Seismic fragility estimates of a moment-resisting frame building controlled by MR dampers using performance-based design
Engineering Structures, Jun 1, 2016
Abstract Seismic fragility was estimated for a controlled high-rise building using 200 kN magneto... more Abstract Seismic fragility was estimated for a controlled high-rise building using 200 kN magnetorheological (MR) dampers with direct performance-based design (DPBD) to assess seismic vulnerability and to validate the performance of the DPBD which was previously developed. The DPBD offers multiple control design layouts for various performance levels subjected to different hazard levels using multi-objective optimization approaches. These multiple control design layouts for the given performance levels need to be validated using random seismic excitations because those performance-based designs (PBD) had been devolved based on the specific strength of design objective earthquakes (i.e., hazard levels) from the DPBD. In order to evaluate those PBD cases using MR dampers, two different approaches for fragility estimation of the four PBD cases under two hazard levels are conducted: traditional approach using the overall maximum interstory drift and system reliability approach which considers multiple limit states associated with the maximum interstory drift for stories within the entire system. The results are compared using 41 earthquake ground motions. From this study, overall seismic fragility relations have been derived from extensive fragility analyses in terms of broad range of hazard levels for multiple performance levels which were achieved by new direct performance-based design using MR dampers. Moreover, it is observed that the multiple performance-based control design cases obtained from DPBD clearly show significant reduction in seismic vulnerability compared to the uncontrolled case. It also shows different seismic fragility estimates against seismic hazards reflecting the performance enhancement based on the initial objective of the DPBD. Based on the results, the system reliability approach can identify the stories that have close interstory drifts to the overall maximum value allowing for more accurate estimates of the seismic fragility of multi-story buildings.
Automated air-coupled impact echo based non-destructive testing using machine learning
The integration of sensing technology with structural health monitoring (SHM) has lead to advance... more The integration of sensing technology with structural health monitoring (SHM) has lead to advancements in how structures are monitored and investigated. One of the issues that has accompanied advancement in the industry is the time required to carry out testing on large-scale concrete reinforced structures using methods like impact-echo and ground penetrating radar (GPR). Back end processing and automation of testing systems are two means of addressing time consuming testing programs. This study proposes a semi-autonomous testing setup to carry out impact-echo testing on a lab specimen and a full-scale field structure. The testing method is coupled with artificial neural network (ANN) processing to decrease the need for user-interactions to produce results from the testing. The use of the semi-autonomous testing method and ANN processing is postulated to decrease the time needed for testing and improve the repeatability and accuracy of the impact-echo testing.
Seismic Fragility Analysis for Semi-Actively Controlled Structures Using MR Dampers
A Review of Benchmark Study on Response Control of Seismically-Excited Highway Bridges
A benchmark model of seismically excited bridge has been proposed in 2004. The benchmark problem ... more A benchmark model of seismically excited bridge has been proposed in 2004. The benchmark problem package consists of dynamical model of the bridge with designs of sample controllers in MATLAB environment with prescribed evaluation criteria and ground motions. Research contributions to the benchmark study by researchers around the world have been published in as special issue (vol. 16, issues; 5–6, year; 2009) of Structural Control and Health Monitoring. This paper presents a review of contributions to the ...
Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor typ... more Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures.
Structural and mechanical system identification under dynamic loading has been an important resea... more Structural and mechanical system identification under dynamic loading has been an important research topic over the last three or four decades. Many Kalman-filtering-based approaches have been developed for linear and nonlinear systems. For example, to predict nonlinear systems, an unscented Kalman filter was applied. However, from extensive literature reviews, the unscented Kalman filter still showed weak performance on systems with large degrees of freedom. In this research, a modified unscented Kalman filter is proposed by integration of a cubature Kalman filter to improve the system identification performance of systems with large degrees of freedom. The novelty of this work lies on conjugating the unscented transform with the cubature integration concept to find a more accurate output from the transformation of the state vector and its related covariance matrix. To evaluate the proposed method, three different numerical models (i.e., the single degree-of-freedom BouceWen model, the linear 3-degrees-of-freedom system, and the 10-degrees-of-freedom system) are investigated. To evaluate the robustness of the proposed method, high levels of noise in the measured response data are considered. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.
Computer-aided Civil and Infrastructure Engineering, May 15, 2018
Visual inspection has traditionally been used for structural health monitoring. However, assessme... more Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring are costly and inefficient because of the number of inspectors and sensors required. To date, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has become popular, but UAVs require skilled pilots or a global positioning system (GPS) for autonomous flight. Unfortunately, GPS cannot be used by a UAV for autonomous flight near some parts of certain structures (e.g., beneath a bridge), but these are the critical locations that should be inspected to monitor and maintain structural health. To address this difficulty, this article proposes an autonomous UAV method using ultrasonic beacons to replace the role of GPS, a deep convolutional neural network (CNN) for damage detection, and a geo-tagging method for the localization of damage. Concrete cracks, as an example of structural damage, were successfully detected with 97.7% specificity and 91.9% sensitivity, by processing video data collected from an autonomous UAV.
Despite many contact-sensor-based methods have been proposed to monitor and detect structural def... more Despite many contact-sensor-based methods have been proposed to monitor and detect structural defects, there are still difficulties compensating for environmental effects and malfunctions of attached sensors, which are main reasons for transmitting false signals. Moreover, regardless of releasing correct or incorrect signals, it eventually leads to human-conducted on-site inspections. In light of these shortcomings, vision-based inspections are considered as potential approach to overcome the explained issues. A number of vision-based methods for detecting damages from images have been developed. However, there are only a few vision-based methods for detecting loosened bolts. Thus, a computer-vision method for detecting loosened bolts is proposed. This study includes two algorithms. The first one is a preprocessing to crop bolt images from bolted-joint images. The second algorithm is a feature extraction by integrating previously proposed algorithms in computer-vision. To accomplish an automated inspection, linear support vector machine is trained and used as a classifier. The robustness of the proposed is verified by the experimental validation; 22 bolt images are used to build a classifier, and 40 bolt images are tested.
Many structural damage detection methods using machine learning algorithms and clustering methods... more Many structural damage detection methods using machine learning algorithms and clustering methods have been proposed and developed in recent years. Novelty detection is a common method that is based on an unsupervised learning technique to detect structural damage. The detection process involves applying the novelty detection algorithm to recognize abnormal data from the testing data sets. In order to make these algorithms capable of identifying abnormal data, sufficient normal data must first be obtained and used as training data. It is the fact that sufficient normal data is relatively convenient to measure compared to abnormal data for large-scale civil structures. Abnormal data from the testing data sets can be identified by using the well-trained normal model established by the algorithms. In this paper, a machine learning based novelty detection method called the Density Peaks based Fast Clustering Algorithm (DPFCA) is introduced and some improvements to this algorithm are made to increase the precision of detecting and localizing the damage in an experimental structure. Feature extraction is also an important factor in the process of damage detection. Thus, two damage-sensitive features such as crest factor, and transmissibility are extracted from the measured responses in the experiments. Experimental results showed good performance of the innovative method in detecting and locating the structural damage positions in various scenarios.
This chapter introduces three new multi-objective genetic algorithms (MOGAs) for minimum distribu... more This chapter introduces three new multi-objective genetic algorithms (MOGAs) for minimum distributions of both actuators and sensors within seismically excited large-scale civil structures such that the structural responses are also minimized. The first MOGA is developed through the integration of Implicit Redundant Representation (IRR), Genetic Algorithm (GA), and Non-dominated sorting GA 2 (NSGA2): NS2-IRR GA. The second one is proposed by combining the best features of both IRR GA and Strength Pareto Evolutionary Algorithm (SPEA2): SP2-IRR GA. Lastly, Gene Manipulation GA (GMGA) is developed based on novel recombination and mutation mechanism. To demonstrate the effectiveness of the proposed three algorithms, two full-scale twenty-story buildings under seismic excitations are investigated. The performances of the three new algorithms are compared with the ones of the ASCE benchmark control system while the uncontrolled structural responses are used as a baseline. It is shown that the performances of the proposed algorithms are slightly better than those of the benchmark control system. In addition, GMGA outperforms the other genetic algorithms.
Identification of large-scale systems with noisy data using an iterated cubature unscented Kalman filter
Online structural health monitoring of large-scale models of infrastructures under hazardous envi... more Online structural health monitoring of large-scale models of infrastructures under hazardous environmental loadings— like earthquakes—has been a vital research topic during recent years. A linear Kalman filter has been employed in many cases in which the desired parameters are extracted in a propagated state vector during a recursive regime. Also, many other kinds of nonlinear filters have been developed for nonlinear systems identification following the linear Kalman filter concept, such as the unscented Kalman filter and the cubature Kalman filter. The main contribution of these two Kalman filtering techniques relies on the propagation of a covariance matrix instead of nonlinear transition and measurement functions. Our extensive literature review shows that divergence of estimated states for large degree-offreedom (DoF) models is the main drawback of these techniques. To overcome this weakness, these two filters’ predefined points, sigma points, are combined—with some modifications—to have more predetermined points for the propagation of states and output of covariance matrices. The proposed technique was developed to be used for large DoF systems with a high level of noisy measured data, which indicates a robust identification system. To evaluate the proposed method, a numerical model (10 DoF linear system) with high levels of noise in the measured response data are employed to evaluate the robustness of the proposed method. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.
This paper presents comparative studies for various sensor faults and noise effects on the perfor... more This paper presents comparative studies for various sensor faults and noise effects on the performance of several recently proposed semi-active control algorithms for the control of large-scale magnetorheological dampers. Sensor faults or noises due to various environmental factors and long-term deteriorations may cause degradations in the performance of the control system. In this paper, the authors have carried out an in-depth literature review of the sensor fault or malfunction and noise problems, diagnostic methods and compensation approaches applicable to semiactive control algorithms. For three recently developed semi-active controllers, namely clipped optimal control, decentralized output feedback polynomial control and Lyapunov controller, an extensive study has been carried out on the robustness of these controllers during sensor faults and noise effects. Based on this research, a new real-time qualitative model-based sensor fault detection and diagnosis (RMSFDD) method has been proposed. This novel RMSFDD method shows a good performance during diverse sensor fault types in semi-active control with a real-time application without any training and heavy computational cost.
While many structural damage detection methods have been developed in recent decades, few data-dr... more While many structural damage detection methods have been developed in recent decades, few data-driven methods in unsupervised learning mode have been developed to solve the practical difficulties in data acquisition for civil infrastructures in different scenarios. To address such a challenge, this paper proposes a number of improved unsupervised novelty detection methods and conducts extensive comparative studies on a laboratory scale steel bridge to examine their performances of damage detection. The key concept behind unsupervised novelty detection in this paper is that only normal data from undamaged structural scenarios are required to train statistical models with these methods. Then, these trained models are used to identify abnormal testing data from damaged scenarios. To detect structural damage in the form of loosening bolts in the steel bridge, four machine-learning methods (i.e., K-nearest neighbors method, Gaussian mixture models, One-class support vector machines, Density peaks-based fast clustering method) and one deep learning method using a deep auto-encoder are selected. Meanwhile, some modifications and improvements are made to enable these methods to detect structural damage in unsupervised novelty detection mode. In their comparative studies, the advantages and disadvantages of these methods are analyzed based on their results of structural damage detection.
EWSHM - 7th European Workshop on Structural Health Monitoring, Jul 8, 2014
Over the past number of decades the structural health monitoring (SHM) research community has dev... more Over the past number of decades the structural health monitoring (SHM) research community has developed and published a large variety and number of methodologies for the purpose of detecting and locating damage in a structure using sensor measurement data. While almost all of these methods have demonstrated some degree of success in detecting damage, different approaches have differing costs, and corresponding tradeoffs in performance. Typical costs include computational effort, the development of an accurate structural model, or the collection of a large volume of data. Whether or not these costs are worth the investment depends on the specific SHM scenario. In this paper we analyze four different SHM methodologies, including model-based and data-based approaches, outlining their individual strengths and weaknesses, tradeoffs between cost and performance,and suggesting appropriate application areas for each. The efficacy of the methods is evaluated using data collected from a steel-frame laboratory structure.
DNoiseNet: Deep learning-based feedback active noise control in various noisy environments
Engineering Applications of Artificial Intelligence, May 1, 2023
Hybrid Concrete Crack Segmentation and Quantification Across Complex Backgrounds Without a Large Training Dataset
Springer eBooks, Oct 5, 2021
Vision-based concrete crack detection technique using cascade features
This paper presents an existing face detection method using cascade features updated for determin... more This paper presents an existing face detection method using cascade features updated for determining the cracks on concrete surfaces. The main goal of structural health monitoring (SHM) is to safeguard our existing structures from cracks, corrosion, delamination, and spalls due to incessant use of structures. Cracks are the foremost defect that will occur in the structures, and they require quick attention before they lead to structural failure; it is a laborious job to detect the cracks using personnel (visual inspection) practices, which produce highly unreliable results. The results of contact sensor-based crack detection techniques, however, mainly depend on parameters such as temperature, sensitivity, accessibility, etc. Recently there has been high expansion in computer vision (image processing) techniques that facilitate the detection of cracks. In this study, a modified cascade face detection technique based on the Viola-Jones algorithm is proposed to detect cracks in concrete walls. Cascade features calculated from the Viola-Jones algorithm are trained on positive and negative datasets of images with and without cracks. Once training is completed, the Viola-Jones algorithm spots the cracks on test images with bounding boxes drawn around the region of the cracks.
Modal strain energy has been reported by researchers as one of the sensitive physical measures th... more Modal strain energy has been reported by researchers as one of the sensitive physical measures that can be used as a damage index in structural health monitoring. Inverse problem-solving based approaches using single-objective optimization algorithms are also one of the promising damage identification methods. However, integration of these potential methods is currently limited with partial success in the detection of structural damages due to errors and noises. In this study, a novel damage detection approach using hybrid multi-objective optimization algorithms is proposed to detect multiple damages in a 3-dimensional steel structure. This study developed an approach to overcome the shortcomings of the single-objective genetic algorithm based approaches using multi-objective formulations for minimizing errors of damage indices between actual damaged structures and simulated damages. The performance of the proposed hybrid multi-objective genetic algorithm is compared to that of traditional single-objective optimizations based approaches. This study accurately detects the location and extent of induced multiple minor damages of the laboratory 3-dimensional steel structure.
Motion Magnification Based Damage Detection Using High Speed Video
Structural system identification and damage detection is an important engineering challenge due t... more Structural system identification and damage detection is an important engineering challenge due to the increase in aging infrastructure in the United States. In order to identify a structural system or detect structural damage, measured responses of structures are used for structural identification and damage detection. Typically the acceleration response is measured, although displacement responses inherently have more information of structural dynamic behavior than acceleration or velocity. In this paper a displacement measurement methodology using high-speed video, previously proposed using the motion magnification algorithm and optical flow, is used as the input for a damage detection algorithm using an unscented Kalman filter. This noncontact displacement measurement methodology has advantages; it does not require a time consuming instrumentation process and does not add any additional mass to the structure. However, the methodology still needs improvement due to its higher noise level relative to traditional accelerometer and laser vibrometer measurements. In order to detect structural damage using displacements measured from high speed video, an unscented Kalman filter is used to simultaneously remove noise from the displacement measurement and identify the current stiffness and damping coefficient values of the structure assuming a known mass. To validate the damage detection method, a numerical state-space formulation is derived for the structural system. While traditional formulations for unscented Kalman filter based approaches require such information to predict structural parameters such as stiffness and damping, however this newly derived dynamic formation does not require external forcing information. Experimental tests are carried out to test the proposed method. Steel cantilever beams are tested with bolt-loosening of the boundary condition connection as a damage scenario. The experimental results show reasonable predictions of the stiffness and damping values compared to simple dynamic analysis calculations of the beam. doi: 10.12783/SHM2015/294
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