- Structural Control & Health Monitoring
- System Identification
- Artificial Intelligence
- Structural Dynamics
- Structural Analysis
- Mechanics of Solids
- Engineering Mechanics
- Engineering Computation
Askari, M, Yu, Y, Zhang, C, Samali, B & Gu, X 2019, 'Real-Time Tracking of Structural Stiffness Reduction with Unknown Inputs, Using Self-Adaptive Recursive Least-Square and Curvature-Change Techniques', International Journal of Structural Stability and Dynamics, vol. 19, no. 10.View/Download from: Publisher's site
© 2019 World Scientific Publishing Company. In this paper, a new computationally efficient algorithm is developed for online and real-time identification of time, location, and severity of abrupt changes in structural stiffness as well as the unknown inputs such as earthquake signal. The proposed algorithm consists of three stages and is based on self-adaptive recursive least-square (RLS) and curvature-change approaches. In stage 1 (intact structure), a simple compact RLS is hired to estimate the unknown parameters and input of the structure such as stiffness and earthquake. Once the damage has occurred, its time and location are identified in stage 2, using two robust damage indices which are based on the structural jerk response and the error between measured and estimated responses of structure from RLS. Finally, the damage severity as well as the unknown excitations are identified in the third stage (damaged structure), using a self-adaptive multiple-forgetting-factor RLS. The method is validated through numerical and experimental case studies including linear and nonlinear buildings, a truss structure, and a three-story steel frame with different excitations and damage scenarios. Results show that the proposed algorithm can effectively identify the time-varying structural stiffness as well as unknown excitations with high computational efficiency, even when the measured data is contaminated with different levels of noise. In addition, as no optimization method is used here, it can be applied to real-time applications with computational efficiency.
Gu, X, Yu, Y, Li, Y, Li, J, Askari, M & Samali, B 2019, 'Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control', Mechanical Systems and Signal Processing, vol. 119, pp. 380-398.View/Download from: Publisher's site
In this paper, a “smart” base isolation strategy is proposed in this study utilising a semi-active magnetorheological elastomer (MRE) isolator whose stiffness can be controlled in real-time and reversible fashion. By modulating the applied current, the horizontal stiffness of the MRE isolator can be controlled and thus the control action can be generated for the isolated structure. To overcome the inherent nonlinearity and hysteresis of the MRE isolator, radial basis function neural network based fuzzy logic control (RBF-NFLC) was developed due to its inherent robustness and capability in coping with uncertainties. The NFLC was optimised by a non-dominated sorting genetic algorithm type II (NSGA-II) for better suited fuzzy control rules as well as most appropriate parameters for the membership functions. To evaluate the effectiveness of the proposed smart base isolation system, four scenarios are tested under various historical earthquake excitations, i.e. bare building with no isolation, passive isolated structure, MRE isolated structure with Bang-Bang control, MRE isolated structure with proposed NFLC. A three-storey shear building model was adopted as the testing bed. Through the testing results, limited performance of passive isolation system was revealed. In contrast, the adaptability of the proposed isolation strategy was demonstrated and it is proven that the smart MRE base isolation system is able to provide satisfactory protection for both structural and non-structural elements of the system over a wide range of hazard dynamic loadings.
Yu, Y, Samali, B, Zhang, C & Askari, M 2019, 'Hysteresis modeling for cyclic behavior of concrete-steel composite joints using modified CSO', Steel and Composite Structures, vol. 33, no. 2, pp. 277-298.View/Download from: Publisher's site
Copyright © 2019 Techno-Press, Ltd. Concrete filled steel tubular (CFST) column joints with composite beams have been widely used as lateral loading resisting elements in civil infrastructure. To better utilize these innovative joints for the application of structural seismic design and analysis, it is of great importance to investigate the dynamic behavior of the joint under cyclic loading. With this aim in mind, a novel phenomenal model has been put forward in this paper, in which a Bouc-Wen hysteresis component is employed to portray the strength and stiffness deterioration phenomenon caused by increment of loading cycle. Then, a modified chicken swarm optimization algorithm was used to estimate the optimal model parameters via solving a global minimum optimization problem. Finally, the experimental data tested from five specimens subjected to cyclic loadings were used to validate the performance of the proposed model. The results effectively demonstrate that the proposed model is an easy and more realistic tool that can be used for the pre-design of CFST column joints with reduced beam section (RBS) composite beams.
Askari, M, Li, J & Samali, B 2017, 'Cost-effective multi-objective optimal positioning of magnetorheological dampers and active actuators in large nonlinear structures', Journal of Intelligent Material Systems and Structures, vol. 28, no. 2, pp. 230-253.View/Download from: Publisher's site
The optimal number and location of control devices not only play a major role in an effective structural control system but also lead to a cost-effective design. This article presents a multi-objective optimization method based on a new genetic algorithm for simultaneous finding of the optimal number and placement of actuators and magnetorheological dampers, in active and semi-active vibration control of structures. The proposed strategy considers three objective functions to be minimized through optimization, including peak inter-storey drift ratio, peak acceleration and peak base shear force to make sure both human comfort and safety of the structure are guaranteed. Also, by choosing a pre-defined level of performance on dynamic responses of a structure, the designer can decide on decreasing or increasing the number of control devices in a systematic way and minimize the control cost. The approach is then validated through a nonlinear 20-storey benchmark problem. The results from active control system show how a problem that was initially solved with 25 actuators can be solved with less than a quarter of those actuators, having similar results in terms of aforementioned indices. The optimal distribution of different numbers of magnetorheological dampers in the same benchmark building is also studied in this article and compared to those obtained from actuators. Due to highly nonlinear behaviour of these devices, and also the complexity of the under-study benchmark structure, few reported researches have been conducted in this area. Also, the comparison between optimal places of active and semi-active control devices in the same structure has hitherto not been reported in the open literature.
Fu, W, Zhang, C, Sun, L, Askari, M, Samali, B, Chung, KL & Sharafi, P 2017, 'Experimental investigation of a base isolation system incorporating MR dampers with the high-order single step control algorithm', Applied Sciences, vol. 7, no. 4, pp. 1-15.View/Download from: Publisher's site
© 2017 by the authors. The conventional isolation structure with rubber bearings exhibits large deformation characteristics when subjected to infrequent earthquakes, which may lead to failure of the isolation layer. Although passive dampers can be used to reduce the layer displacement, the layer deformation and superstructure acceleration responses will increase in cases of fortification earthquakes or frequently occurring earthquakes. In addition to secondary damages and loss of life, such excessive displacement results in damages to the facilities in the structure. In order to overcome these shortcomings, this paper presents a structural vibration control system where the base isolation system is composed of rubber bearings with magnetorheological (MR) damper and are regulated using the innovative control strategy. The high-order single-step algorithm with continuity and switch control strategies are applied to the control system. Shaking table test results under various earthquake conditions indicate that the proposed isolation method, compared with passive isolation technique, can effectively suppress earthquake responses for acceleration of superstructure and deformation within the isolation layer. As a result, this structural control method exhibits excellent performance, such as fast computation, generic real-time control, acceleration reduction and high seismic energy dissipation etc. The relative merits of the continuity and switch control strategies are also compared and discussed.
Askari, M, Li, J & Samali, B 2016, 'A compact self-adaptive recursive least square approach for real-timestructural identification with unknown inputs', Advances in Structural Engineering, vol. 19, no. 7, pp. 1118-1129.View/Download from: Publisher's site
A new online tracking technique, based on recursive least square with adaptive multiple forgetting factors, is presented in this article which can estimate abrupt changes in structural parameters during excitation and also identify the unknown inputs to the structure, for example, earthquake signal. The method considers an adaptive rule for each of the forgetting factors assigned to each of the unknown parameters and thus enables simultaneous identification of different time-varying parameters of the system. The method is validated through both linear and nonlinear case studies, with different excitations and damage scenarios. The results show that the proposed algorithm can effectively identify the time-varying parameters such as damping, stiffness as well as unknown excitations with high computational efficiency, even when the measured data are contaminated with different levels of noise. However, when damage occurs while the excitation is small, the identification error remains at a small range, and therefore, covariance cannot be amplified to effectively track the changes in unknown parameters.
Askari, M, Li, J & Samali, B 2016, 'Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study', International Journal of Structural Stability and Dynamics, vol. 16, no. 6, pp. 1-18.View/Download from: Publisher's site
System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.
Askari, M, Li, J & Samali, B 2016, 'Semi-active control of smart building-MR damper systems using novel TSK-Inv and max-min algorithms', SMART STRUCTURES AND SYSTEMS, vol. 18, no. 5, pp. 1005-1028.View/Download from: Publisher's site
Askari, M, Li, J, Samali, B & Gu, X 2016, 'Experimental forward and inverse modelling of magnetorheological dampers using an optimal Takagi-Sugeno-Kang fuzzy scheme', Journal of Intelligent Material Systems and Structures, vol. 27, no. 7, pp. 904-914.View/Download from: Publisher's site
© The Author(s) 2015. An evolving encoding scheme is presented in this article for a fuzzy-based nonlinear system identification scheme, using the subtractive fuzzy C-mean clustering and a modified version of non-dominated sorting genetic algorithm. This method is able to automatically select the best inputs as well as the structure of the fuzzy model such as rules and membership functions. Moreover, three objective functions are considered to satisfy both accuracy and compactness of the model. The developed method is then employed to identify both forward and inverse models of a highly nonlinear structural control device, that is, magnetorheological damper. Experimental results showed that the proposed evolving Takagi-Sugeno-Kang fuzzy model can identify and grasp the nonlinear behaviour of magnetorheological damper very well with minimal number of inputs and fuzzy rules.
Gu, X, Li, J, Li, Y & Askari, M 2016, 'Frequency control of smart base isolation system employing a novel adaptive magneto-rheological elastomer base isolator', Journal of Intelligent Material Systems and Structures, vol. 27, no. 7, pp. 849-858.View/Download from: Publisher's site
In the past decades, base isolation techniques have become increasingly popular for seismic protection of civil structures owing to its capability of decoupling buildings from harmful ground motion. However, it has been recognised recently that the traditional passive base isolation technique could encounter a serious problem during earthquakes due its incapability in adjusting the isolation frequency to cope with the unpredictability and diversity of earthquakes. To address this challenge, a great deal of research efforts have been conducted to improve traditional base isolation systems, most of which focused on hybrid supplementary devices (passive, active and semi-active types) for the isolators to control displacement or to dissipate seismic energy. On the other hand, the most effective approach to address the aforementioned challenge should lay on varying isolator stiffness in real-time to achieve real-time spontaneous decoupling. A recent advance of the development of an adaptive magneto-rheological elastomer base isolator has brought such idea to reality as the new magneto-rheological elastomer base isolator is capable to alter its stiffness significantly in real-time. In this article, an innovative smart base isolation system employing such magneto-rheological elastomer isolator is proposed and a novel frequency control algorithm is developed to shift the fundamental frequency of the structure away from the dominant frequency range of earthquakes. Such design enables the building to avoid resonant state in real-time according to the on-coming spectrum of the earthquakes. Extensive simulation has been conducted using a five-storey benchmark model with the isolation system, and testing results indicate that the proposed control system is able to significantly suppress both the floor accelerations and inter-storey drifts simultaneously under different earthquakes.
Askari, M & Markazi, AHD 2012, 'A new evolving compact optimised Takagi-Sugeno fuzzy model and its application to nonlinear system identification', International Journal of Systems Science, vol. 43, no. 4, pp. 776-785.View/Download from: Publisher's site
A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive clustering and non-dominated sorting genetic algorithm. The proposed method consists of two parts. The first part is related to the selection of most relevant or influencing inputs to the system and the second one is related to the tuning of fuzzy rules and parameters of the membership functions. The main purpose of the proposed scheme is to reduce the complexity and increase the accuracy of the model. In particular, three objectives are considered in the process of optimisation, namely, the number of inputs, number of rules and the root mean square of the modelling error. The performance of the developed method is validated by identifying the Box-Jenkins nonlinear benchmark system, and to the modelling of the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper. The latter is also a challenging problem due to the inherent hysteretic and highly nonlinear dynamics of the MR damper. It is shown that the developed evolving Takagi-Sugeno (T-S) fuzzy model can identify and grasp the nonlinear dynamics of both systems very well, while a small number of inputs and fuzzy rules are required for this purpose. © 2012 Copyright Taylor and Francis Group, LLC.
Li, J, Li, Y, Askari, M & Ha, QP 2014, 'Future Intelligent Civil Structures: Challenges and Opportunities', The 31st International Symposium on Automation and Robotics in Construction and Mining (ISARC 2014), International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Sydney, pp. 72-79.View/Download from: Publisher's site
An intelligent civil structure offers ultimate protection to its structure, contents and occupants in terms of safety and functionality against undesired dynamic loadings and structural deficiency. In this paper, the concept of the future intelligent civil structure featuring self-adaptive, selfprognostic, self-sensing, self-powering and self-repairing abilities, is proposed. A decade research efforts from Centre for Built Infrastructure Research, University of Technology Sydney, towards the development and concept proof of such intelligent structure is reviewed.
Askari, M, Li, J & Samali, B 2013, 'A Multi-objective Subtractive FCM Based TSK Fuzzy System with Input Selection, and Its Application to Dynamic Inverse Modelling of MR Dampers', Lecture Notes in Computer Science, International Conference on Artificial Intelligence and Soft Computing, Elsevier, Zakopane, POLAND, pp. 215-226.View/Download from: Publisher's site
A new encoding scheme is presented for a fuzzy-based nonlinear system identification methodology, using the subtractive Fuzzy C-Mean clustering and a modified version of non-dominated sorting genetic algorithm. This method is able to automatically select the best inputs as well as the structure of the fuzzy model such as rules and membership functions. Moreover, three objective functions are considered to satisfy both accuracy and compactness of the model. The proposed method is then employed to identify the inverse model of a highly nonlinear structural control device, namely Magnetorheological (MR) damper. It is shown that the developed evolving TakagiSugeno-Kang (TSK) fuzzy model can identify and grasp the nonlinear dynamics of inverse systems very well, while a small number of inputs and fuzzy rules are required for this purpose.
Askari, M, Li, J & Samali, B 2012, 'Adaptive multiple forgetting factor recursive least square (AMFF-RLS) for real-time structural identification', From materials to structures: Advancement through innovation - 22nd Australian Conference On The Mechanics Of Structures And Materials, Australasian Conference on the Mechanics of Structures and Materials, CRC press/Balkema, Sydney, Australia, pp. 879-884.View/Download from: Publisher's site
System identification refers to any systematic way of deriving or improving models of systems through the use of experimental and field testing inputâoutput data. In the field of civil engineering, identification of the state of the structure during the dynamic loads, such as earthquake, to predict the current state of the structure and detect any damage or hazard,when it occurs, has posed a great challenge to the research community. Therefore, online and real-time structural parameters identification has recently drawn more attractions, although few research works have been reported especially for cases where measurement data are contaminated by high level noise. The Recursive Least Square with single forgetting factor has been widely used in estimation and tracking of time-varying parameters in the fields of electrical and mechanical engineering. However, when there are multiple parameters that each (or some) varies with a different rate, this method cannot perform well. On the other hand, a priori information on the changing rate of the parameters might not be available, and the forgetting factors must be updated adaptively. This paper presents a new adaptive tracking technique, based on the Recursive Least Square (RLS) approach with Adaptive Multiple Forgetting Factors (AMFF). The proposed method considers an adaptive rule for each of the forgetting factors assigned to each of the parameters and thus, enables simultaneous estimation of the time-varying stiffness and damping of the storeys of the structure. Numerical examples show that results of this RLS-based approach are accurate and robust, even when the observed data are contaminated with different types and significantlevels of noise.
Askari, M, Li, J & Samali, B 2011, 'Semi-Active LQG Control of Seismically Excited Nonlinear Buildings using Optimal Takagi-Sugeno Inverse Model of MR Dampers', Procedia Engineering: The Proceedings of the Twelfth East Asia-Pacific Conference on Structural Engineering and Construction EASEC12, East Asia-Pacific Conference on Structural Engineering and Construction, Elsevier, Hong Kong, pp. 2765-2772.View/Download from: Publisher's site
A novel semi-active control method for a seismically excited nonlinear benchmark building equipped with magnetorheological (MR) dampers is presented and evaluated in this paper. While Linear Quadratic Gaussian (LQG) controller is designed to estimate the optimal control force of a MR damper, the required voltage input for the damper to produce such control force is achieved by a proposed optimal Takagi- Sogeno(T-S) fuzzy inverse model. The proposed T-S fuzzy inverse model of dampers is derived using subtractive clustering, non-dominated sorting genetic algorithm II (NSGAII) and adaptive neuro-fuzzy inference systems (ANFIS). The effectiveness of this strategy is illustrated and verified using simulated response of a 20-storey full-scale nonlinear benchmark building excited by several historical earthquake records. The designed semi-active system is compared with the performances of active control as well as clipped optimal control (COC) systems, which are based on the same nominal controller as is used in this study. The results are discussed based on the evaluation criteria suggested for the benchmark problem by International Association for Structural Control and Monitoring (IASCM) for comparison with other algorithms and demonstrate the superiority of this scheme over other strategies.