Can supervise: YES
- Structural Health Monitoring
- Structural Dynamics
- Vibration Testing and Analysis
- Engineering Computation
- Finite Element Analysis
- Structural Analysis
Anaissi, A, Makki Alamdari, M, Rakotoarivelo, T & Khoa, NLD 2018, 'A tensor-based structural damage identification and severity assessment', Sensors (Switzerland), vol. 18, no. 1.View/Download from: UTS OPUS or Publisher's site
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors.
Kalhori, H, Alamdari, MM, Zhu, X & Samali, B 2018, 'Nothing-on-Road Axle Detection Strategies in Bridge-Weigh-in-Motion for a Cable-Stayed Bridge: Case Study', JOURNAL OF BRIDGE ENGINEERING, vol. 23, no. 8.View/Download from: UTS OPUS or Publisher's site
Makki Alamdari, M, Samali, B, Li, J, Lu, Y & Mustapha, S 2017, 'Structural condition assessment using entropy-based time series analysis', Journal of Intelligent Material Systems and Structures, vol. 28, no. 14, pp. 1941-1956.View/Download from: UTS OPUS or Publisher's site
© 2017, © The Author(s) 2017. We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.
Alamdari, MM, Rakotoarivelo, T & Khoa, NLD 2017, 'A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge', Mechanical Systems and Signal Processing, vol. 87, no. PART A, pp. 384-400.View/Download from: UTS OPUS or Publisher's site
© 2016 Elsevier Ltd This paper presents the results of a large scale Structural Health Monitoring application on the Sydney Harbour Bridge in Australia. This bridge has many structural components, and our work focuses on a subset of 800 jack arches under the traffic lane 7. Our goal is to identify which of these jack arches (if any) respond differently to the traffic input, due to potential structural damages or instrumentation issues. We propose a novel non-model-based method to achieve this objective, using a spectrum-driven feature based on the Spectral Moments (SMs) from measured responses from the jack arches. SMs contain information from the entire frequency range, thus subtle differences between the normal signals and distorted ones could be identified. Our method then applies a modified k-means−− clustering algorithm to these features, followed by a selection mechanism on the clustering results to identify jack arches with abnormal responses. We performed an extensive evaluation of the proposed method using real data from the bridge. This evaluation included a control component, where the approach successfully detected jack arches with already known damage or issues. It also included a test component, which applied the method to a large set of nodes over a month of data to detect any potential anomaly. The detected anomalies turned out to have indeed system issues after further investigations.
Sun, M, Alamdari, MM & Kalhori, H 2017, 'Automated Operational Modal Analysis of a Cable-Stayed Bridge', JOURNAL OF BRIDGE ENGINEERING, vol. 22, no. 12.View/Download from: UTS OPUS or Publisher's site
Fakih, MA, Mustapha, S, Makki Alamdari, M & Ye, L 2017, 'Symbolic dynamics time series analysis for assessment of barely visible indentation damage in composite sandwich structures based on guided waves', Journal of Composite Materials, vol. 51, no. 29, pp. 4129-4143.View/Download from: UTS OPUS or Publisher's site
© 2017, © The Author(s) 2017. This study addresses the detection and localization of barely visible indentation damage in composite sandwich structures using ultrasonic guided waves. A quasi-static loading was gradually applied on a specimen of carbon fiber reinforced epoxy with honeycomb core, with the resulting dent size varying between 0.2 and 2.7 mm. The fundamental symmetric (S 0 ) Lamb wave mode was excited to interrogate the structure. An anomaly measure was established based on symbolic time series analysis; it was defined as the ratio between the norms of probability vectors obtained from the symbol sequence vectors before and after damage has occurred. The symbolic time series analysis method transforms time series data into symbol sequences according to a pre-constructed symbol space using a set number of partitions. The number of partitions selected was determined based on the maximum Shannon's entropy approach. An imaging algorithm was adopted in order to localize the damage. The effects of the excitation frequency and the number of partitions on the precision of prediction were investigated. The adopted approach showed high sensitivity to a very small change of 0.2 mm on the surface panel after a quasi-static loading of 2-mm indentation. Furthermore, the ability of the method to detect progressive damage was demonstrated. The results obtained demonstrate that symbolic time series analysis has excellent potential for use in detecting small defects such as barely visible indentation damage.
Kalhori, H, Makki Alamdari, M, Zhu, X, Samali, B & Mustapha, S 2017, 'Non-intrusive schemes for speed and axle identification in bridge-weigh-in-motion systems', Measurement Science and Technology, vol. 28, no. 2, pp. 1-16.View/Download from: UTS OPUS or Publisher's site
© 2017 IOP Publishing Ltd. Bridge weigh-in-motion (BWIM) is an approach through which the axle and gross weight of trucks travelling at normal highway speed are identified using the response of an instrumented bridge. The vehicle speed, the number of axles, and the axle spacing are crucial parameters, and are required to be determined in the majority of BWIM algorithms. Nothing-on-the-road (NOR) strategy suggests using the strain signals measured at some particular positions underneath the deck or girders of a bridge to obtain this information. The objective of this research is to present a concise overview of the challenges of the current non-intrusive schemes for speed and axle determination through bending-strain and shear-strain based approaches. The problem associated with the global bending-strain responses measured at quarter points of span is discussed and a new sensor arrangement is proposed as an alternative. As for measurement of local responses rather than the global responses, the advantage of shear strains over bending strains is presented. However, it is illustrated that shear strains at quarter points of span can only provide accurate speed estimation but fail to detect the correct number of axles. As a remedy, it is demonstrated that, even for closely-spaced axles, the shear strain at the beginning of the bridge is capable of reliably identifying the number of axles. In order to provide a fully automated speed and axle identification system, appropriate signal processing including low-pass filtering and wavelet transforms are applied to the raw time signals. As case studies, the results of experimental testing in laboratory and on a real bridge are presented.
Alamdari, MM, Samali, B, Li, J, Kalhori, H & Mustapha, S 2016, 'Spectral-Based Damage Identification in Structures under Ambient Vibration', JOURNAL OF COMPUTING IN CIVIL ENGINEERING, vol. 30, no. 4.View/Download from: UTS OPUS or Publisher's site
Diez, A, Nguyen, LDK, Alamdari, MM, Wang, Y, Chen, F & Runcie, P 2016, 'A clustering approach for structural health monitoring on bridges', JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING, vol. 6, no. 3, pp. 429-445.View/Download from: UTS OPUS or Publisher's site
Mustapha, S, Ye, L, Dong, X & Alamdari, MM 2016, 'Evaluation of barely visible indentation damage (BVID) in CF/EP sandwich composites using guided wave signals', Mechanical Systems and Signal Processing.View/Download from: UTS OPUS or Publisher's site
© 2016 Elsevier Ltd. Barely visible indentation damage after quasi-static indentation in sandwich CF/EP composites was assessed using ultrasonic guided wave signals. Finite element analyses were conducted to investigate the interaction between guided waves and damage, further to assist in the selection process of the Lamb wave sensitive modes for debonding identification. Composite sandwich beams and panels structures were investigated. Using the beam structure, a damage index was defined based on the change in the peak magnitude of the captured wave signals before and after the indentation, and the damage index was correlated with the residual deformation (defined as the depth of the dent), that was further correlated with the amount of crushing within the core. Both A 0 and S 0 Lamb wave modes showed high sensitivity to the presence of barely visible indentation damage with residual deformation of 0.2 mm. Furthermore, barely visible indentation damage was assessed in composite sandwich panels after indenting to 3 and 5 mm, and the damage index was defined, based on (a) the peak magnitude of the wave signals before and after indentation or (b) the mismatch between the original and reconstructed wave signals based on a time-reversal algorithm, and was subsequently applied to locate the position of indentation.
Gu, X, Yu, Y, Li, J, Li, Y & Alamdari, M 2016, 'Semi-active storey isolation system employing MRE isolator with parameter identification based on NSGA-II with DCD', Earthquake and Structures, vol. 11, no. 6, pp. 1101-1121.View/Download from: UTS OPUS or Publisher's site
Base isolation, one of the popular seismic protection approaches proven to be effective in practical applications, has been widely applied worldwide during the past few decades. As the techniques mature, it has been recognised that, the biggest issue faced in base isolation technique is the challenge of great base displacement demand, which leads to the potential of overturning of the structure, instability and permanent damage of the isolators. Meanwhile, drain, ventilation and regular maintenance at the base isolation level are quite difficult and rather time- and fund- consuming, especially in the highly populated areas. To address these challenges, a number of efforts have been dedicated to propose new isolation systems, including segmental building, additional storey isolation (ASI) and mid-storey isolation system, etc. However, such techniques have their own flaws, among which whipping effect is the most obvious one. Moreover, due to their inherent passive nature, all these techniques, including traditional base isolation system, show incapability to cope with the unpredictable and diverse nature of earthquakes. The solution for the aforementioned challenge is to develop an innovative vibration isolation system to realise variable structural stiffness to maximise the adaptability and controllability of the system. Recently, advances on the development of an adaptive magneto-rheological elastomer (MRE) vibration isolator has enlightened the development of adaptive base isolation systems due to its ability to alter stiffness by changing applied electrical current. In this study, an innovative semi-active storey isolation system inserting such novel MRE isolators between each floor is proposed. The stiffness of each level in the proposed isolation system can thus be changed according to characteristics of the MRE isolators. Nondominated sorting genetic algorithm type II (NSGA-II) with dynamic crowding distance (DCD) is utilised for the optimisation of the para...
Alamdari, M.M., Samali, B. & Li, J. 2015, 'Damage localization based on symbolic time series analysis', Structural Control and Health Monitoring, vol. 22, no. 2, pp. 374-393.View/Download from: UTS OPUS or Publisher's site
Copyright © 2014 John Wiley & Sons, Ltd. The objective of this paper is to localize damage in a single or multiple state at early stages of development on the basis of the principles of symbolic dynamics. Symbolic time series analysis (STSA) of noise-contaminated responses is used for feature extraction to detect and localize a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. Basically, in STSA, statistical features of the symbol sequence can be used to describe the dynamic status of the system. Symbolic dynamics has some useful characteristics making it highly demanded for implementation in real-time observation application such as SHM. First, it significantly reduces the dimension of information and provides information-rich representation of the underlying data. Second, symbolic dynamics and the set of statistical measures built upon it represent a solid framework to address the main challenges of the analysis of nonstationary time data. Finally, STSA often allows capturing the main features of the underlying system whilst alleviating the effects of harmful noise. The method presented in this paper consists of four primary steps: (i) acquisition of the time series data; (ii) creating the symbol space to produce symbol sequences on the basis of the wavelet transformed version of time series data; (iii) developing the symbol probability vectors to achieve anomaly measures; and (iv) localizing damage on the basis of any sudden variation in anomaly measure of different locations. The method was applied on a flexural beam and a 2-D planar truss bridge subjected to varying Gaussian excitation in presence of 2% white noise to examine the efficiency and limitations of the method. Simulation results under various damage conditions confi rmed the efficiency of the proposed approach for localization of gradually evolving deterioration in the structure; however, for the future work, the method nee...
Makki Alamdari, M, Samali, B & Li, J 2015, 'Damage localization based on symbolic time series analysis', Structural Control and Health Monitoring, vol. 22, no. 2, pp. 374-393.View/Download from: UTS OPUS or Publisher's site
The objective of this paper is to localize damage in a single or multiple state at early stages of development on the basis of the principles of symbolic dynamics. Symbolic time series analysis (STSA) of noise-contaminated responses is used for feature extraction to detect and localize a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. Basically, in STSA, statistical features of the symbol sequence can be used to describe the dynamic status of the system. Symbolic dynamics has some useful characteristics making it highly demanded for implementation in real-time observation application such as SHM. First, it significantly reduces the dimension of information and provides information-rich representation of the underlying data. Second, symbolic dynamics and the set of statistical measures built upon it represent a solid framework to address the main challenges of the analysis of nonstationary time data. Finally, STSA often allows capturing the main features of the underlying system whilst alleviating the effects of harmful noise. The method presented in this paper consists of four primary steps: (i) acquisition of the time series data; (ii) creating the symbol space to produce symbol sequences on the basis of the wavelet transformed version of time series data; (iii) developing the symbol probability vectors to achieve anomaly measures; and (iv) localizing damage on the basis of any sudden variation in anomaly measure of different locations. The method was applied on a flexural beam and a 2-D planar truss bridge subjected to varying Gaussian excitation in presence of 2% white noise to examine the efficiency and limitations of the method. Simulation results under various damage conditions confirmed the efficiency of the proposed approach for localization of gradually evolving deterioration in the structure; however, for the future work, the method needs to be verified by experimental data.
Nguyen, V, Dackermann, U, Li, J, Alamdari, MM, Mustapha, S, Runcie, P & Ye, L 2015, 'Damage Identification of a Concrete Arch Beam Based on Frequency Response Functions and Artificial Neural Networks', Electronic Journal of Structural Engineering, vol. 14, no. 1, pp. 75-84.View/Download from: UTS OPUS
This paper presents a vibration-based structural health monitoring (SHM) technique for the identification of damage in a concrete arch beam replica section of the Sydney Harbour Bridge. The proposed technique uses residual frequency response functions (FRFs) combined with principal component analysis (PCA) to form a damage specific feature (DSF) that is used as an input parameter to artificial neural networks (ANNs). Extensive laboratory testing and numerical modelling are undertaken to validate the method. In the proposed technique, FRFs are obtained by the standard modal testing and damage is identified using ANNs that innovatively map the DSF to the severity of damage (length of damage cut). The results of the experimental and numerical validation show that the proposed technique can successfully quantify damage induced to a concrete arch beam simulating a real life structural component of the Sydney Harbour Bridge.
Mustapha, S, Hu, Y, Nguyen, K, Alamdari, MM, Runcie, P, Dackermann, U, Nguyen, VV, Li, J & Ye, L 2015, 'Pattern recognition based on time series analysis using vibration data for structural health monitoring in civil structures', Electronic Journal of Structural Engineering, vol. 14, no. 1, pp. 106-115.View/Download from: UTS OPUS
A statistical pattern recognition technique was developed based on the time series analysis to detect cracking in steel reinforced concrete structures using vibration measurements. The technique has been developed for the Sydney Harbour Bridge. The measurements were collected from single and tri-axial accel-erometers, which were integrated into sensor nodes that were developed at the National ICT Australia. The approach is based on two staged Auto-Regressive (AR) and Auto-Regressive with exogenous inputs (ARX) prediction models. The variation between the residual errors obtained from the intact and damaged states were used to define a Damage Index (DI) capable of identifying physical changed which could be due to structural damage. The effect of the severity of damage on the deviation of the AR-ARX model from its in-tact state was also scrutinised. The results of the field trial and the laboratory testing demonstrated the ability of the approach in identifying the presence of cracking and handling large volumes of data in a very efficient manner.
Alamdari, MM, Li, J & Samali, B 2015, 'Damage identification using 2-D discrete wavelet transform on extended operational mode shapes', ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING, vol. 15, no. 3, pp. 698-710.View/Download from: Publisher's site
Makki Alamdari, M, Li, J & Samali, B 2014, 'FRF-based damage localization method with noise suppression approach', Journal Of Sound And Vibration, vol. 333, no. 14, pp. 3305-3320.View/Download from: UTS OPUS or Publisher's site
Li, J, Samali, B & Alamdari, MM 2014, 'A Novel FRF-Based Damage Localisation Method Using Random Vibration', Applied Mechanics and Materials, vol. 553, pp. 713-718.View/Download from: UTS OPUS or Publisher's site
This paper presents a novel damage localization method based on the measured Frequency Response Functions (FRFs) without demanding any previous data records of the structure in its healthy state. The main innovation of this study starts with reconstruction of FRFs curvature to develop spatial shape functions. It is demonstrated that reconstructed data significantly magnifies the influence of low-frequency spectra in damage detection procedure which is considered the milestone of this approach as excitation of the higher frequencies is not easy to obtain in most practical applications. The modified curvature data in all measured frequencies and locations is interpreted as a two dimensional image and then processed by employing 2-D discrete wavelet transform to detect any abrupt variation at damage site. Level one wavelet decomposition is utilised to provide the finest detail coefficients. It is illustrated that this approach presents a more recognizable pattern at damage site in all measured frequencies. The pattern can be described by a horizontal line parallel to the frequency spectra in 2-D image. Hence, the horizontal detail coefficients are utilised to detect this pattern as they are more sensitive to perturbation with orientation parallel to horizontal axis in the image. The main contribution of this approach lies in the fact that the proposed technique is able to detect the structural damage in all measured frequencies and the effectiveness of the method is independent of the excitation location. Moreover, the results provide a better visualisation at damage site which other FRF-based damage detection methods could not obtain. Applying broadband FRF data in this approach and the fact that there is no need for data from the healthy state of the structure are other advantages accompanying this method. The robustness of the proposed damage identification method was examined with various damage conditions in both single and multiple states. Moreover, the feasib...
Makki Alamdari, M, Li, J, Samali, B, Ahmadian, H & Naghavi, A 2014, 'Nonlinear Joint Model Updating in Assembled Structures', Journal Of Engineering Mechanics-asce, vol. 140.View/Download from: Publisher's site
Dynamic response of mechanical structures is significantly affected by joints. Joints introduce remarkable frictional damping and
localized flexibility to the structure; hence, to obtain a more accurate representation of a system's dynamics, it is crucial to take these effects into
account. This paper investigates the application of finite-element model updating on characterization of a nonlinear joint interface. A thin layer
of virtual elements is used at a joint location to represent the nonlinear behavior of the coupling in the tangential direction. The material
properties of the elements are described by a nonlinear constitutive stress-strain equation that defines the nonlinear state of the joint interface. In
this study, Richard–Abbot elastic-plastic material was considered, which is capable of characterizing energy dissipation and softening
phenomena in a joint at a nonlinear state. Uncertain material parameters are adjusted to minimize the residual between the numerical and
experimental nonlinear frequency responses. Minimization was carried out based on iterative sensitivity-based optimization. The procedure was
implemented on an assembled structure consisting of two steel threaded pipes coupled to each other by a nut interface. It was demonstrated that
the proposed technique significantly reduced the uncertainties in the joint modeling and led to a more reliable description of the assembled
Anaissi, A, Khoa, NLD, Mustapha, S, Alamdari, MM, Braytee, A, Wang, Y & Chen, F 2017, 'Adaptive one-class support vector machine for damage detection in structural health monitoring', Advances in Knowledge Discovery and Data Mining (LNAI), Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Springer, Jeju, South Korea, pp. 42-57.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer International Publishing AG. Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one class data and then classify any new query samples. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter ϭ. This paper proposes a new algorithm named Appropriate Distance to the Enclosing Surface (ADES) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the edge and interior samples, and their distances to the enclosing surface of OCSVM. The algorithm selects the optimal value of ϭ which generates a hyperplane that is maximally distant from the interior samples but close to the edge samples. The sets of interior and edge samples are identified using a hard margin linear support vector machine. The algorithm was successfully validated using sensing data collected from the Sydney Harbour Bridge, in addition to five public datasets. The designed ADES algorithm is an appropriate choice to identify the optimal value of ϭ for OCSVM especially in high dimensional datasets.
Anaissi, A, Khoa, NLD, Rakotoarivelo, T, Alamdari, MM & Wang, Y 2017, 'Self-advised incremental one-class support vector machines: An application in structural health monitoring', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 484-496.View/Download from: Publisher's site
© Springer International Publishing AG 2017. Incremental One-Class Support Vector Machine (OCSVM) methods provide critical advantages in practical applications, as they are able to capture variations of the positive samples over time. This paper proposes a novel self-advised incremental OCSVM algorithm, which decides whether an incremental step is required to update its model or not. As opposed to existing method, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM as proxies for data in order to determine which new data points should be included in the training set and trigger an update of the model's coefficients. This new online OCSVM algorithm was extensively evaluated using real data from Structural Health Monitoring (SHM) case studies. These results showed that this new online method provided significant improvements in classification error rates, was able to assimilate the changes in the positive data distribution over the time, and maintained a high damage detection accuracy in these SHM cases.
Khoa, NLD, Alamdari, MM, Runcie, P & Nguyen, VV 2017, 'Damage identification on bridges using ambient vibration testing', Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure - 5th International Symposium on Life-Cycle Engineering, IALCCE 2016, pp. 283-290.
© 2017 Taylor & Francis Group, London. Structural health monitoring has been increasingly used due to the advances in sensing technology and data analysis, facilitating the shift from time-based to condition-based maintenance. This work is part of the efforts which have applied structural health monitoring to the Sydney Harbour Bridge – one of Australia's iconic structures. It combines data fusion and feature extraction, dimensionality reduction and pattern recognition techniques to accurately distinguish faulty components from well-functioning ones using ambient vibration testing. Specifically, frequency domain decomposition is used to aggregate data from multiple sensors and random projection is used for dimensionality reduction on the feature data. Then, healthy and damaged patterns of bridge components are learned in the lower dimensional projected space using one-class support vector machine. The experimental results showed high feasibility of the proposed method in damage detection and assessment in structural health monitoring.
Makki Alamdari, M, Khoa, N, Rakotoarivelo, T, Kalhori, H & Li, J 2017, 'Structural health monitoring in the Sydney harbour bridge using spectral moments', SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings, Structural Health Monitoring of Intelligence Infrastructure Conference, Brisbane, Australia, pp. 481-490.
© 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved. The motivation behind this paper is to develop a spectral-based damage identification scheme using output only acceleration responses. The presented method is in the context of non-model-based damage identification methods and does not require any representative numerical/analytical model of the structure. The method utilizes spectral moments of the response as damage sensitive feature. Spectral moments directly retrieve information from the power spectral density of the response. Unlike the modal data that only provide information at a limited number of eigen-frequencies, spectral moments capture information from the entire spectra, hence they can distinguish any subtle differences between a normal and distorted signal. The feasibility of the approach in damage identification was validated using real data from the Sydney Harbour Bridge. There are approximately 800 jack arches over a total distance of 1.2 km need to be continuously monitored. For this study, two instrumented jack arches were considered. These joints are located on the eastern side of the bridge underneath the bus lane near the north pylon. One of these two joints had a known crack in 2012, along the front face propagating toward the surface of the deck, while the other joint was intact. This damage was repaired in 2013. Acceleration data were collected from tri-axial accelerometers mounted on the base of each joint before and after repair. The presented spectral-based method along with the hypothesis testing involving the KS-test were applied to obtain a decision on whether or not the structure is damaged. Spectral moments with different orders were also investigated. It was demonstrated that the proposed spectrum-driven feature can reliably distinguish between the healthy and damaged joints which is of great importance for the asset owner. The presented results illustrated high potent...
Nguyen, VV, Li, J, Yu, Y, Dackermann, U & Alamdari, MM 2016, 'Simulation of various damage scenarios using finite element modelling for structural health monitoring systems', Proceedings of the 24th Australian Conference on the Mechanics of Structures and Materials (ACMSM24), Australian Conference on the Mechanics of Structures and Materials, CRC Press/Balkema, Australia, pp. 1541-1546.View/Download from: UTS OPUS
© 2017 Taylor & Francis Group, London. Structural Health Monitoring (SHM) is a developing technology for asset management of structures including bridge assets. A crucial benefit of SHM is its ability to monitor the health status of structures using continuous measurements. As a key in SHM, the application of damage detection algorithms to assess the condition of a structure using vibration measurements can be enhanced by providing structural information under various damaged scenarios, which can be obtained from updated numerical models that realistically represent the in-situ structure. However, the dynamic characteristics of a structure are sensitive to uncertainties of various parameters, including material properties and boundary conditions, which require updating in the Finite Element (FE) model to ensure that the model replicates the actual structure. This study focuses on the development of an FE model for the accurate simulation of a jack arch replica structure of the Sydney Harbour Bridge. An experimental jack arch replica is produced to simulate various damage scenarios for laboratory testing. A matching FE model of the jack arch replica is generated and updated using Genetic Algorithm (GA) based on experimental measurements. Damage is simulated in the updated model and the results are validated using the experimental test results. The successful simulation of damage using updated FE models enables the generation of a large number of damage cases that can be trained into an SHM system to improve its damage detection capabilities.
Alamdari, MM, Khoa, NLD, Runcie, P, Li, J & Mustapha, S 2016, 'Characterization of gradually evolving structural deterioration in jack arch bridges using support vector machine', Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks - Proceedings of the 8th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2016, pp. 2322-2327.View/Download from: UTS OPUS
© 2016 Taylor & Francis Group, London. The main objective of structural health monitoring is to provide reliable information about the health state of the critical structures by implementing a damage characterization strategy to detect the presence of damage, location, severity as possibly failure prediction as soon as the damage occurs. This paper presents a robust approach to detect and characterize a gradually evolving damage based on time responses data captured from a steel reinforced concrete structure. The presented method is in the context of unsupervised and nonmodel-based approaches, hence, there is no need for any representative numerical/finite element model of the structure to be built. In this work, we propose one-class support vector machine as an anomaly detection method. One-class support vector machine fits well for damage diagnosis in structural health monitoring since there may exist many damaged patterns and one-class support vector machine can detect all of them as anomalies. To demonstrate the feasibility of the method in the detection and assessment of a gradually evolving deterioration, a test bed was established to replicate a concrete jack arch which is a main structural component on the Sydney Harbour Bridge – one of Australia's iconic structures. The structure is a concrete cantilever beam with an arch section which is located on the eastern side of the bridge underneath the bus lane. It is assumed that the structure is subjected to Gaussian white noise excitation. A crack is introduced in the structure using a cutting saw and its length is progressively increased in four stages while the depth was constant; these four damage cases correspond to less than 0.5% reduction in the first three modes of the structure. The damage identification results using the presented approach demonstrated the feasibility of applying support vector machine as a learning technique for damage characterization in structural health monitoring. The method a...
Cheema, P, Khoa, NLD, Alamdari, MM, Liu, W, Wang, Y, Chen, F & Runcie, P 2016, 'On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data', CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, ACM, Indianapolis, Indiana, USA, pp. 1813-1822.View/Download from: UTS OPUS or Publisher's site
Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.
Alamdari, MM, Khoa, NLD, Rakotoarivelo, T, Kalhori, H & Mustapha, S 2016, 'Damage identification in the concrete jack arche bridge using spectral moments', Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks - Proceedings of the 8th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2016, International Conference on Bridge Maintenance, Safety and Management (IABMAS), CRCNet, Foz Do Iguacu, Brazil, pp. 2271-2276.
© 2016 Taylor & Francis Group, London.The motivation behind this paper is to develop a spectral-based damage identification scheme using output only acceleration response. The method utilizes spectral moment of the response as damage sensitive feature. Spectral moments retrieve information directly from the power spectrum. The major advantages of the method include, first, the method works based on the output only measurement data without demanding any representative model of the structure; second, unlike modal data, the presented method is a broadband approach which implements information from a wide frequency range. The feasibility of the approach in damage identification was validated using real data from the Sydney Harbour Bridge. Currently the bridge carries eight lanes of road traffic and two railway lines. Traffic lane 7 is a dedicated bus and taxi lane on the Eastern side of the bridge. Lane 7 consists of an asphalt road surface on a concrete deck supported by concrete and steel jack arches. There are approximately 800 jack arches over a total distance of 1.2 km. The jack arches are physically very difficult to access and are inspected typically at two yearly intervals according to standard visual inspection practices. For this study, three instrumented jack arches were considered. These joints are located on the eastern side of the bridge underneath the bus lane near the north pylon. One of the joints had a known crack, along the front face and the crack propagated toward the surface of the deck, while the other joints were intact: one very far from the damaged joint and the other one very close to the damaged joint. Acceleration data were collected from tri-axial accelerometers mounted on the base of each joint; data were collected before and after repair conducted on the damaged joint. The presented method was applied to see whether the presence of crack in the damaged joint can be identified and to investigate the impact of repair on the damaged joint...
Alamdari, MM, Li, J & Samali, B 2014, 'Damage localisation using symbolic time series approach', Conference Proceedings of the Society for Experimental Mechanics Series, Conference of Society for Experimental Mechanics Series, pp. 109-115.View/Download from: UTS OPUS or Publisher's site
© The Society for Experimental Mechanics, Inc. 2014. The objective of this paper is to localise damage in a single or multiple state at early stages of development based on the principles of symbolic dynamics. Symbolic Time Series Analysis (STSA) of noise-contaminated responses is used for feature extraction to detect and localise a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. The method consists of four primary steps: (1) generating the time series data by a set of measurements over time at evenly spaced locations along the structure; (2) creating the symbol space to generate symbol sequences based on the wavelet transformed version of time series data; (3) developing the symbol probability vectors to achieve anomaly measures; (4) localising damage based on any sudden variation in anomaly measure of two adjacent locations. The method was applied to a clamped–clamped beam subjected to random excitation in presence of 5 % white noise to examine the efficiency and limitations of the method. Simulation results under various damage conditions confirmed the efficiency of the proposed approach for localisation of gradually evolving deterioration in the structure, however, for the future work the method needs to be verified by experimental data.
Li, J, Makki Alamdari, M & Samali, B 2014, 'Application Of Symbolic Time Series Analysis For Damage Localisation In Truss Structures', Proceedings of the 23rd Australasian Conference on the Mechanics of Structures and Materials, Australasian Conference on the Mechanics of Structures and Materials, Southern Cross University, Byron Bay, Australia, pp. 1179-1184.
Reliability of truss bridges can be significantly affected by local damages as damage changes the load
path in the structure. As damage increases, the load-carrying capacity of the structure considerably
reduces which might result in catastrophic failure. Hence, it is important to detect structural damages
as early stage as possible to avoid further propagation. In the present work, a time series-based method
is proposed to detect and localise damage in truss structures. The method works based on Symbolic
Time Series Analysis (STSA) of time responses to localise a gradually evolving deterioration in the
structure according to the changes in the statistical behaviour of symbol sequences. First, the symbol
sequences are generated by transforming the measured time data to symbol space to reduce the
dimension of information and then the probability vectors for each symbol sequence is created.
Damage localisation is carried out by comparing the probability vectors of different measured
locations. It is expected that the damaged member shows a higher degree of variation in the
probability vector which is introduced as damage sensitive feature. Numerical demonstrations on a
plane truss are presented to illustrate the accuracy and efficiency of the proposed method. Gradually
evolving damage is introduced by the stiffness reduction in truss members. Finite element technique is
employed to obtain the time response of the structure subjected to ambient vibration. The simulated
responses are polluted with random noise to take into account the influence of practical uncertainties.
Simulation results under various damage conditions demonstrate the effectiveness of the proposed
algorithm in detection and localisation of gradually evolving damage in single or multiple states in
presence of measurement noise up to 5%.
Makki-Alamdari, M, Li, J & Samali, B 2012, 'A comparative study on the performance of the damage detection methods in the frequency domain', From materials to structures: Advancement through innovation, Australasian Conference on the Mechanics of Structures and Materials, CRC press/Balkema, Sydney, Australia, pp. 867-872.View/Download from: UTS OPUS or Publisher's site
During last two decades, a vast number of damage detection methods have been proposed either in frequency or time domain. These methods normally have their own advantages and limitations or suitable applications; the purpose of this study is to examine the performance of the some popular methods on localisation a possible damage on a sample structure. All of the chosen methods are based on the frequency domain data and work based on proposing a damage sensitive indicator which contains spatial information. Mode shape curvature, frequency response functionsâ curvature, modal strain energy, flexibility matrix and spatial wavelet transform were amongst those damage detection methods were chosen for this study. The case study considers a clamped-clamped beam which was modelled by solid elements in order to define several damage stages based on different crack depth. Damage was simulated by reduction in elastic modulus of the elements in damage zone. The transient response of the structure due to an external impact excitation was obtained by ANSYS and then polluted by different percentages of white noise. The time-domain responses at selected evenly-spaced locationswas then processed byMATLAB to achieve the FRFs and mode shapes respectively by applying Fourier transform and eigenvalue realization algorithm (ERA). Based on the obtained results, it was found that despite some of these methods were suggested by so many researchers, they completely fail in localising damage in the structure even at high level of damage severity.
Makki-Alamdari, M, Li, J & Samali, B 2012, 'A FRF-based damage detection method utilising wavelet decomposition', From materials to structures: Advancement through innovation, Australasian Conference on the Mechanics of Structures and Materials, CRC press/Balkema, Sydney, Australia, pp. 873-877.View/Download from: UTS OPUS or Publisher's site
Damage in a structure causes deviation in dynamic responses of the structure either in frequency or time domain in comparison with its healthy status. The purpose of this study is to present a new damage detection method in order to detect and localize the structural damage. This novel algorithm is based on the directly-measured frequency response functions (FRFs). The approach is composed of three major steps: first, developing the curvature of FRFs which produces spatially distributed shape functions at each frequency coordinate, secondly, normalization of FRFsâ curvature in order to enhance the influence of the lower-frequency-band data; finally decomposition of the obtained profiles (normalized version of FRFsâ curvature) by conducting wavelet analysis to detect any possible structural abnormality through structure. The combination of these three steps leads to a robust algorithm in detection and localisation of any damage in the structure even at small levels which other FRF-based methods were unable to detect. There are some benefits with the presented method: first, this method does not need higher-frequency-range data which is hard to obtain in most civil applications; second, there is no need for baseline data from the intact structure; This is particularly attractive for practical applications as it opens an opportunity for online monitoring of the structural integrity without demanding for any previous data records of the structure. The performance of the method is evaluated on a numerical model and the effect of different parameters such as the location of the excitation point, the level and the location of the damage was studied; the results demonstrated that the method can efficiently identify the location of the damage in the structure even for damage at small levels.