Intelligence
- Smart Cities and Systems
- Data Driven IoT
- Evolutionary Computation
- Multivariate Data Analysis
© Springer International Publishing Switzerland 2015. This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.
Analysis of Turbulent Flows is written by one of the most prolific authors in the field of CFD. Professor Tuncer Cebeci calls on both his academic and industrial experience from teaching aerodynamics at SUPAERO, and directing DMAE at ONERA when presenting this work. Each chapter has been specifically constructed to provide a comprehensive overview of turbulent flow and its measurement. Analysis of Turbulent Flows serves as an advanced textbook for PhD candidates working in the field of CFD, making this book essential reading for researchers, practitioners in industry and MSc and MEng students. Key features include; An overview of the development and application of Computational Fluid Dynamics (CFD), with real applications to industry A free-to-access companion website which contains computer programs suitable for solving non-linear equations that arise in modeling turbulent flows Contains a unique section on 'Short-cut' methods - simple approaches to practical engineering problems. About the author; Professor Tuncer Cebeci Chair of the Department of Aerospace Engineering, California State University, Professor Cebeci is widely regarded as an expert in the field of Turbulent Flows and has received many accolades for his work. He was named the first Distinguished Professor in the California State University System, and he received numerous awards including Fellow of the American Institute of Aeronautics and Astronautics. He also received the Presidential Science Award from Turkey. An overview of the development and application of Computational Fluid Dynamics (CFD), with real applications to industry. A free-to-access companion website which contains computer programs suitable for solving non-linear equations that arise in modeling turbulent flows. Contains a unique section on 'Short-cut' methods - simple approaches to practical engineering problems. © 2013 Elsevier Inc. All rights reserved.
Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book. © 2013 Elsevier Inc. All rights reserved.
Manikandan, R, Patan, R, Gandomi, AH, Sivanesan, P & Kalyanaraman, H 2020, 'Hash polynomial two factor decision tree using IoT for smart health care scheduling', Expert Systems with Applications, vol. 141.View/Download from: Publisher's site
© 2019 The steady growth of an aging population and increased frequency of chronic disease led to the development of Smart Health Care (SHC) systems. While patient prioritization is the core of any SHC system, handling the response time by medical practitioners is a prevailing challenge. With advancements in information technology, the concept of the Internet of Things (IoT) has made it possible to integrate SHC systems with the Cloud environment to not only ensure patient prioritization according to disease prevalence, but also to minimize response time. In this work, an IoT-based scheduling method, called the Hash Polynomial Two-factor Decision Tree (HP-TDT) is proposed to increase scheduling efficiency and reduce response time by classifying patients as being normal or in a critical state in minimal time. The HP-TDT scheduling method involves three stages including the registration stage, the data collection stage, and the scheduling stage. The registration phase is carried out through Open Address Hashing (OAH) model for reducing the key generation response time. Next, the data collection stage is performed using the Polynomial Data Collection (PDC) algorithm. By incorporating PDC, computation overhead is reduced because a number of operations are considered during data collection. Finally, scheduling is performed by applying two-factor, entropy and information gain according to a decision tree. With this, scheduling efficiency is improved due to the classification of patients as being normal or in a critical state. The proposed method minimizes response time, computational overhead, and improves essential scheduling efficiency.
Akhani, M, Kashani, AR, Mousavi, M & Gandomi, AH 2019, 'A hybrid computational intelligence approach to predict spectral acceleration', MEASUREMENT, vol. 138, pp. 578-589.View/Download from: UTS OPUS or Publisher's site
Behera, TM, Mohapatra, SK, Samal, UC, Khan, MS, Daneshmand, M & Gandomi, AH 2019, 'Residual Energy-Based Cluster-Head Selection in WSNs for IoT Application', IEEE INTERNET OF THINGS JOURNAL, vol. 6, no. 3, pp. 5132-5139.View/Download from: Publisher's site
Cheng, R, Omidvar, MN, Gandomi, AH, Sendhoff, B, Menzel, S & Yao, X 2019, 'Solving Incremental Optimization Problems via Cooperative Coevolution', IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol. 23, no. 5, pp. 762-775.View/Download from: Publisher's site
Dhingra, S, Madda, RB, Gandomi, AH, Patan, R & Daneshmand, M 2019, 'Internet of Things Mobile-Air Pollution Monitoring System (IoT-Mobair)', IEEE INTERNET OF THINGS JOURNAL, vol. 6, no. 3, pp. 5577-5584.View/Download from: Publisher's site
Doborjeh, M, Kasabov, N, Doborjeh, Z, Enayatollahi, R, Tu, E & Gandomi, AH 2019, 'Personalised modelling with spiking neural networks integrating temporal and static information', NEURAL NETWORKS, vol. 119, pp. 162-177.View/Download from: Publisher's site
Gandomi, AH, Deb, K, Averill, RC, Rahnamayan, S & Omidvar, MN 2019, 'Using semi-independent variables to enhance optimization search', EXPERT SYSTEMS WITH APPLICATIONS, vol. 120, pp. 279-297.View/Download from: Publisher's site
Govindarajan, P, Soundarapandian, RK, Gandomi, AH, Patan, R, Jayaraman, P & Manikandan, R 2019, 'Classification of stroke disease using machine learning algorithms', Neural Computing and Applications.View/Download from: Publisher's site
© 2019, Springer-Verlag London Ltd., part of Springer Nature. This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients' symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.
Kasinathan, G, Jayakumar, S, Gandomi, AH, Ramachandran, M, Fong, SJ & Patan, R 2019, 'Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier', EXPERT SYSTEMS WITH APPLICATIONS, vol. 134, pp. 112-119.View/Download from: UTS OPUS or Publisher's site
Khari, M, Garg, AK, Gandomi, AH, Gupta, R, Patan, R & Balusamy, B 2019, 'Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-8.View/Download from: Publisher's site
Krishnamurthi, R, Patan, R & Gandomi, AH 2019, 'Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition', Future Generation Computer Systems, vol. 98, pp. 650-659.View/Download from: UTS OPUS or Publisher's site
© 2019 Elsevier B.V. In this modern era, the use of computer technology and computing devices play significant role in every day human activities. From the disabled people perspective, there is huge demand to improve Human–Computer Interaction (HCI), to overcome their difficulty in using the standard interactive devices. Basically, HCI provides a way for humans to interact with a computer using a keyboard, a mouse, and other input devices in real-time. This paper proposes a novel assistive pointer device called Frontier Point method (FPM), which is based on a hand movement recognition technique. The proposed hand movement recognition technique primarily focuses on the direction of hand movement for dynamic recognition in real-time using least square fitting and virtual frame techniques. Next based on boundary values, such that if the hand crosses a boundary value of a given quadrant, then a SENDKEY stroke is generated that corresponds to that range. This method is implemented with the help of a depth sensor camera called Kinect. Kinect takes the RGB data and depth data of the human skeleton and generates coordinate information corresponding to specific body joints. Experiments were conducted in which different users were evaluated for their ability to navigate a PowerPoint presentation multiple times. Collectively, an average recognition time of 2.386 s was calculated with an average recognition rate of 97.37%.
Kumar, A, Ramachandran, M, Gandomi, AH, Patan, R, Lukasik, S & Soundarapandian, RK 2019, 'A deep neural network based classifier for brain tumor diagnosis', Applied Soft Computing Journal, vol. 82.View/Download from: UTS OPUS or Publisher's site
© 2019 Elsevier B.V. Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.
Mousavi, M, Holloway, D, Olivier, JC, Alavi, AH & Gandomi, AH 2019, 'A Shannon entropy approach for structural damage identification based on self-powered sensor data', Engineering Structures, vol. 200.View/Download from: UTS OPUS or Publisher's site
© 2019 Elsevier Ltd Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions.
Poongodi, T, Khan, MS, Patan, R, Gandomi, AH & Balusamy, B 2019, 'Robust Defense Scheme Against Selective Drop Attack in Wireless Ad Hoc Networks', IEEE ACCESS, vol. 7, pp. 18409-18419.View/Download from: Publisher's site
Sarveghadi, M, Gandomi, AH, Bolandi, F & Alavi, AH 2019, 'Development of prediction models for shear strength of SFRCB using a machine learning approach', NEURAL COMPUTING & APPLICATIONS, vol. 31, no. 7, pp. 2085-2094.View/Download from: Publisher's site
Sekaran, K, Khan, MS, Patan, R, Gandomi, AH, Krishna, PV & Kallam, S 2019, 'Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach', IEEE Access, vol. 7, pp. 30203-30212.View/Download from: Publisher's site
© 2013 IEEE. Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments.
Selvaraj, A, Patan, R, Gandomi, AH, Deverajan, GG & Pushparaj, M 2019, 'Optimal virtual machine selection for anomaly detection using a swarm intelligence approach', APPLIED SOFT COMPUTING, vol. 84.View/Download from: Publisher's site
© 2019, Springer-Verlag London Ltd., part of Springer Nature. Abstract: In the real world, we often come across conditions like optimization of more than one objective functions concurrently which are of conflicting nature and that makes the prospect of the problem more intricate. To overpower this contrasting state, an efficient meta-heuristic (MH) is required, which provides a balanced trade-off between diverging objective functions and gives an optimum set of solutions. In this article, a recently proposed MH called Heat Transfer Search (HTS) algorithm is enforced to elucidate the structural optimization problems with Multi-objective functions (described as MOHTS). MOHTS is an efficient MH which works on the principle of heat transfer and thermodynamics, where search agents are molecules which interact with other molecules and with surrounding through conduction, convection, and radiation modes of heat transfer. Five challenging benchmark problems of truss optimization have been taken into consideration here to examine the effectiveness of MOHTS. Procure results through the proposed method show the predominance over considered MHs. These benchmark problems are considered for discrete design variables for the structural optimization problem with two objectives, namely minimization of truss weight and maximization of nodal displacement. Here, the Pareto-optimal front achieved through computational experiments, in the process of optimization, is evaluated by three distinct performance quality indicators namely the Hypervolume, the Front spacing metric, and Inverted Generational Distance. Also, the obtained results after a number of runs are compared with other existing optimizers in the literature like multi-objective ant system, multi-objective ant colony system, and multi-objective symbiotic organism search, which manifest the superiority in the performance of the proposed algorithm over others. The statistical analysis of the experimental work has been carried...
Tejani, GG, Pholdee, N, Bureerat, S, Prayogo, D & Gandomi, AH 2019, 'Structural optimization using multi-objective modified adaptive symbiotic organisms search', EXPERT SYSTEMS WITH APPLICATIONS, vol. 125, pp. 425-441.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Alavi, AH & Gong, D 2019, 'A comprehensive review of krill herd algorithm: variants, hybrids and applications', ARTIFICIAL INTELLIGENCE REVIEW, vol. 51, no. 1, pp. 119-148.View/Download from: Publisher's site
Zamani, H, Nadimi-Shahraki, MH & Gandomi, AH 2019, 'CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems', Applied Soft Computing Journal.View/Download from: Publisher's site
© 2019 Elsevier B.V. In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design.
Aslani, M, Ghasemi, P & Gandomi, AH 2018, 'Constrained mean-variance mapping optimization for truss optimization problems', STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, vol. 27, no. 6.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2018, 'Metaheuristics in Reliability and Risk Analysis', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol. 4, no. 3, pp. 02018001-02018001.View/Download from: Publisher's site
© 2017 Elsevier Ltd The parameter-less population pyramid (P3) is a recent evolutionary computation algorithm proposed for black box optimization. Shown to be efficient for a variety of benchmark problems, P3 replaces the conventional constant population model with expanding sets of expanding populations. We investigated how this new metaheuristic optimization algorithm would transfer to optimize large-scale tower structure problems involving different constraints: geometric and mechanical. P3 is examined by optimizing two discrete tower design problems, 26-story and 35- story tower structures. The performance of P3 is compared with other well-known evolutionary algorithms for black-box optimization including random restart hill climbing, parameter-less hierarchical Bayesian optimization algorithm, differential evolution, and a modified genetic algorithm. The results show that does P3 not only finds the best final solutions, but it also reaches high quality solutions much faster than the other algorithms This fast optimization is vital for the tedious and large-scale structural engineering problems. Finally, the unique search features used in the P3 and the implications for future studies are discussed.
Gandomi, AH & Kashani, AR 2018, 'Automating pseudo-static analysis of concrete cantilever retaining wall using evolutionary algorithms', MEASUREMENT, vol. 115, pp. 104-124.View/Download from: Publisher's site
Gandomi, AH & Kashani, AR 2018, 'Construction Cost Minimization of Shallow Foundation Using Recent Swarm Intelligence Techniques', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 14, no. 3, pp. 1099-1106.View/Download from: Publisher's site
Gandomi, AH & Kashani, AR 2018, 'Probabilistic evolutionary bound constraint handling for particle swarm optimization', Operational Research, vol. 18, no. 3, pp. 801-823.View/Download from: Publisher's site
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Keeping the search space between the valid domains is one of the most important necessities for most of the optimization problems. Among the optimization algorithms, particle swarm optimization (PSO) is highly likely to violate boundary limitations easily because of its oscillating behavior. Therefore, PSO is led to be sensitive to bound constraint handling (BCH) method. This matter has not been taken to account very much until now. This study attempt to apply and explore the efficiency of one of the most recent BCH schemes called evolutionary boundary constraint handling (EBCH) on PSO. In addition, probabilistic evolutionary boundary constraint handling (PEBCH) is also introduced in this study as an update on EBCH approach. As a complementary step of previous efforts, in the current document, PSO with both EBCH and PEBCH are utilized to solve several benchmark functions and the results are compared to other approaches in the literature. The results reveal that, in most cases, the EBCH and PEBCH can considerably improve the performance of the PSO algorithm in comparison with other BCH methods.
Gharehbaghi, S, Gandomi, AH, Achakpour, S & Omidvar, MN 2018, 'A hybrid computational approach for seismic energy demand prediction', EXPERT SYSTEMS WITH APPLICATIONS, vol. 110, pp. 335-351.View/Download from: Publisher's site
Kurugodu, H, Bordoloi, S, Hong, Y, Garg, A, Garg, A, Sreedeep, S & Gandomi, AH 2018, 'Genetic programming for soil-fiber composite assessment', ADVANCES IN ENGINEERING SOFTWARE, vol. 122, pp. 50-61.View/Download from: Publisher's site
Mousavi, M & Gandomi, AH 2018, 'An input-output damage detection method using static equivalent formulation of dynamic vibration', ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING, vol. 18, no. 2, pp. 508-514.View/Download from: Publisher's site
Nagasubramanian, G, Sakthivel, RK, Patan, R, Gandomi, AH, Sankayya, M & Balusamy, B 2018, 'Securing e-health records using keyless signature infrastructure blockchain technology in the cloud', Neural Computing and Applications.View/Download from: Publisher's site
© 2018, Springer-Verlag London Ltd., part of Springer Nature. Health record maintenance and sharing are one of the essential tasks in the healthcare system. In this system, loss of confidentiality leads to a passive impact on the security of health record whereas loss of integrity leads can have a serious impact such as loss of a patient's life. Therefore, it is of prime importance to secure electronic health records. Health records are represented by Fast Healthcare Interoperability Resources standards and managed by Health Level Seven International Healthcare Standards Organization. Centralized storage of health data is attractive to cyber-attacks and constant viewing of patient records is challenging. Therefore, it is necessary to design a system using the cloud that helps to ensure authentication and that also provides integrity to health records. The keyless signature infrastructure used in the proposed system for ensuring the secrecy of digital signatures also ensures aspects of authentication. Furthermore, data integrity is managed by the proposed blockchain technology. The performance of the proposed framework is evaluated by comparing the parameters like average time, size, and cost of data storage and retrieval of the blockchain technology with conventional data storage techniques. The results show that the response time of the proposed system with the blockchain technology is almost 50% shorter than the conventional techniques. Also they express the cost of storage is about 20% less for the system with blockchain in comparison with the existing techniques.
Tahmassebi, A & Gandomi, AH 2018, 'Building energy consumption forecast using multi-objective genetic programming', Measurement: Journal of the International Measurement Confederation, vol. 118, pp. 164-171.View/Download from: Publisher's site
© 2018 Elsevier Ltd A multi-objective genetic programming (MOGP) technique with multiple genes is proposed to formulate the energy performance of residential buildings. Here, it is assumed that loads have linear relation in terms of genes. On this basis, an equation is developed by MOGP method to predict both heating and cooling loads. The proposed evolutionary approach optimizes the most significant predictor input variables in the model for both accuracy and complexity, while simultaneously solving the unknown parameters of the model. In the proposed energy performance model, relative compactness has the most and orientation the least contribution. The proposed MOGP model is simple and has a high degree of accuracy. The results show that MOGP is a suitable tool to generate solid models for complex nonlinear systems with capability of solving big data problems via parallel algorithms.
Tahmassebi, A, Gandomi, AH, Fong, S, Meyer-Baese, A & Foo, SY 2018, 'Multi-stage optimization of a deep model: A case study on ground motion modeling', PLOS ONE, vol. 13, no. 9.View/Download from: Publisher's site
Tahmassebi, A, Gandomi, AH, Schulte, MHJ, Goudriaan, AE, Foo, SY & Meyer-Baese, A 2018, 'Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification', Complexity, vol. 2018.View/Download from: Publisher's site
© 2018 Amirhessam Tahmassebi et al. This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.
Abualigah, LM, Khader, AT, Hanandeh, ES & Gandomi, AH 2017, 'A novel hybridization strategy for krill herd algorithm applied to clustering techniques', Applied Soft Computing Journal, vol. 60, pp. 423-435.View/Download from: Publisher's site
© 2017 Elsevier B.V. Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms.
Ahmadi, M, Naderpour, H, Kheyroddin, A & Gandomi, AH 2017, 'Seismic failure probability and vulnerability assessment of steel-concrete composite structures', Periodica Polytechnica Civil Engineering, vol. 61, no. 4, pp. 939-950.View/Download from: Publisher's site
© 2017, Budapest University of Technology and Economics. All rights reserved. Building collapse in earthquakes caused huge losses, both in human and economic terms. To assess the risk posed by using the composite members, this paper investigates seismic failure probability and vulnerability assessment of steel-concrete composite structures constituted by rectangular concrete filled steel tube (RCFT) columns and steel beams. To enable numerical simulation of RCFT-structure, the details of components modeling are developed using OpenSEES finite element analysis package and the validation of proposed procedure is investigated through comparisons with available experimental results. The seismic fragility and vulnerability curves of RCFT-structures are created through nonlinear dynamic analysis using an appropriate suite of ground motions for seismic loss assessment. These curves developed for three-, six- and nine-story prototypes of RCFT-structure. Fragility curves are an appropriate tool for representing the seismic failure probabilities and vulnerability curves demonstrate a probability of exceeding loss to a measure of ground motion intensity.
Babanajad, SK, Gandomi, AH & Alavi, AH 2017, 'New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach', ADVANCES IN ENGINEERING SOFTWARE, vol. 110, pp. 55-68.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Gandomi, M & Kazemi, S 2017, 'Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement', MEASUREMENT, vol. 95, pp. 367-376.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR & Zeighami, F 2017, 'Retaining wall optimization using interior search algorithm with different bound constraint handling', INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, vol. 41, no. 11, pp. 1304-1331.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Mousavi, M & Jalalvandi, M 2017, 'Slope stability analysis using evolutionary optimization techniques', INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, vol. 41, no. 2, pp. 251-264.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Roke, DA & Mousavi, M 2017, 'Optimization of retaining wall design using evolutionary algorithms', STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, vol. 55, no. 3, pp. 809-825.View/Download from: Publisher's site
Gholampour, A, Gandomi, AH & Ozbakkaloglu, T 2017, 'New formulations for mechanical properties of recycled aggregate concrete using gene expression programming', CONSTRUCTION AND BUILDING MATERIALS, vol. 130, pp. 122-145.View/Download from: Publisher's site
Kiani, B, Sajedi, S, Gandomi, AH, Huang, Q & Liang, RY 2017, 'Optimal adjustment of ACI formula for shrinkage of concrete containing pozzolans', CONSTRUCTION AND BUILDING MATERIALS, vol. 131, pp. 485-495.View/Download from: Publisher's site
Mirjalili, S, Gandomi, AH, Mirjalili, SZ, Saremi, S, Faris, H & Mirjalili, SM 2017, 'Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems', Advances in Engineering Software, vol. 114, pp. 163-191.View/Download from: Publisher's site
© 2017 Elsevier Ltd This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
Sajedi, S, Huang, Q, Gandomi, AH & Kiani, B 2017, 'Reliability-Based Multiobjective Design Optimization of Reinforced Concrete Bridges Considering Corrosion Effect', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol. 3, no. 3.View/Download from: Publisher's site
© 2016 American Society of Civil Engineers. Chloride-induced corrosion is known as the dominant cause of premature damage in reinforced concrete (RC) bridges in the United States. However, the current corrosion management strategies do not suggest an optimum design procedure for RC bridges in corrosive environments considering both reliability and cost. In this paper, a module based on a reliability-based multiobjective design optimization (RB-MODO) technique using a nondominated sorting genetic algorithm is proposed for the optimum design of RC bridge beams considering corrosion. The procedure simultaneously maximizes the reliability of the structure and minimizes the material costs, given a design service life. As an illustration, the developed procedure is used for flexural design of interior T beams of a RC bridge with and without considering corrosion effect subjected to various design constraints and service lives. Three types of materials are used in the design process: normal strength concrete with black steel rebars, normal strength concrete with epoxy-coated rebars, and high-performance concrete with black steel rebars. Lastly, the optimum design strategy is selected among the considered materials based on the Pareto front results obtained from the proposed RB-MODO procedure.
Shahrara, N, Celik, T & Gandomi, AH 2017, 'GENE EXPRESSION PROGRAMMING APPROACH TO COST ESTIMATION FORMULATION FOR UTILITY PROJECTS', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 23, no. 1, pp. 85-95.View/Download from: Publisher's site
Shahrara, N, Celik, T & Gandomi, AH 2017, 'RISK ANALYSIS OF BOT CONTRACTS USING SOFT COMPUTING', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 23, no. 2, pp. 232-240.View/Download from: Publisher's site
Asghari, A & Gandomi, AH 2016, 'Ductility reduction factor and collapse mechanism evaluation of a new steel knee braced frame', STRUCTURE AND INFRASTRUCTURE ENGINEERING, vol. 12, no. 2, pp. 239-255.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2016, 'AN INTRODUCTION OF KRILL HERD ALGORITHM FOR ENGINEERING OPTIMIZATION', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 22, no. 3, pp. 302-310.View/Download from: Publisher's site
Gandomi, AH, Sajedi, S, Kiani, B & Huang, Q 2016, 'Genetic programming for experimental big data mining: A case study on concrete creep formulation', AUTOMATION IN CONSTRUCTION, vol. 70, pp. 89-97.View/Download from: Publisher's site
Gandomi, M, Soltanpour, M, Zolfaghari, MR & Gandomi, AH 2016, 'Prediction of peak ground acceleration of Iran's tectonic regions using a hybrid soft computing technique', GEOSCIENCE FRONTIERS, vol. 7, no. 1, pp. 75-82.View/Download from: Publisher's site
Kashani, AR, Gandomi, AH & Mousavi, M 2016, 'Imperialistic Competitive Algorithm: A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes', GEOSCIENCE FRONTIERS, vol. 7, no. 1, pp. 83-89.View/Download from: Publisher's site
Kiani, B, Gandomi, AH, Sajedi, S & Liang, RY 2016, 'New Formulation of Compressive Strength of Preformed-Foam Cellular Concrete: An Evolutionary Approach', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 28, no. 10.View/Download from: Publisher's site
Mousavi, M & Gandomi, AH 2016, 'A hybrid damage detection method using dynamic-reduction transformation matrix and modal force error', ENGINEERING STRUCTURES, vol. 111, pp. 425-434.View/Download from: Publisher's site
Wang, G-G, Deb, S, Gandomi, AH & Alavi, AH 2016, 'Opposition-based krill herd algorithm with Cauchy mutation and position clamping', NEUROCOMPUTING, vol. 177, pp. 147-157.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Alavi, AH & Deb, S 2016, 'A hybrid method based on krill herd and quantum-behaved particle swarm optimization', NEURAL COMPUTING & APPLICATIONS, vol. 27, no. 4, pp. 989-1006.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Alavi, AH & Deb, S 2016, 'A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization', INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, vol. 25, no. 2.View/Download from: Publisher's site
Wang, GG, Gandomi, AH, Yang, XS & Alavi, AH 2016, 'A new hybrid method based on krill herd and cuckoo search for global optimisation tasks', International Journal of Bio-Inspired Computation, vol. 8, no. 5, pp. 286-299.View/Download from: Publisher's site
©2016 Inderscience Enterprises Ltd. Recently, Gandomi and Alavi proposed a new heuristic search method, called krill herd (KH), for solving global optimisation problems. In order to make KH more effective, a hybrid meta-heuristic cuckoo search and krill herd (CSKH) method is proposed for function optimisation. The CSKH introduces krill updating (KU) and krill abandoning (KA) operator originated from cuckoo search (CS) during the process when the krill updating so as to greatly enhance its effectiveness and reliability dealing with numerical optimisation problems. The KU operator inspires the intensive exploitation and allows the krill individuals implement a careful search in the later run phase of the search, while KA operator is used to further enhance the exploration of the CSKH in place of a fraction of the worse krill at the end of each generation. The effectiveness of these improvements is tested by 14 standard benchmarking functions and experimental results show, in most cases, this hybrid meta-heuristic CSKH algorithm is more effective and efficient than the original KH and other approaches.
Wang, G-G, Gandomi, AH, Yang, X-S & Alavi, AH 2016, 'A new hybrid method based on krill herd and cuckoo search for global optimisation tasks', INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, vol. 8, no. 5, pp. 286-299.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Zhao, X & Chu, HCE 2016, 'Hybridizing harmony search algorithm with cuckoo search for global numerical optimization', SOFT COMPUTING, vol. 20, no. 1, pp. 273-285.View/Download from: Publisher's site
Gandomi, AH & Roke, DA 2015, 'Assessment of artificial neural network and genetic programming as predictive tools', ADVANCES IN ENGINEERING SOFTWARE, vol. 88, pp. 63-72.View/Download from: Publisher's site
Gandomi, AH & Yun, GJ 2015, 'Coupled SelfSim and genetic programming for non- linear material constitutive modelling', INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, vol. 23, no. 7, pp. 1101-1119.View/Download from: Publisher's site
Gandomi, AH, Faramarzifar, A, Ghanad Rezaee, P, Asghari, A & Talatahari, S 2015, 'New design equations for elastic modulus of concrete usingmulti expression programming', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 21, no. 6, pp. 761-774.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Mousavi, M & Jalalvandi, M 2015, 'Slope stability analyzing using recent swarm intelligence techniques', INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, vol. 39, no. 3, pp. 295-309.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Roke, DA & Mousavi, M 2015, 'Optimization of retaining wall design using recent swarm intelligence techniques', ENGINEERING STRUCTURES, vol. 103, pp. 72-84.View/Download from: Publisher's site
Hosseini, SSS, Yang, XS, Gandomi, AH & Nemati, A 2015, 'Solutions of non-smooth economic dispatch problems by swarm intelligence', Adaptation, Learning, and Optimization, vol. 18, pp. 129-146.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2015. The increasing costs of fuels and operations of power generating units necessitate the development of optimization methods for economic dispatch (ED) problems. Classical optimization techniques such as direct search and gradient methods often fail to find global optimum solutions. Modern optimization techniques are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This chapter presents a novel method to determine the feasible optimal solutions of the ED problems utilizing the newly developed Bat Algorithm (BA). The proposed BA is based on the echolocation behavior of bats. This technique is adapted to solve non-convex ED problems under different nonlinear constraints such as transmission losses, ramp rate limits, multi-fuel options and prohibited operating zones. Parameters are tuned to give the best results for these problems. To describe the efficiency and applicability of the proposed algorithm, we will use four ED test systems with non-convexity. We will compare our results with some of the most recently published ED solution methods. Comparing with the other existing techniques, the proposed approach can find better solutions than other methods. This method can be deemed to be a promising alternative for solving the ED problems in real systems.
Kennedy, MJ, Gandomi, AH & Miller, CM 2015, 'Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal', Journal of Environmental Chemical Engineering, vol. 3, no. 4, pp. 2829-2838.View/Download from: Publisher's site
© 2015 Elsevier Ltd. In this study, four different neural network models were evaluated for predicting both turbidity and dissolved organic matter (DOM) removal during the coagulation process at the Akron Water Treatment Plant (Akron, Ohio, USA). DOM was monitored and characterized using fluorescence spectroscopy and parallel factor (PARAFAC) analysis, building upon previous research which identified three unique fluorescence components (C1, C2, and C3). Neural network models were built using operational data to predict each of the fluorescence components and turbidity after coagulation based on variable raw water quality and chemical doses. Correlation coefficients between measured and model predicted values for the final turbidity, C1, C2, and C3 models on an unseen test data set were 0.91, 0.95, 0.97, and 0.51, respectively. The predictive capability of the top performing model for each parameter was evaluated using parametric analysis, external validation criteria, and the absolute relative error distribution. Results suggest that the models for settled turbidity and the three settled component scores are valid and can be used to predict the removal of individual fractions of DOM (as measured by PARAFAC components) as a function of chemical dose and raw water quality, providing the water plant the ability to simultaneously manage two key water quality treatment objectives.
Mirzahosseini, M, Najjar, YM, Alavi, AH & Gandomi, AH 2015, 'Next-Generation Models for Evaluation of the Flow Number of Asphalt Mixtures', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 15, no. 6.View/Download from: Publisher's site
Talatahari, S, Gandomi, AH, Yang, X-S & Deb, S 2015, 'Optimum design of frame structures using the Eagle Strategy with Differential Evolution', ENGINEERING STRUCTURES, vol. 91, pp. 16-25.View/Download from: Publisher's site
Wang, GG, Gandomi, AH & Alavi, AH 2015, 'Study of lagrangian and evolutionary parameters in krill herd algorithm', Adaptation, Learning, and Optimization, vol. 18, pp. 111-128.View/Download from: Publisher's site
© 2015, Springer International Publishing Switzerland. Krill Herd (KH) is a novel swarm-based intelligent optimization method developed through the idealization of the krill swarm. In the basic KH method, all the movement parameters used are originated from real nature-driven data found in the literature. The parameter setting based on such data is not necessarily the best selection. In this work, a systematic method is presented for the selection of the best parameter setting for the KH algorithm through an extensive study of arrays of high-dimensional benchmark problems. An important finding is that the best performance of KH can be obtained by setting effective coefficient of the krill individual (Cbest), food coefficient (Cfood), maximum diffusion speed (Dmax), crossover probability (Cr) and mutation probability (Mu) parameters to 4.00, 4.25, 0.014, 0.225, and 0.025, respectively. This finding would eliminate the concerns regarding the optimal tuning of the KH algorithm for its most future applications.
Bagheri, M, Borhani, TNG, Gandomi, AH & Manan, ZA 2014, 'A simple modelling approach for prediction of standard state real gas entropy of pure materials', SAR AND QSAR IN ENVIRONMENTAL RESEARCH, vol. 25, no. 9, pp. 695-710.View/Download from: Publisher's site
Bayazidi, AM, Wang, G-G, Bolandi, H, Alavi, AH & Gandomi, AH 2014, 'Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete', MATHEMATICAL PROBLEMS IN ENGINEERING.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Asghari, A, Niroomand, H & Nazar, AM 2014, 'An innovative approach for modeling of hysteretic energy demand in steel moment resisting frames', NEURAL COMPUTING & APPLICATIONS, vol. 24, no. 6, pp. 1285-1291.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Kazemi, S & Gandomi, M 2014, 'Formulation of shear strength of slender RC beams using gene expression programming, part I: Without shear reinforcement', AUTOMATION IN CONSTRUCTION, vol. 42, pp. 112-121.View/Download from: Publisher's site
Gandomi, AH, Mohammadzadeh, DS, Luis Perez-Ordonez, J & Alavi, AH 2014, 'Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups', APPLIED SOFT COMPUTING, vol. 19, pp. 112-120.View/Download from: Publisher's site
Guo, L, Wang, G-G, Gandomi, AH, Alavi, AH & Duan, H 2014, 'A new improved krill herd algorithm for global numerical optimization', NEUROCOMPUTING, vol. 138, pp. 392-402.View/Download from: Publisher's site
Talatahari, E, Talatahari, S, Gandomi, AH & Yang, XS 2014, 'Advances of Swarm Intelligent Systems in Gene Expression Data Classification', JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, vol. 22, no. 3, pp. 307-315.
Talatahari, S, Gandomi, AH & Yun, GJ 2014, 'Optimum design of tower structures using Firefly Algorithm', STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, vol. 23, no. 5, pp. 350-361.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH & Alavi, AH 2014, 'An effective krill herd algorithm with migration operator in biogeography-based optimization', APPLIED MATHEMATICAL MODELLING, vol. 38, no. 9-10, pp. 2454-2462.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Alavi, AH & Hao, G-S 2014, 'Hybrid krill herd algorithm with differential evolution for global numerical optimization', NEURAL COMPUTING & APPLICATIONS, vol. 25, no. 2, pp. 297-308.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Yang, X-S & Alavi, AH 2014, 'A novel improved accelerated particle swarm optimization algorithm for global numerical optimization', ENGINEERING COMPUTATIONS, vol. 31, no. 7, pp. 1198-1220.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Nejad, HC, Mollahasani, A & Rashed, A 2013, 'Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems', NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 6, pp. 1771-1786.View/Download from: Publisher's site
Aminian, P, Niroomand, H, Gandomi, AH, Alavi, AH & Esmaeili, MA 2013, 'New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach', NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 1, pp. 119-131.View/Download from: Publisher's site
Babanajad, SK, Gandomi, AH, Mohammadzadeh S, D & Alavi, AH 2013, 'Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming', AUTOMATION IN CONSTRUCTION, vol. 36, pp. 136-144.View/Download from: Publisher's site
Bagheri, M, Gandomi, AH, Bagheri, M & Shahbaznezhad, M 2013, 'Multi-expression programming based model for prediction of formation enthalpies of nitro-energetic materials', EXPERT SYSTEMS, vol. 30, no. 1, pp. 66-78.View/Download from: Publisher's site
Gandomi, A & Zolfaghari, S 2013, 'Profitability of loyalty reward programs: An analytical investigation', OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, vol. 41, no. 4, pp. 797-807.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Shadmehri, DM & Sahab, MG 2013, 'An empirical model for shear capacity of RC deep beams using genetic-simulated annealing', ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING, vol. 13, no. 3, pp. 354-369.View/Download from: Publisher's site
Gandomi, AH, Talatahari, S, Tadbiri, F & Alavi, AH 2013, 'Krill herd algorithm for optimum design of truss structures', INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, vol. 5, no. 5, pp. 281-288.View/Download from: Publisher's site
Gandomi, AH, Talatahari, S, Yang, X-S & Deb, S 2013, 'Design optimization of truss structures using cuckoo search algorithm', STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, vol. 22, no. 17, pp. 1330-1349.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S & Alavi, AH 2013, 'Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems', ENGINEERING WITH COMPUTERS, vol. 29, no. 1, pp. 17-35.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S & Alavi, AH 2013, 'Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (vol 29, pg 17, 2013)', ENGINEERING WITH COMPUTERS, vol. 29, no. 2, pp. 245-245.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S, Alavi, AH & Talatahari, S 2013, 'Bat algorithm for constrained optimization tasks', NEURAL COMPUTING & APPLICATIONS, vol. 22, no. 6, pp. 1239-1255.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S, Talatahari, S & Alavi, AH 2013, 'Firefly algorithm with chaos', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 18, no. 1, pp. 89-98.View/Download from: Publisher's site
Gandomi, AH, Yun, GJ & Alavi, AH 2013, 'An evolutionary approach for modeling of shear strength of RC deep beams', MATERIALS AND STRUCTURES, vol. 46, no. 12, pp. 2109-2119.View/Download from: Publisher's site
Gandomi, AH, Yun, GJ, Yang, X-S & Talatahari, S 2013, 'Chaos-enhanced accelerated particle swarm optimization', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 18, no. 2, pp. 327-340.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Mollahasani, A, Gandomi, AH & Esmaeili, MA 2013, 'Formulation of soil angle of shearing resistance using a hybrid GP and OLS method', ENGINEERING WITH COMPUTERS, vol. 29, no. 1, pp. 37-53.View/Download from: Publisher's site
Talatahari, S, Kheirollahi, M, Farahmandpour, C & Gandomi, AH 2013, 'A multi-stage particle swarm for optimum design of truss structures', NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 5, pp. 1297-1309.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH & Alavi, AH 2013, 'A chaotic particle-swarm krill herd algorithm for global numerical optimization', KYBERNETES, vol. 42, no. 6, pp. 962-978.View/Download from: Publisher's site
Yun, GJ, Rahimi, MR, Gandomi, AH, Lim, G-C & Choi, J-S 2013, 'Stress sensing performance using mechanoluminescence of SrAl2O4:Eu (SAOE) and SrAl2O4:Eu, Dy (SAOED) under mechanical loadings', SMART MATERIALS AND STRUCTURES, vol. 22, no. 5.View/Download from: Publisher's site
Alavi, AH & Gandomi, AH 2012, 'Discussion on "Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures" by Ismail Dincer [Adv. Eng. Software 42 (2011) 160-171]', ADVANCES IN ENGINEERING SOFTWARE, vol. 52, pp. 44-46.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Mousavi, SM 2012, 'Discussion on "Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods"', ENGINEERING GEOLOGY, vol. 137, pp. 107-108.View/Download from: Publisher's site
Alavi, AH, Mollahasani, A, Gandomi, AH & Bazaz, JB 2012, 'Formulation of secant and reloading soil deformation moduli using multi expression programming', ENGINEERING COMPUTATIONS, vol. 29, no. 1-2, pp. 173-197.View/Download from: Publisher's site
Asghari, A, Mirghaderi, R & Gandomi, AH 2012, 'Determination of ultimate load and possible failure path for a rigid strip footing on soil partially supported by retaining wall using an adaptive refinement process', International Journal of Mathematical Modelling and Numerical Optimisation, vol. 3, no. 3, pp. 210-230.View/Download from: Publisher's site
In most branches of soil mechanics and foundation engineering, approximate methods are often used for computing the ultimate load of a footing and slope stability analysis. There are different types of approximate methods. Such as slip-line method, limit equilibrium and limit analysis. Each of the mentioned methods has restrictions in the displacement computing values, failure path, simultaneously satisfying equilibrium equations, fundamental relationships and compatibility equations, and access to the real failure mechanism. Finite element method can be used to overcome some of these restrictions. But finite element method itself has weaknesses. Some of these weaknesses are lack of awareness of suitable element sizes, lack of awareness of error value in analysis process, lack of accurate information about failure path development and costly access to failure mechanism path occurrence. Here, the adaptive refinement based on the rule of gradient recovery and concept of norm was presented as a powerful method for overcoming the restrictions of the general finite element method. This paper also tries to find an access to both the propagation of failure path and the ultimate load value for a rigid strip footing on soil partially supported by a retaining wall. Copyright © 2012 Inderscience Enterprises Ltd.
Bagheri, M, Bagheri, M, Gandomi, AH & Golbraikh, A 2012, 'Simple yet accurate prediction method for sublimation enthalpies of organic contaminants using their molecular structure', THERMOCHIMICA ACTA, vol. 543, pp. 96-106.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2012, 'A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems', NEURAL COMPUTING & APPLICATIONS, vol. 21, no. 1, pp. 171-187.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2012, 'A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems', NEURAL COMPUTING & APPLICATIONS, vol. 21, no. 1, pp. 189-201.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2012, 'Krill herd: A new bio-inspired optimization algorithm', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 17, no. 12, pp. 4831-4845.View/Download from: Publisher's site
Gandomi, AH, Babanajad, SK, Alavi, AH & Farnam, Y 2012, 'Novel Approach to Strength Modeling of Concrete under Triaxial Compression', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 24, no. 9, pp. 1132-1143.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S, Talatahari, S & Deb, S 2012, 'Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization', COMPUTERS & MATHEMATICS WITH APPLICATIONS, vol. 63, no. 1, pp. 191-200.View/Download from: Publisher's site
Hosseini, SSS & Gandomi, AH 2012, 'Short-term load forecasting of power systems by gene expression programming', NEURAL COMPUTING & APPLICATIONS, vol. 21, no. 2, pp. 377-389.View/Download from: Publisher's site
Mollahasani, A, Alavi, AH & Gandomi, AH 2012, 'Reply to Comments on "Empirical modelling of plate load test moduli of soil via gene expression programming" by Ali Mollahasani, Amir Hossein Alavi, Amir Hossein Gandomi [Computers and Geotechnics 38 (2011) 281-286]', COMPUTERS AND GEOTECHNICS, vol. 39, pp. 73-74.View/Download from: Publisher's site
Mousavi, SM, Aminian, P, Gandomi, AH, Alavi, AH & Bolandi, H 2012, 'A new predictive model for compressive strength of HPC using gene expression programming', ADVANCES IN ENGINEERING SOFTWARE, vol. 45, no. 1, pp. 105-114.View/Download from: Publisher's site
Talatahari, S, Azar, BF, Sheikholeslami, R & Gandomi, AH 2012, 'Imperialist competitive algorithm combined with chaos for global optimization', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 17, no. 3, pp. 1312-1319.View/Download from: Publisher's site
Yang, XS & Gandomi, AH 2012, 'Bat algorithm: A novel approach for global engineering optimization', Engineering Computations (Swansea, Wales), vol. 29, no. 5, pp. 464-483.View/Download from: Publisher's site
Purpose - Nature-inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature-inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks. Design/methodology/approach - The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature. Findings - BA has been carefully implemented and carried out optimization for eight well-known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms. Originality/value - The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail. © Emerald Group Publishing Limited.
Yang, X-S, Hosseini, SSS & Gandomi, AH 2012, 'Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect', APPLIED SOFT COMPUTING, vol. 12, no. 3, pp. 1180-1186.View/Download from: Publisher's site
Zargari, SA, Siabil, SZ, Alavi, AH & Gandomi, AH 2012, 'A computational intelligence-based approach for short-term traffic flow prediction', EXPERT SYSTEMS, vol. 29, no. 2, pp. 124-142.View/Download from: Publisher's site
Alavi, AH & Gandomi, AH 2011, 'A robust data mining approach for formulation of geotechnical engineering systems', ENGINEERING COMPUTATIONS, vol. 28, no. 3-4, pp. 242-274.View/Download from: Publisher's site
Alavi, AH & Gandomi, AH 2011, 'Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing', COMPUTERS & STRUCTURES, vol. 89, no. 23-24, pp. 2176-2194.View/Download from: Publisher's site
Alavi, AH, Ameri, M, Gandomi, AH & Mirzahosseini, MR 2011, 'Formulation of flow number of asphalt mixes using a hybrid computational method', CONSTRUCTION AND BUILDING MATERIALS, vol. 25, no. 3, pp. 1338-1355.View/Download from: Publisher's site
Alavi, AH, Aminian, P, Gandomi, AH & Esmaeili, MA 2011, 'Genetic-based modeling of uplift capacity of suction caissons', EXPERT SYSTEMS WITH APPLICATIONS, vol. 38, no. 10, pp. 12608-12618.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Modaresnezhad, M & Mousavi, M 2011, 'New Ground-Motion Prediction Equations Using Multi Expression Programing', JOURNAL OF EARTHQUAKE ENGINEERING, vol. 15, no. 4, pp. 511-536.View/Download from: Publisher's site
Aminian, P, Javid, MR, Asghari, A, Gandomi, AH & Esmaeili, MA 2011, 'A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method', NEURAL COMPUTING & APPLICATIONS, vol. 20, no. 8, pp. 1321-1332.View/Download from: Publisher's site
Divsalar, M, Javid, MR, Gandomi, AH, Soofi, JB & Mahmood, MV 2011, 'HYBRID GENETIC PROGRAMMING-BASED SEARCH ALGORITHMS FOR ENTERPRISE BANKRUPTCY PREDICTION', APPLIED ARTIFICIAL INTELLIGENCE, vol. 25, no. 8, pp. 669-692.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2011, 'Applications of computational intelligence in behavior simulation of concrete materials', Studies in Computational Intelligence, vol. 359, pp. 221-243.View/Download from: Publisher's site
The application of Computational Intelligence (CI) to structural engineering design problems is relatively new. This chapter presents the use of the CI techniques, and specifically Genetic Programming (GP) and Artificial Neural Network (ANN) techniques, in behavior modeling of concrete materials. We first introduce two main branches of GP, namely Tree-based Genetic Programming (TGP) and Linear Genetic Programming (LGP), and two variants of ANNs, called Multi Layer Perceptron (MLP) and Radial Basis Function (RBF). The simulation capabilities of these techniques are further demonstrated by applying them to two conventional concrete material cases. The first case is simulation of concrete compressive strength using mix properties and the second problem is prediction of elastic modulus of concrete using its compressive strength. © 2011 Springer-Verlag Berlin Heidelberg.
Gandomi, AH & Alavi, AH 2011, 'Multi-stage genetic programming: A new strategy to nonlinear system modeling', INFORMATION SCIENCES, vol. 181, no. 23, pp. 5227-5239.View/Download from: Publisher's site
Structural optimization is an important area related to both optimization and structural engineering. Structural optimization problems are often used as benchmarks to validate new optimization algorithms or to test the suitability of a chosen algorithm. In almost all structural engineering applications, it is very important to find the best possible parameters for given design objectives and constraints which are highly non-linear, involving many different design variables. The field of structural optimization is also an area undergoing rapid changes in terms of methodology and design tools. Thus, it is highly necessary to summarize some benchmark problems for structural optimization. This chapter provides an overview of structural optimization problems of both truss and non-truss cases. © 2011 Springer-Verlag Berlin Heidelberg.
Gandomi, AH, Alavi, AH & Yun, GJ 2011, 'Formulation of uplift capacity of suction caissons using multi expression programming', KSCE JOURNAL OF CIVIL ENGINEERING, vol. 15, no. 2, pp. 363-373.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Yun, GJ 2011, 'Nonlinear modeling of shear strength of SFRC beams using linear genetic programming', STRUCTURAL ENGINEERING AND MECHANICS, vol. 38, no. 1, pp. 1-25.
Gandomi, AH, Alavi, AH, Mirzahosseini, MR & Nejad, FM 2011, 'Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 23, no. 3, pp. 248-263.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Mousavi, M & Tabatabaei, SM 2011, 'A hybrid computational approach to derive new ground-motion prediction equations', ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol. 24, no. 4, pp. 717-732.View/Download from: Publisher's site
Gandomi, AH, Sahab, MG & Rahai, A 2011, 'A dynamic nondestructive damage detection methodology for orthotropic plate structures', STRUCTURAL ENGINEERING AND MECHANICS, vol. 39, no. 2, pp. 223-239.
Gandomi, AH, Tabatabaei, SM, Moradian, MH, Radfar, A & Alavi, AH 2011, 'A new prediction model for the load capacity of castellated steel beams', JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, vol. 67, no. 7, pp. 1096-1105.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S & Alavi, AH 2011, 'Mixed variable structural optimization using Firefly Algorithm', COMPUTERS & STRUCTURES, vol. 89, no. 23-24, pp. 2325-2336.View/Download from: Publisher's site
Hosseini, SSS, Jafarnejad, A, Behrooz, AH & Gandomi, AH 2011, 'Combined heat and power economic dispatch by mesh adaptive direct search algorithm', EXPERT SYSTEMS WITH APPLICATIONS, vol. 38, no. 6, pp. 6556-6564.View/Download from: Publisher's site
Mirzahosseini, MR, Aghaeifar, A, Alavi, AH, Gandomi, AH & Seyednour, R 2011, 'Permanent deformation analysis of asphalt mixtures using soft computing techniques', EXPERT SYSTEMS WITH APPLICATIONS, vol. 38, no. 5, pp. 6081-6100.View/Download from: Publisher's site
Mollahasani, A, Alavi, AH & Gandomi, AH 2011, 'Empirical modeling of plate load test moduli of soil via gene expression programming', COMPUTERS AND GEOTECHNICS, vol. 38, no. 2, pp. 281-286.View/Download from: Publisher's site
Mollahasani, A, Alavi, AH, Gandomi, AH & Rashed, A 2011, 'Nonlinear neural-based modeling of soil cohesion intercept', KSCE JOURNAL OF CIVIL ENGINEERING, vol. 15, no. 5, pp. 831-840.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Gandomi, AH & Mollahasani, A 2011, 'Nonlinear genetic-based simulation of soil shear strength parameters', JOURNAL OF EARTH SYSTEM SCIENCE, vol. 120, no. 6, pp. 1001-1022.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Mollahasani, A & Gandomi, AH 2011, 'A hybrid computational approach to formulate soil deformation moduli obtained from PLT', ENGINEERING GEOLOGY, vol. 123, no. 4, pp. 324-332.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Gandomi, M 2010, 'Comment on 'Sivapragasam C, Maheswaran R, Venkatesh V. 2008. Genetic programming approach for flood routing in natural channels. Hydrological Processes 22: 623-628'', HYDROLOGICAL PROCESSES, vol. 24, no. 6, pp. 798-799.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Heshmati, AAR 2010, 'Discussion on "Soft computing approach for real-time estimation of missing wave heights" by SN Londhe [Ocean Engineering 35 (2008) 1080-1089]', OCEAN ENGINEERING, vol. 37, no. 13, pp. 1239-1240.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Mollahassani, A, Heshmati, AA & Rashed, A 2010, 'Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks', JOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, vol. 173, no. 3, pp. 368-379.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Mousavi, M & Mollahasani, A 2010, 'High-precision modeling of uplift capacity of suction caissons using a hybrid computational method', GEOMECHANICS AND ENGINEERING, vol. 2, no. 4, pp. 253-280.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Sahab, MG & Gandomi, M 2010, 'Multi expression programming: a new approach to formulation of soil classification', ENGINEERING WITH COMPUTERS, vol. 26, no. 2, pp. 111-118.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Sahab, MG 2010, 'New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming', MATERIALS AND STRUCTURES, vol. 43, no. 7, pp. 963-983.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Taghipour, A 2010, 'Discussion on "Alternative data-driven methods to estimate wind from waves by inverse modeling" by Mansi Daga, M. C. Deo [Natural Hazards (2008) NHAZ 524, Article 9299, DOI 10.1007/s11069-008-9299-2]', NATURAL HAZARDS, vol. 52, no. 3, pp. 671-673.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Arjmandi, P, Aghaeifar, A & Seyednour, R 2010, 'GENETIC PROGRAMMING AND ORTHOGONAL LEAST SQUARES: A HYBRID APPROACH TO MODELING THE COMPRESSIVE STRENGTH OF CFRP-CONFINED CONCRETE CYLINDERS', JOURNAL OF MECHANICS OF MATERIALS AND STRUCTURES, vol. 5, no. 5, pp. 735-753.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Sahab, MG & Arjmandi, P 2010, 'Formulation of elastic modulus of concrete using linear genetic programming', JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, vol. 24, no. 6, pp. 1273-1278.View/Download from: Publisher's site
Hosseini, SSS & Gandomi, AH 2010, 'Discussion of "Economic Load Dispatch-A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO"', IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 25, no. 1, pp. 590-590.View/Download from: Publisher's site
Hosseini, SSS & Gandomi, AH 2010, 'Discussion on "Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm, by P. Subbaraj et al., Applied Energy 86 (2009) 915-921."', APPLIED ENERGY, vol. 87, no. 4, pp. 1459-1459.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Gandomi, AH, Esmaeili, MA & Gandomi, M 2010, 'A data mining approach to compressive strength of CFRP-confined concrete cylinders', STRUCTURAL ENGINEERING AND MECHANICS, vol. 36, no. 6, pp. 759-783.View/Download from: Publisher's site
Mousavi, SM, Gandomi, AH, Alavi, AH & Vesalimahmood, M 2010, 'Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares', STRUCTURAL ENGINEERING AND MECHANICS, vol. 36, no. 2, pp. 225-241.View/Download from: Publisher's site
Sadat Hosseini, SS & Gandomi, AH 2010, 'WITHDRAWN: Discussion of 'Combined heat and power economic dispatch by harmony search algorithm' by A. Vasebi et al., International Journal of Electrical Power and Energy Systems 29 (2007) 713–719', International Journal of Electrical Power & Energy Systems.View/Download from: Publisher's site
Singh, AK, Deo, MC & Kumar, VS 2010, 'Discussion: Neural network - genetic programming for sediment transport', PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-MARITIME ENGINEERING, vol. 163, no. 3, pp. 135-136.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Gandomi, M & Sadat Hosseini, SS 2009, 'Prediction of maximum dry density and optimum moisture content of stabilised soil using RBF neural networks', IES Journal Part A: Civil and Structural Engineering, vol. 2, no. 2, pp. 98-106.View/Download from: Publisher's site
This article proposes a novel approach for the prediction of maximum dry density (MDD) and optimum moisture content (OMC) of soil-stabiliser mix by using radial basis function (RBF) neural networks. RBF neural network is utilised to construct comprehensive and accurate models to be able to relate the MDD and OMC of stabilised soil to the properties of natural soil such as particle size distribution, plasticity, linear shrinkage and the type and quantity of stabilising additives. Two separate sets of RBF prediction models, one for the MDD and the other for the OMC, have been developed. A parametric study was also conducted in this study using the results obtained from the proposed models to evaluate the sensitivity of MDD and OMC due to variation of the influencing parameters. A comprehensive set of data including a wide range of soil types obtained from previously published stabilisation test results was used for training, validation and testing the prediction models. The accuracy of the proposed models is satisfactory when compared with that of the experimental results. The results of proposed RBF models were further compared with those of the existing models found in literature and found to be more accurate. © 2009 Taylor & Francis Group, LLC.
Gandomi, AH & Alavi, AH 2009, 'Discussion on "Predicting the shear strength of reinforced concrete beams using artificial neural networks" by MY Mansour, M. Dicleli, JY Lee, J. Zhang [Eng Struct 26 (2004) 781-799]', ENGINEERING STRUCTURES, vol. 31, no. 11, pp. 2801-2801.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Kazemi, S & Alinia, MM 2009, 'Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming', JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, vol. 65, no. 8-9, pp. 1738-1750.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Hosseini, SSS 2008, 'A Discussion on "Genetic programming for retrieving missing information in wave records along the west coast of India" [Applied Ocean Research 2007; 29 (3): 99-111]', APPLIED OCEAN RESEARCH, vol. 30, no. 4, pp. 338-339.View/Download from: Publisher's site
Dutta, S & Gandomi, AH 2020, 'Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels' in Handbook of Probabilistic Models, Elsevier, pp. 369-381.View/Download from: Publisher's site
Jayabarathi, T, Raghunathan, T & Gandomi, AH 2018, 'The bat algorithm, variants and some practical engineering applications: A review' in Studies in Computational Intelligence, pp. 313-330.View/Download from: Publisher's site
© Springer International Publishing AG 2018. The bat algorithm (BA), a metaheuristic algorithm developed by Xin-She Yang in 2010, has since been modified, and applied to numerous practical optimization problems in engineering. This chapter is a survey of the BA, its variants, some sample real-world optimization applications, and directions for future research.
Tahmassebi, A & Gandomi, AH 2018, 'Genetic Programming Based on Error Decomposition: A Big Data Approach' in Genetic Programming Theory and Practice XV, Springer International Publishing, pp. 135-147.View/Download from: Publisher's site
© 2017 Elsevier Inc. All rights reserved. The literature shows that the Gravitational Search Algorithm (GSA) is really competitive compared to Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on benchmark functions. However, local optima entrapment and slow convergence are hindrances when solving real engineering problems. Such issues originate from slow movement of masses due to nearly equal weight proportional to the number of iterations. In this study, 10 chaotic maps tune the gravitational constant (. G) to overcome these problems. The gravitational constant balances exploration and exploitation, so chaotic maps are allowed to perform this duty in this study. Ten unconstrained benchmark functions examine the proposed Chaotic GSA (CGSA) algorithms. This work also considers finding the optimal design for welded beam and pressure vessel designs to prove the applicability of the proposed method. The results prove that chaotic maps improve the performance of GSA.
Sahab, MG, Toropov, VV & Gandomi, AH 2017, 'Optimum Design of Composite Concrete Floors Using a Hybrid Genetic Algorithm' in Handbook of Neural Computation, pp. 581-589.View/Download from: Publisher's site
© 2017 Elsevier Inc. All rights reserved. Composite steel-concrete floors are used commonly in bridge decks and as story floors in different kind of buildings. This paper presents optimal design of composite floors consisting of steel joists and a concrete slab on the top of them. The slab thickness, typical joist spacing, and the standard steel section size of joists are considered as design variables. The objective function is the cost of unit area of composite floor including cost of materials and labors for reinforcement, concrete, formwork and steel joists. AISC-ASD89 and EURO-4 (EN 1994) code provisions are considered to define design constraints. A hybrid algorithm based on a modified genetic algorithm is implemented to solve the optimization problem. The effect of many design parameters such as unit cost of materials and characteristic strength of concrete on the optimum solution is investigated using a practical design example.
Alavi, AH, Gandomi, AH, Mollahasani, A & Bazaz, JB 2013, 'Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems' in Metaheuristics in Water, Geotechnical and Transport Engineering, pp. 289-310.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2013, 'Expression Programming Techniques for Formulation of Structural Engineering Systems' in Metaheuristic Applications in Structures and Infrastructures, pp. 439-455.View/Download from: Publisher's site
A new metaheuristic optimization algorithm, called krill herd (KH), has been recently proposed by Gandomi and Alavi. In this study, KH is introduced for structural optimization. For more verification, KH is subsequently applied to three design problems reported in the literature. The performance of the KH algorithm is further compared with various algorithms representative of the state of the art in the area. The comparisons show that the results obtained by KH can be better than the best solutions obtained by the existing methods in these three case studies. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.
Gandomi, AH, Yang, XS, Talatahari, S & Alavi, AH 2013, 'Metaheuristic Algorithms in Modeling and Optimization' in Metaheuristic Applications in Structures and Infrastructures, pp. 1-24.View/Download from: Publisher's site
Sahab, MG, Toropov, VV & Gandomi, AH 2013, 'A Review on Traditional and Modern Structural Optimization: Problems and Techniques' in Metaheuristic Applications in Structures and Infrastructures, pp. 25-47.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Mollahasani, A 2012, 'A genetic programming-based approach for the performance characteristics assessment of stabilized soil' in Variants of Evolutionary Algorithms for Real-World Applications, pp. 343-376.View/Download from: Publisher's site
© 2012 Springer-Verlag Berlin Heidelberg. All rights are reserved. This chapter presents a variant of genetic programming, namely linear genetic programming (LGP), and a hybrid search algorithm coupling LGP and simulated annealing (SA), called LGP/SA, to predict the performance characteristics of stabilized soil. LGP and LGP/SA relate the unconfined compressive strength (UCS), maximum dry density (MDD), and optimum moisture content (OMC) metrics of stabilized soil to the properties of the natural soil as well as the types and quantities of stabilizing additives. Different sets of LGP and LGP/SA-based prediction models have been separately developed. The contributions of the parameters affecting UCS, MDD, and OMC are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the trends of the results are compared with previous studies. A comprehensive set of data obtained from the literature has been used for developing the models. Experimental results confirm that the accuracy of the proposed models is satisfactory. In particular, the LGP-based models are found to be more accurate than the LGP/SA-based models.
Mohebali, B, Tahmassebi, A, Gandomi, AH & Meyer-Baese, A 2019, 'A big data inspired preprocessing scheme for bandwidth use optimization in smart cities applications using Raspberry Pi', Proceedings of SPIE - The International Society for Optical Engineering.View/Download from: Publisher's site
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. The advancement of Internet of Things (IoT) technologies, such as low-cost embedded single board computers which integrate sensors, communication hardware, and processing power in one unit, has given more traction to the concept of Smart Cities. Having cheaper processing power at their disposal, the sensing units are capable of gathering increasingly larger amounts of raw data locally, which must be processed before being usable. One concern for this scheme is the amount of infrastructure and network bandwidth needed to transfer the data from the acquisition location to a server, which may be miles away, for further processing. The bandwidth available to the sensor network, distributed through the city, is expanding in a lower rate than the size and bandwidth demand of the network it serves. Therefore, transferring the unprocessed data to a central server does not seem feasible unless major compromises are made in terms of data resolution and size. This paper proposes a local big data based preprocessing scheme before the data is transferred to the storage. Using this scheme can free up the network bandwidth, exploit the otherwise wasted local processing power, and release processing load from the central server, allowing it to serve a larger network without the need for more powerful hardware. By making efficient use of network infrastructure the smart city applications are more affordable and scalable.
Tahmassebi, A, Ehtemami, A, Mohebali, B, Gandomi, AH, Pinker, K & Meyer-Baese, A 2019, 'Big data analytics in medical imaging using deep learning', Proceedings of SPIE - The International Society for Optical Engineering.View/Download from: Publisher's site
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Big data has been one of the hottest topics of scientific discussions in the recent years. In early 2000s, an industry analyst attempted to describe big data as the three Vs: Volume, Velocity, and Variability. With the new technologies such as Hadoop, it is now feasible to store and use extremely large volumes of data that comes in at an unprecedented velocity. The variability of this data can be large as it can come in different formats such as text documents, voice or video, and financial transactions. Big data analytics has been proven to be useful is various fields such as science, sports, advertising, health care, genomic sequence data, and medical imaging. This study presents a brief overview of big data analytics in medical imaging approaches with considering the importance of contemporary machine learning techniques such as deep learning.
Mohebali, B, Tahmassebi, A, Gandomi, AH, Meyer-Baese, A & Foo, SY 2018, 'A scalable communication abstraction framework for internet of things applications using Raspberry Pi', Proceedings of SPIE - The International Society for Optical Engineering.View/Download from: Publisher's site
Tahmassebi, A, Gandomi, AH & Meyer-Baese, A 2018, 'A Pareto Front Based Evolutionary Model for Airfoil Self-Noise Prediction', 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings.View/Download from: Publisher's site
© 2018 IEEE. According to NASA's report on the technologies that could reduce external aircraft noise by 10 dB, a challenge equally as important as finding approaches on airframe noise reduction is the demand to bring up strategies by which airframe noise can be predicted both accurately and rapidly. One of the components of the overall airframe noise is the self-noise of the airfoil itself. In this paper, an evolutionary symbolic implementation for airfoil self-noise prediction was proposed. Multi-objective genetic programming as a subset of evolutionary computation along with adaptive regression by mixing algorithm was used to create an executable fused model. The developed model was tested on the airfoil self-noise database and the performance of the developed model was compared to the previous works and benchmark machine learning algorithms. The reasonable results suggest that the proposed model can be applied to noise generation by low-Mach-number turbulent flows in aerospace, automobile, underwater, and wind turbine acoustic communities.
Tahmassebi, A, Gandomi, AH & Meyer-Baese, A 2018, 'An Evolutionary Online Framework for MOOC Performance Using EEG Data', 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings.View/Download from: Publisher's site
© 2018 IEEE. Massive Online Open Course (MOOC) is a scalable, free or affordable online course which emerged as one of the fastest growing distance education platforms in the past decade. One of the biggest challenges that threatens distance education is abnormality in the overall level of consciousness of students while they are taking the course. In this paper, an evolutionary online framework was proposed to improve the performance of MOOCs via noninvasive electro-physiological monitoring methods such as electroencephalography (EEG). Based on the proposed platform, EEG signals can be recorded from users while they are wearing any EEG headsets. EEG measures a brain's spontaneous voltage fluctuations resulting from ionic current within the neurons of the brain via multiple electrodes placed on the scalp. A total of eleven extracted features from EEG signals were employed as the inputs of the evolutionary classification algorithm to predict two classes of confused and not-confused for each individual. An accuracy of 89 % was considered significant enough to suggest that there is difference in the EEG signals of individuals with confusion versus not-confused individuals.
Tahmassebi, A, Schulte, MHJ, Gandomi, AH, Goudriaan, AE, McCann, I & Meyer-Baese, A 2018, 'Deep learning in medical imaging: FMRI big data analysis via convolutional neural networks', ACM International Conference Proceeding Series.View/Download from: Publisher's site
© 2018 Copyright held by the owner/author(s). This paper aims at implementing novel biomarkers extracted from functional magnetic resonance imaging (fMRI) images taken at resting-state using convolutional neural networks (CNN) to predict relapse in heavy smoker subjects. In this regard, two classes of subjects were studied. The first class contains 19 subjects that took the drug N-acetylcysteine (NAC), and the second class contains 20 subjects that took a placebo. The subjects underwent a double-blind smoking cessation treatment. The resting-state fMRI of the subjects' brains were recorded through 200 snapshots before and after the treatment. The relapse data was assessed after 6 months past the treatment. The data was pre-processed and an undercomplete autoencoder along with various similarity metrics was developed to extract salient features that could differentiate the pre and post treatment images. Finally, the extracted feature matrix was fed into robust classification algorithms to classify the subjects in terms of relapse and non-relapse. The XGBoost algorithm with 0.86 precision and an AUC of 0.92 outperformed the other classification methods in prediction of relapse in subjects.
Tahmassebi, A, Gandomi, AH & Meyer-Bäse, A 2017, 'High performance GP-Based approach for fMRI big data classification', ACM International Conference Proceeding Series.View/Download from: Publisher's site
© 2017 Copyright is held by the owner/author(s). We consider resting-state Functional Magnetic Resonance Imaging (fMRI) of two classes of patients: one that took the drug Nacetylcysteine (NAC) and the other one a placebo before and after a smoking cessation treatment. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-Treatment based on their fMRI scans. 80% accuracy was obtained using Independent Component Analysis (ICA) along with Genetic Programming (GP) classifier using High Performance Computing (HPC) which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.
Tahmassebi, A, Gandomi, AH, McCann, I, Schulte, MH, Schmaal, L, Goudriaan, AE & Meyer-Baese, A 2017, 'An evolutionary approach for fMRI big data classification', 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, pp. 1029-1036.View/Download from: Publisher's site
© 2017 IEEE. Resting-state function magnetic resonance imaging (fMRI) images allow us to see the level of activity in a patient's brain. We consider fMRI of patients before and after they underwent a smoking cessation treatment. Two classes of patients have been studied here, that one took the drug N-acetylcysteine and the ones took a placebo. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-treatment based on their fMRI scans. The image slices of brain are used as the variable and as results here we deal with a big data problem with about 240,000 inputs. To handle this problem, the data had to be reduced and the first process in doing that was to create a mask to apply to all images. The mask was created by averaging the before images for all patients and selecting the top 40% of voxels from that average. This mask was then applied to all fMRI images for all patients. The average of the difference in the before treatment and after fMRI images for each patient were found and these were flattened to one dimension. Then a matrix was made by stacking these 1D arrays on top of each other and a data reduction algorithm was applied on it. Lastly, this matrix was fed into some machine learning and Genetic Programming algorithms and leave-one-out cross-validation was used to test the accuracy. Out of all the data reduction machine learning algorithms used, the best accuracy was obtained using Principal Component Analysis along with Genetic Programming classifier. This gave an accuracy of 74%, which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.
Trivedi, IN, Gandomi, AH, Jangir, P & Jangir, N 2017, 'Study of different boundary constraint handling schemes in interior search algorithm', Advances in Intelligent Systems and Computing, pp. 547-564.View/Download from: Publisher's site
© Springer Nature Singapore Pte Ltd. 2017. Real-world problems usually have bounded search space and, therefore, the performance of optimization algorithms on them is also related to choose a proper boundary constraint handling methods. There are several classical approaches in the literature to handle the bounds. Usually, optimization algorithm use does not pay attentions to choose a proper boundary constraint handling scheme. In this study different boundary constraint handling schemes such as evolutionary scheme and classical schemes (including reflecting scheme, absorbing scheme, toroidal scheme, and random scheme) are evaluated on a recent evolutionary algorithm called interior search algorithm (ISA). In this paper, all the boundary constraint handling approaches have been adopted to ISA to solve a wide set of global numerical benchmark problems. The conclusions are made based on statistical results which clearly show the importance of different boundary constraint han-dlings in the searching process. The results obtained using the evolutionary boundary constraint handling scheme are better than the ones obtained by the other well-known approaches and it seems this scheme is suitable for a wider range of evolutionary optimization problems with very good convergence rate.
Trivedi, IN, Gandomi, AH, Jangir, P, Kumar, A, Jangir, N & Totlani, R 2017, 'Adaptive krill herd algorithm for global numerical optimization', Advances in Intelligent Systems and Computing, pp. 517-525.View/Download from: Publisher's site
© Springer Nature Singapore Pte Ltd. 2017. A recent bio-inspired optimization algorithm, that is, based on the Lagrangian and evolutionary behavior of krill individuals in nature is called the Krill Herd (KH) Algorithm. Randomization has a key role in both exploration and exploitation of a problem using KH algorithm. A new randomization technique termed adaptive technique is integrated with Krill Herd algorithm and tested on several global numerical functions. The KH uses Lagrangian movement which includes induced movement, random diffusion, and foraging motion, and therefore, it covers a vast area in the exploration phase. And then adding the powerful adaptive randomization technique potent the adaptive KH (AKH) algorithm to attain global optimal solution with faster convergence as well as less parameter dependency. The proposed AKH outperforms the standard KH in terms of both statistical results and best solution.
Gandomi, AH & Kashani, AR 2016, 'Evolutionary Bound Constraint Handling for Particle Swarm Optimization', 2016 4TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI), 4th International Symposium on Computational and Business Intelligence (ISCBI), IEEE, Olten, SWITZERLAND, pp. 148-152.
Kanagaraj, G, Ponnambalam, SG & Gandomi, AH 2015, 'Hybridizing Cuckoo Search with Bio-inspired Algorithms for Constrained Optimization Problems', SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING (SEMCCO 2015), 6th International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO), SPRINGER INTERNATIONAL PUBLISHING AG, CMR Tech Campus, Hyderabad, INDIA, pp. 260-273.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR & Mousavi, M 2015, 'Boundary constraint handling affection on slope stability analysis', Computational Methods in Applied Sciences, pp. 341-358.View/Download from: Publisher's site
© 2015 Springer International Publishing Switzerland. In an engineering optimization problem such as soil slope problem, each design variable has permissible solution domain. Therefore, efficiency of an optimization algorithm may be affected by the method used for keeping the solutions within the defined boundaries or boundary constraint handling method. Despite importance of selecting constraint handling approach, there aren't adequate studies in this field. Heterogeneous slope stability optimization in the presence of a band of weak soil layer is considered as a complex geotechnical problem that requires satisfying boundary constraints. Evolutionary boundary constraint handling is one of the recently proposed methods that is very easy to implement and very effective. The present study intended to improve the optimization results by means of evolutionary boundary constraint handling scheme on slope stability optimization problem. In the current chapter five benchmark problems are analyzed using absorbing and evolutionary boundary constraint handling schemes and their results are compared to check the validity of this method. Based on achieved results optimization algorithm performance is improved by using the proposed boundary constraint handling method.
Hosseini, SSS, Gandomi, AH, Nemati, A & Hosseini, SHS 2015, 'Reactive power and voltage control based on mesh adaptive direct search algorithm', Computational Methods in Applied Sciences, pp. 217-231.View/Download from: Publisher's site
© 2015 Springer International Publishing Switzerland. This is a pioneer study that presents a new optimization algorithm called mesh adaptive direct search (MADS) to solve optimal steady-state performance of power systems. MADS is utilized to specify the optimal settings of control variables, i.e. transformer taps and generator voltages for optimal reactive power and voltage control of IEEE 30-bus system. Covariance matrix adaptation evolution strategy (CMAES) algorithm is utilized as a strong search strategy in the MADS technique to enhance its effectiveness. The results acquired by the hybrid search algorithm coupling MADS and CMAES, called MADS-CMAES, and the MADS algorithm itself without any search method are compared with multi-objective evolutionary and particle swarm optimization algorithms, demonstrating the superiority of MADS. The proposed MADS-based techniques are very robust against their parameters and changing the search space because of their inherent adaptive tuning.
Shahrara, N, Çelik, T & Gandomi, AH 2015, 'Analysing build-operate-transfer models in utility projects', Civil-Comp Proceedings.
© Civil-Comp Press, 2015. In this paper the financial viability of undertaking build-operate-transfer (BOT) contracts for sewer and water projects in California, United States of America is analysed. Furthermore with the aid of sensitivity analysis, risk parameters are identified. Sensitivity analysis results demonstrate that project construction cost determines the financial viability of undertaking a BOT contract; therefore, reliable construction cost prediction, based on limited information, at the early stages of the project planning phase is crucial for development of an objective BOT agreement. This study utilizes gene expression programming (GEP), an extension of the genetic algorithm and genetic programming, to develop a prediction model for estimating the construction cost of water and sewer rehabilitation/replacement projects. A database used for developing the model was established on the basis of data related to 210 water and sewer projects obtained from the City of San Diego, California, United States of America.
Wang, G-G, Deb, S, Gandomi, AH & Alavi, AH 2015, 'A Hybrid PBIL-Based Krill Herd Algorithm', 2015 3RD INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI 2015), 3rd International Symposium on Computational and Business Intelligence (ISCBI), IEEE, Bali, INDONESIA, pp. 39-44.View/Download from: Publisher's site
Gandomi, AH & Roke, DA 2014, 'Engineering Optimization using Interior Search Algorithm', 2014 IEEE SYMPOSIUM ON SWARM INTELLIGENCE (SIS), IEEE Symposium on Swarm Intelligence (SIS), IEEE, Orlando, FL, pp. 20-26.
Gandomi, AH & Roke, DA 2014, 'Seismic response prediction of self-centering, concentrically-braced frames using genetic programming', Structures Congress 2014 - Proceedings of the 2014 Structures Congress, pp. 1221-1232.View/Download from: Publisher's site
© 2014 American Society of Civil Engineers. Conventional concentrically braced frame (CBF) systems are commonly used in earthquake-resistant structural systems. However, they have limited drift capacity before brace buckling occurs. Self-centering, concentrically-braced frame (SC-CBF) systems have recently been developed to increase drift capacity prior to initiation of damage and to minimize residual drift. SC-CBFs have more complex behavior than conventional CBFs. The seismic response of SC-CBFs depends on many new parameters such as rocking behavior, post-tensioning bars, and energy dissipation elements. Additionally, uncertainty of mechanical properties (e.g., coefficient of friction in the friction-bearings) can affect the system response. To design SC-CBF systems, an accurate prediction of the statistical parameters of roof drift demand is essential. In this study, genetic programming is used to predict the mean and standard deviation of SC-CBF peak roof drift response under the design basis earthquake using the most effective mechanical and geometric parameters. The results of this study can then be used in the future to design more efficient SC-CBF systems with a more accurate roof drift prediction.
Wang, G-G, Deb, S, Gandomi, AH, Zhang, Z & Alavi, AH 2014, 'A Novel Cuckoo Search with Chaos Theory and Elitism Scheme', 2014 INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE ISCMI 2014, 2014 International Conference on Soft Computing & Machine Intelligence (ISCMI 2014), IEEE, New Delhi, INDIA, pp. 64-69.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Ting, TO & Yang, XS 2013, 'Intelligent modeling and prediction of elastic modulus of concrete strength via gene expression programming', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 564-571.View/Download from: Publisher's site
The accurate prediction of the elastic modulus of concrete can be very important in civil engineering applications. We use gene expression programming (GEP) to model and predict the elastic modulus of normal-strength concrete (NSC) and high-strength concrete (HSC). The proposed models can relate the modulus of elasticity of NSC and HSC to their compressive strength, based on reliable experimental databases obtained from the published literature. Our results show that GEP can be an effective method for deriving simplified and precise formulations for the elastic modulus of NSC and HSC. Furthermore, the comparison study in the present work indicates that the GEP predictions are more accurate than other methods. © 2013 Springer-Verlag Berlin Heidelberg.
Heshmati, RAA, Alavi, AH, Keramati, M & Gandomi, AH 2009, 'A radial basis function neural network approach for compressive strength prediction of stabilized soil', Geotechnical Special Publication, pp. 147-153.View/Download from: Publisher's site
This study considers the use of artificial neural networks (ANNs) to predict the unconfined compressive strength (UCS) of soil-stabilizer mix. Radial basis function (RBF) as one of the most widely used ANN architectures is utilized to construct comprehensive models to relate the UCS of stabilized soil to the properties of natural soil and type and quantity of stabilizing additives. A comprehensive set of data obtained from previously published stabilization test results was used for model development. A subsequent parametric study was carried out and the trends of the results have been confirmed via previous laboratory studies. The RBF based estimates are compared with the experimental and numerical results of other researchers and found to be more accurate. © 2009 ASCE.
Alavi, AH, Heshmati, AA, Gandomi, AH, Askarinejad, A & Mirjalili, M 2008, 'Utilisation of computational intelligence techniques for stabilised soil', Proceedings of the 6th International Conference on Engineering Computational Technology.
One of the main objectives of chemical stabilisation is to increase the compressive strength of soils. A wide range of parameters affect the strength improvement in cementitious stabilisation with chemicals. Accordingly, it is difficult to determine some kinds of functional relationships in strength improvement which make the precision of strength prediction to be satisfying. The purpose of the present study is to use two computational intelligence techniques namely, multilayer perceptron (MLP) and linear genetic programming (LGP), in order to develop the mathematical models to be capable of predicting the unconfined compressive strength. Subsequently, a comparison between these methods was performed in terms of prediction performance. Properties of natural soil such as textural properties, plasticity and linear shrinkage, stabiliser quantities and types (cement, lime, asphalt), for a wide range of soil types were used in order to generate the mathematical models to be able to predict the compressive strength as a quality of stabilised soil. A comprehensive set of data including 219 previously published stabilised unconfined compressive strength experimental determinations were utilised to develop the models. © 2008 Civil-Comp Press.
Alavi, AH, Heshmati, AA, Salehzadeh, H, Gandomi, AH & Askarinejad, A 2008, 'Soft computing based approaches for high performance concrete', Proceedings of the 6th International Conference on Engineering Computational Technology.
Different parameters influence the compressive strength and workability properties of high performance concrete (HPC) mixes. Accordingly, an extensive understanding of relation between these parameters and properties of the resulting matrix is required for developing a standard mix design procedure for HPC mix. The complex behaviour of strength and workability improvement and a need to avoid trying several mix proportions to generate a successful mix suggest the necessity to develop comprehensive mathematical models to be able to evaluate the performance characteristics of HPC mixes with high accuracy. Therefore, in this paper, linear genetic programming (LGP) is utilised for the first time in the literature, to develop mathematical models to be able to predict the strength and slump flow of HPC mixes from the influencing parameters. Subsequently, the LGP based prediction results are compared with the results of proposed multilayer perceptron (MLP) in terms of prediction performance. Sand cement ratio, coarse aggregate cement ratio, water cement ratio, percentage of silica fume and percentage of superplasticiser are used as the input variables to the models to predict the strength and slump flow of HPC mixes. A reliable database was obtained from the previously published literature in order to develop the models. © 2008 Civil-Comp Press.
Gandomi, AH, Alavi, AH, Sahab, MG, Gandomi, M & Gorji, MS 2008, 'Empirical models for prediction of flexural resistance and initial stiffness of welded beam-column joints', EASEC-11 - Eleventh East Asia-Pacific Conference on Structural Engineering and Construction.
Gandomi, AH, Alavi, AH, Sahab, MG, Gandomi, M & Safari Gorji, M 2008, 'Empirical models for the prediction of flexural resistance and initial stiffness of welded beam-column joints', EASEC-11 - Eleventh East Asia-Pacific Conference on Structural Engineering and Construction.
Welded beam-column joints play a fundamental role in the global response of steel structures. The flexural resistance and initial stiffness properties of the joints are affected by different parameters. It is idealistic to develop models, relating these properties of the joints to the influencing parameters. This paper proposes a novel approach for the prediction of flexural resistance and initial stiffness of welded joints by using a hybrid search algorithm that couples genetic programming (GP) and simulated annealing (SA), called GP/SA. Column height, column flange width, column flange thickness, column flange yield stress, column web thickness, column web yield stress, beam height, beam web thickness, beam web yield stress, beam flange thickness, beam flange width, beam flange yield stress, and weld thickness are used as input variables to the models. A reliable database from the previously published literature was employed to develop the empirical models. The accuracy of the proposed models is satisfactory as compared to experimental results. GP/SA models are further compared with the corresponding design code (Eurocode 3) reference values. The results demonstrate that the GP/SA based models have better performance than Eurocode 3 models.
Gorji, MS, Taribakhsh, M, Gandomi, A & Makhsous, SJ 2008, 'Seismic assessment and retrofit of khorjinee frames in typical Iranian steel buildings', EASEC-11 - Eleventh East Asia-Pacific Conference on Structural Engineering and Construction.
Previous post-earthquake inspections and evaluation of damage in buildings in Iran have demonstrated that many steel frame buildings were suffered from high rate of damage due to lack of shear and moment capacities in the connections, especially those with common Khorjinee connections. Khorjinee connection is one of the beam-to-column connections in steel frame structures. It has become especially popular in Iran for conventional buildings, due to its speed and convenient in construction process and economic privileges in comparison to the other types of steel connections. The frames with Khorjinee connections are vulnerable to earthquakes and there are serious concerns about their performance in future earthquakes.