•Identification of full-field modal parameters of structural components from video.•Hankel DMD of the acquired full-field spatiotemporal displacement response.•Experimental validation on three-story ...frame and cantilever beam using ERA.•H-DMD full-field dynamic modes of cantilever beam compare well with FEM analysis.•Full-field dominant vibrational modes of Tacoma Narrows’ suspension cable from video.
The video of vibrating structure provides dense quantitative continuous spatial information that is harnessed using various computer vision algorithms in the past decade. Computer vision algorithms that implement sparse optical flow, have been successful in acquiring the Lagrangian representation of motion. Such vision-based measurement already proved its potential in replacing the need for a contact based vibration measurement sensors. In order to obtain full-field, spatially dense, vibrational modes, a large number of discrete sensors would be necessary throughout the specimen’s length, making it impractical. The phase-based video processing method is found to be successful in visualizing full-field operational mode shapes of a vibrating structure. However, the phase-based optical flow provides an Eulerian representation of the motion at every pixel of the image space, which does not acquire the full-field spatiotemporal Lagrangian displacement trajectory of the structure. Hence, there is a need to design a target-free, noncontact vision-based framework that can directly extract and quantify full-field dynamic modes of a real-life vibrating structural member from its video, by acquiring the trajectory of every particle on the structure at each frame of the video using optical flow. The continuous edge of a moving object is a rich optical feature whose motion perpendicular to its orientation can be tracked in Lagrangian coordinates using optical flow. In a recent paper by authors, the method of measuring full-field displacement response of a vibrating continuous edge of a structural member has been reported. In this paper, the full-field displacement response is acquired using the recently presented method and subsequently, its spatially dense dynamic modes are extracted from its video using the acquired full-field spatiotemporal displacement response of the vibrating structure. The full-field dynamic modes and modal parameters are extracted using the Hankel dynamic mode decomposition method, which is applicable both for linear, as well as nonlinear dynamical systems. Further, experimental validation of the proposed method is presented for two kinds of structures (1) a three-story steel frame, and (2) an aluminum cantilever beam, both undergoing free vibration. The results obtained using the proposed method is validated with the displacement measured using Laser Doppler Vibrometer at a particular point of the edge. The extracted modal properties using the proposed methodology compares satisfactorily with the results of the eigensystem realization algorithm applied to the discrete point accelerometer measurements attached at all three floors of the frame. Also, the numerically obtained mode shapes from the analytical model of the cantilever beam validate the estimated modal parameters and the full-field mode shapes. To validate the efficacy of the proposed method in real-world structures, part of the vibrating cable of Tacoma Narrows bridge is tracked from its video, moments before its collapse. The proposed method does successfully extract the dominating vibrational modes of the cables of the Tacoma Narrows bridge.
Summary
Cables are essential components of the cable‐stayed bridges as they serve as the main load‐bearing component. Hence, continuous monitoring of such cables becomes necessary as they are ...vulnerable to the fatigue damage induced by dynamic loads. Sensors are attached to the cables to examine the health of the cables; however, these contact‐based sensors can malfunction in harsh weather condition, which makes impossible to estimate the cable health in such unfavorable condition. Therefore, in this paper, we propose a completely noncontact video‐based stay‐cable tension measurement technique where the video is recorded using a moving handheld camera at a significant distance from the structure itself. Here, the cable tension is determined from vibration‐based measurement, but the vibration of the cable recorded in the video includes the true vibration of the cable along with the camera motion. Hence, we amalgamated a series of image processing techniques to nullify the camera movement. First, we detect the camera movement based on the movement of the bridge deck and pylon, which are fixed objects, using Kanade–Lucas–Tomasi (KLT) feature tracking algorithm. Then we nullify the camera movement by using the affine transformation matrix obtained by random sample consensus (RANSAC) algorithm. Subsequently from the steady video, the cable motions are estimated using the phase‐based motion estimation technique. From the time history of the cable vibration, real‐time frequency variations are estimated using Short‐Time Fourier Transform (STFT). Finally, the real‐time tension is determined from this dominant frequency variation history using the taut‐string theory. This paper shows the significant potential of camera‐based sensing techniques in structural health monitoring as the mean estimated tension and the design cable tension are found to be comparable.
•A Kalman filtering approach for SHM under changing environmental conditions.•Statistical properties of the residual are used to discriminate damage.•A Bayesian test and a damage measure are proposed ...for practical considerations.•The approach is assessed under uniform and non-uniform temperature fields.
A Kalman filtering based framework for structural damage assessment under changing environmental conditions is presented. The approach is based on the well-known property that the filtering residual is a realization of a white stochastic process when the filter is operating under optimal conditions. To decouple structural damage and environmental effects two additional properties of the filtering residual are employed: i) under global changes in the structure caused by environmental variations the residual remains a white process, and thus its spectral density is approximately constant; ii) local changes caused by structural damage induce peaks in the residual spectral density at the affected vibration frequencies, and thus the residual is a colored process. A Bayesian whiteness test is employed to discriminate between the two situations under finite length data conditions (damage detection), while a normalized damage measure based on the spectral moments of the residual spectral density is proposed as a quantitative damage-sensitive feature (damage quantification). The proposed approach is numerically verified in a continuous beam model of a bridge under different operating conditions, including a robustness assessment for non-uniform temperature fields. It is shown that the approach has the capability to decouple physical changes caused by structural damage and varying environmental conditions, providing robust damage measures for structural health monitoring applications.
AbstractOutput-only algorithms are needed for modal identification when only structural responses are available. The recent years have witnessed the fast development of blind source separation (BSS) ...as a promising signal processing technique, pursuing to recover the sources using only the measured mixtures. As the most popular tool solving the BSS problem, independent component analysis (ICA) is able to directly extract the time-domain modal responses, which are viewed as virtual sources, from the observed system responses; however, it has been shown that ICA loses accuracy in the presence of higher-level damping. In this study, the modal identification issue, which is incorporated into the BSS formulation, is transformed into a time-frequency framework. The sparse time-frequency representations of the monotone modal responses are proposed as the targeted independent sources hidden in those of the system responses which have been short-time Fourier-transformed (STFT); they can then be efficiently extracted by ICA, whereby the time-domain modal responses are recovered such that the modal parameters are readily obtained. The simulation results of a multidegree-of-freedom system illustrate that the proposed output-only STFT-ICA method is capable of accurately identifying modal information of lightly and highly damped structures, even in the presence of heavy noise and nonstationary excitation. The laboratory experiment on a highly damped three-story frame and the analysis of the real measured seismic responses of the University of Southern California hospital building demonstrate the capability of the method to perform blind modal identification in practical applications.
Immediate assessment of structural integrity of important civil infrastructures, like bridges, hospitals, or dams, is of utmost importance after natural disasters. Currently, inspection is performed ...manually by engineers who look for local damages and their extent on significant locations of the structure to understand its implication on its global stability. However, the whole process is time-consuming and prone to human errors. Due to their size and extent, some regions of civil structures are hard to gain access for manual inspection. In such situations, a vision-based system of Unmanned Aerial Vehicles (UAVs) programmed with Artificial Intelligence algorithms may be an effective alternative to carry out a health assessment of civil infrastructures in a timely manner. This paper proposes a framework of achieving the above-mentioned goal using computer vision and deep learning algorithms for detection of cracks on the concrete surface from its image by carrying out image segmentation of pixels, i.e., classification of pixels in an image of the concrete surface and whether it belongs to cracks or not. The image segmentation or dense pixel level classification is carried out using a deep neural network architecture named U-Net. Further, morphological operations on the segmented images result in dense measurements of crack geometry, like length, width, area, and crack orientation for individual cracks present in the image. The efficacy and robustness of the proposed method as a viable real-life application was validated by carrying out a laboratory experiment of a four-point bending test on an 8-foot-long concrete beam of which the video is recorded using a camera mounted on a UAV-based, as well as a still ground-based, video camera. Detection, quantification, and localization of damage on a civil infrastructure using the proposed framework can directly be used in the prognosis of the structure’s ability to withstand service loads.
This paper addresses two problems in structural damage identification: locating damage and assessing damage severity, which are incorporated into the classification framework based on the theory of ...sparse representation (SR) and compressed sensing (CS). The sparsity nature implied in the classification problem itself is exploited, establishing a sparse representation framework for damage identification. Specifically, the proposed method consists of two steps: feature extraction and classification. In the feature extraction step, the modal features of both the test structure and the reference structure model are first blindly extracted by the unsupervised complexity pursuit (CP) algorithm. Then in the classification step, expressing the test modal feature as a linear combination of the bases of the over-complete reference feature dictionary—constructed by concatenating all modal features of all candidate damage classes—builds a highly underdetermined linear system of equations with an underlying sparse representation, which can be correctly recovered by ℓ1-minimization; the non-zero entry in the recovered sparse representation directly assigns the damage class which the test structure (feature) belongs to. The two-step CP–SR damage identification method alleviates the training process required by traditional pattern recognition based methods. In addition, the reference feature dictionary can be of small size by formulating the issues of locating damage and assessing damage extent as a two-stage procedure and by taking advantage of the robustness of the SR framework. Numerical simulations and experimental study are conducted to verify the developed CP–SR method. The problems of identifying multiple damage, using limited sensors and partial features, and the performance under heavy noise and random excitation are investigated, and promising results are obtained.
•Develops a novel classification framework for both locating damage and assessing damage severity.•Alleviates the training process required by traditional pattern recognition based methods.•Straightforward formulations by sparse representation and compressed sensing.•Efficient implementations verified by numerical and experimental examples.
In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field ...sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.
In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial ...information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix’s elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.
Randomly missing data of structural vibration responses time history often occurs in structural dynamics and health monitoring. For example, structural vibration responses are often corrupted by ...outliers or erroneous measurements due to sensor malfunction; in wireless sensing platforms, data loss during wireless communication is a common issue. Besides, to alleviate the wireless data sampling or communication burden, certain accounts of data are often discarded during sampling or before transmission. In these and other applications, recovery of the randomly missing structural vibration responses from the available, incomplete data, is essential for system identification and structural health monitoring; it is an ill-posed inverse problem, however.
This paper explicitly harnesses the data structure itself—of the structural vibration responses—to address this (inverse) problem. What is relevant is an empirical, but often practically true, observation, that is, typically there are only few modes active in the structural vibration responses; hence a sparse representation (in frequency domain) of the single-channel data vector, or, a low-rank structure (by singular value decomposition) of the multi-channel data matrix. Exploiting such prior knowledge of data structure (intra-channel sparse or inter-channel low-rank), the new theories of ℓ1-minimization sparse recovery and nuclear-norm-minimization low-rank matrix completion enable recovery of the randomly missing or corrupted structural vibration response data. The performance of these two alternatives, in terms of recovery accuracy and computational time under different data missing rates, is investigated on a few structural vibration response data sets—the seismic responses of the super high-rise Canton Tower and the structural health monitoring accelerations of a real large-scale cable-stayed bridge. Encouraging results are obtained and the applicability and limitation of the presented methods are discussed.
•Propose a general formulation of randomly missing data completion for recovery of the incomplete or corrupted structural vibration measurements.•Harness the implicit data structure of sparse single-channel vector and reshaped low-rank multi-channel matrix of structural vibration responses.•Missing data recovery enabled by ℓ1-minimization sparse vector recovery and nuclear-norm-minimization reshaped low-rank matrix completion.•Demonstrations by the recorded seismic responses of the Canton Tower and the SHM accelerations of a real cable-stayed bridge.
An accurate and reliable identification of structural damage is of prime importance to evaluate the structural integrity of civil infrastructure systems. However, the adverse effect of normal ...fluctuations in the environment on the effectiveness of damage detection techniques remains a continuing challenge in structural health monitoring applications. In this paper, we present the application of principal component analysis (PCA) to temporal damage detection in continuous beam bridge structures subjected to changing environmental effects. For this purpose, we show that sudden discontinuities in the principal components occur at the onset of damage, and that these discontinuities are observed in the projections of the vibration data on the principal components space. The magnitude of the discontinuity is used to define a damage index for damage quantification. A comprehensive numerical study is used to validate the approach on a continuous beam model of highway bridge structures. In particular a sensitivity analysis is conducted to study the effect of both temperature-dependent boundary conditions and material properties on the principal components for multiple damage scenarios. The numerical results show that the approach is robust to mild nonlinearities caused by the effect of temperature on material properties of composite steel-concrete sections and boundary conditions. Furthermore, the approach is experimentally validated using data of the Z24 bridge in Switzerland measured during a period of one year. It is shown that the approach has the capability of tracking the temporal evolution of various damage states induced on the bridge during the testing program.
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