Identification of Instantaneous Modal Parameter of Time‐Varying Systems via a Wavelet‐Based Approach and Its Application

This work presents an efficient approach using time‐varying autoregressive with exogenous input (TVARX) model and a substructure technique to identify the instantaneous modal parameters of a linear time‐varying structure and its substructures. The identified instantaneous natural frequencies can be used to identify earthquake damage to a building, including the specific floors that are damaged. An appropriate TVARX model of the dynamic responses of a structure or substructure is established using a basis function expansion and regression approach combined with continuous wavelet transform. The effectiveness of the proposed approach is validated using numerically simulated earthquake responses of a five‐storey shear building with time‐varying stiffness and damping coefficients. In terms of accuracy in determining the instantaneous modal parameters of a structure from noisy responses, the proposed approach is superior to typical basis function expansion and regression approach. The proposed method is further applied to process the dynamic responses of an eight‐storey steel frame in shaking table tests to identify its instantaneous modal parameters and to locate the storeys whose columns yielded under a strong base excitation.     

Vibration‐Based Damage Localization in Flexural Structures Using Normalized Modal Macrostrain Techniques from Limited Measurements

Abstract: This article presents damage locating indices based on normalized modal macrostrain (MMS) as improvement on the typical curvature‐dependent methods. Vulnerability to noise and the use of numerical differentiation procedures are the key factors for the poor performance of many curvature‐dependent methods using displacement mode shapes. Whereas dynamic distributed strain measurement data from long‐gauge FBG sensors have significantly improved the performance of many damage identification methods, the sensitivity to local damage diminishes as the gauge length increases. The proposed model‐free damage identification techniques based on normalized MMS vectors are successfully implemented to locate damage in beam‐like structures through numerical simulations and experimental verifications. The unique advantages of the techniques are their simplicity, robustness to noise, ability to precisely identify small damage extents, and localize single and multiple damage states using limited measurable modes from few sensors.     

Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

A method to generate a suite of artificial near‐fault ground motion time histories for specified earthquakes is presented. A wavelet‐based nonstationary (WB‐NS) model has been employed to effectively capture the time‐varying frequency content of a particular acceleration record and continuous wavelet transform has been used to simulate the largest velocity pulse. Furthermore, an iterative procedure using discrete wavelet transform is utilized to modify an earthquake ground motion and generate energy‐compatible ground motion. Eventually, the artificial near‐fault accelerogram is achieved via the superposition of a coherent extracted velocity pulse with a random acceleration record corresponding to a WB‐NS model and multiplied by a time‐modulating envelope function. The effectiveness of the method is demonstrated by comparing the spectral response and Arias intensity curves of the simulated accelerograms with those of the real records.     

A Wavelet‐Based Adaptive Pole Assignment Method for Structural Control

This article presents a time varying wavelet‐based pole assignment (WPA) method to control seismic vibrations in multi‐degree of freedom (MDOF) structural systems. The discrete wavelet transform is used to determine the energy content over the frequency band of the response in real time. The frequency content was implemented in the Big Bang–Big Crunch algorithm to update the optimum values of the closed‐loop poles of the structural system adaptively. To calculate optimum gain matrix, a robust pole placement algorithm was used. The gain matrix is calculated online based on response characteristic in real time and must not be calculated a priori (offline) choice. The WPA is tested on a 10‐story structural system subject to several historical ground motions. It is observed that the WPA has advantages in some design problems. Numerical examples illustrate that the proposed approach reduces the displacement response of the structure in real time more than conventional linear quadratic regulator (LQR) controller.     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

Abstract: In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.     

Vision‐Based Natural Frequency Identification Using Laser Speckle Imaging and Parallel Computing

This study focuses on the identification of the natural frequencies of structures through the analysis of the speckle pattern that a laser creates and a camera records. The laser pointer spreads its light over a target area on the structure and creates the speckle pattern. The ambient vibrations affect the pattern and a camera records the changes. The stream of images is fed into a graphics processing unit (GPU). The developed parallel code includes different algorithms: the speckle contrast image (SCI), the speckle flow imaging (SFI), and an innovative application of k‐means clustering that uses the geometrical centroid of each cluster as virtual sensors. The tracking of the centroid in time domain through the images creates a vibration signal. The signals from different clusters are processed together to extract the natural frequencies of the structure. This study applies the proposed method to different sample structures both in laboratory and in the field to demonstrate how the obtained signals are reliable and easy to handle. The GPU technology enhances the performance of the entire method and allows the achievement of real‐time processing. All these features create an inexpensive, portable, and efficient tool to inspect any structure or its components.     

Developing a Smart Structure Using Integrated Subspace‐Based Damage Detection and Semi‐Active Control

Damage detection in large building structures has always faced challenges due to analyzing the large amount of measured data. In this article, a new damage detection approach based on subspace method is proposed to identify damages using limited output data. Also, a new scheme is presented to develop a smart structure by integrating structural health monitoring with semi‐active control strategy. If damage occurs in such a structure under severe excitations, the proposed scheme has the capability to exert necessary control forces in order to compensate for damage and reduce simultaneously the dynamic response of the structure. The reliability and feasibility of the proposed method are demonstrated by implementing the technique to two shear building structures with semi‐active control devices. Results show that the damage could be identified accurately with saving time and cost due to less computation even under noise existence; and dynamic response is significantly reduced in the smart structure.     

Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

A number of image processing techniques (IPTs) have been implemented for detecting civil infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real‐world situations (e.g., lighting and shadow changes) can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions (e.g., strong light spot, shadows, and very thin cracks). Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.     

基于精确算法的单跨曲线梁结构模态分析

从常曲率单跨曲线梁的动力学离散模型出发,推导出剪、弯、扭耦合的曲线梁刚度矩阵。结合曲线梁集中质量矩阵和多自由度系统运动方程的特征方程进行算法编写,计算出各阶固有频率及振型。通过对比所得到的刚度矩阵与有限元刚度矩阵各自的集成方法,直观展示了曲线梁结构刚度矩阵的分布形式,并论述了两种刚度矩阵的特征。并将刚度矩阵参与模态分析的计算结果与有限元数值仿真结果进行了对比分析,结果表明:前者逼近真解速度快于后者,虽然在中低阶时所求解的各阶固有频率吻合较好,但有限单元法在高阶转动振型求解精度方面存在一定误差。所编写的计算程序可为实际工程中精确求解曲线梁结构模态提供参考。     

Modal Identification for High‐Rise Building Structures Using Orthogonality of Filtered Response Vectors

The modal parameters of civil structures (natural frequency, mode shape, and mode damping ratio) are used for structural health monitoring (SHM), damage detection, and updating the finite element model. Long‐term measurement has been necessary to conduct operational modal analysis (OMA) under various loading conditions, requiring hundreds of thousands of discrete data points for estimating the modal parameters. This article proposes an efficient output‐only OMA technique in the form of filtered response vector (frv)‐based modal identification, which does not need complex signal processing and matrix operations such as singular value decomposition (SVD) and lower upper (LU) factorization, thus overcoming the main drawback of the existing OMA technique. The developed OMA technique also simplifies parameters such as window or averaging, which should be designed for signal processing by the OMA operator, under well‐separated frequencies and loading conditions excited by white noise. Using a simulation model and a 4‐story steel frame specimen, the accuracy and applicability were verified by comparing the dynamic properties obtained by the proposed technique and traditional frequency‐domain decomposition (FDD). In addition, the applicability and efficiency of the method were verified by applying the developed OMA to measured data, obtained through a field test on a 55‐story, 214‐m‐tall high‐rise building.     

型钢-混凝土组合梁的数值模态分析

以组合梁的模态和其在地震作用下的响应为研究目标,从理论和有限元分析两方面进行研究,互为参考;结合实例对混凝土滑移效应下刚度和动弹性模量以及组合梁的自振特性等有关问题进行了探讨,并将计算值与试验结果进行比较。结果表明,计算值与试验值吻合较好,可以为组合梁的相关设计提供参考。     

Modal Response‐Based Visual System Identification and Model Updating Methods for Building Structures

This article proposes a new system identification (SI) method using the modal responses obtained from the dynamic responses of a structure for estimating modal parameters. Since the proposed SI method visually extracts the mode shape of a structure through the plotting of modal responses based on measured data points, the complex calculation process for the correlation and the decomposition for vibration measurements required in SI methods can be avoided. Also, without dependence on configurations of SI methods inducing variations of modal parameters, mode shapes and modal damping ratios can be stably extracted through direct implementation of modal response. To verify the feasibility of the proposed method, the modal parameters of a shear frame were extracted from modal displacement data obtained from a vibration test, and the results were compared with those obtained from the existing frequency domain SI method. The proposed method introduces the maximum modal response ratio of each mode computed by modal displacement data, and from this, the contribution of each mode and each measured location to the overall structural response is indirectly evaluated. Moreover, this article proposes a model updating method establishing the error functions based on the differences between the analytical model and measurement for the natural frequencies and the modal responses reflecting both mode shape and modal contribution. The validity of the proposed method is verified through the response prediction and modal contributions of the models obtained from model updating based on dynamic displacement from a shaking table test for a shear‐type test frame.     

Kinect‐Based Pedestrian Detection for Crowded Scenes

Pedestrian movement data including volumes, walking speeds, and trajectories are essential in transportation engineering, planning, and research. Although traditional image‐based pedestrian detectors provide very rich information, their performance degrades quickly with increased occurrence of occlusion. The three‐dimensional sensing capabilities of Microsoft's Kinect present a potential cost‐effective solution for occlusion‐robust pedestrian detection. This article proposes an efficient pedestrian detection approach for crowded scenes by fusing RGB and depth images from the Kinect. More specifically, we first extract the pedestrian contour regions from RGB images using background subtraction. Then, we develop a region clustering algorithm to extract pedestrians from the contour regions using depth information. Finally, a tracking and counting algorithm is designed to acquire pedestrian volumes. The proposed approach was proven effective with an average detection accuracy of 93.1% at 20 frames per second. These results demonstrate the feasibility of using the low‐cost Kinect device for real‐world pedestrian detection in crowded scenes.     

基于应力波检测的管道模态分析

本文介绍了管材检测中应力波的主要模态,并利用有限元对管材在两端固支边界条件下的模态状况进行了数值模拟分析,为更深入的管材检测提供指导.     

Multi‐objective Optimization of Sensor and Excitation Layouts for Frequency Response Function‐Based Structural Damage Identification

Abstract: The accuracy of many damage identification methods depends significantly on the quality of measurements collected by sensors, such as accelerometers, concerning the response characteristics of a structure. Often the number of sensors used to collect measurements is limited due to available funds, equipment, and access. In addition, the excitation location can significantly affect a sensor's ability to collect quality measurement information. Therefore, both the location and number of sensors and the location of the excitation must be optimized to maximize the quality of information collected. A multi‐objective optimization approach is presented that minimizes the number of sensors specified while maximizing the sensitivity of the frequency response functions (FRFs) collected at each specified sensor location with respect to all possible damaged structural elements. The multiple Pareto‐optimal sensor/excitation layouts obtained aid in determining the number of sensors required to obtain an effective level of measurement information. The benefit of using Pareto‐optimal sensor/excitation layouts is investigated by using the optimized layouts to collect measurement information for a FRF‐based structural damage identification method. Trial results confirm that an increase in damage identification accuracy and efficiency is achieved when Pareto‐optimal sensor/excitation layouts are used instead of nonoptimal layouts. In addition, the Pareto‐optimal layouts improved damage identification accuracy in noisy measurement environments due to increasing the quality of measurements collected.     

Vision‐Based Automated Crack Detection for Bridge Inspection

Visual inspection of bridges is customarily used to identify and evaluate faults. However, current procedures followed by human inspectors demand long inspection times to examine large and difficult to access bridges. Also, highly relying on an inspector's subjective or empirical knowledge induces false evaluation. To address these limitations, a vision‐based visual inspection technique is proposed by automatically processing and analyzing a large volume of collected images. Images used in this technique are captured without controlling angles and positions of cameras and no need for preliminary calibration. As a pilot study, cracks near bolts on a steel structure are identified from images. Using images from many different angles and prior knowledge of the typical appearance and characteristics of this class of faults, the proposed technique can successfully detect cracks near bolts.     

Wavelet‐based approach of time series model for modal identification of a bridge with incomplete input

Identification of modal parameters of a bridge from its earthquake responses is crucial for performing damage assessment of the structure. However, all the input base excitations of the bridge may not be measured because of economic concerns and sensor malfunctions. Consequently, evaluating the modal parameters of a bridge under the consideration of incomplete input measurements is a challenging and important task. An approach that combines the continuous Cauchy wavelet transform with an autoregressive time‐varying moving average with exogenous input (AR‐TVMA‐X) model is proposed in this study to identify the modal parameters of a multispan bridge under multiple support earthquake excitations with incomplete measurements. The efficiency and efficacy of the proposed approach are first validated using numerically simulated responses of a three‐span continuous beam subjected to multiple support nonstationary excitations. A standard procedure of using the proposed approach to identify the modal parameters is established according to comprehensive studies on the effects of noise in the data, the number of supports whose excitations are used in the AR‐TVMA‐X model, and the orders of the AR‐TVMA‐X model on the accuracy of identifying the modal parameters. This procedure is further applied to process the earthquake responses of a two‐span cable‐stayed 510‐m‐long bridge to demonstrate the engineering applicability of the proposed approach.     

A texture‐Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces

Regular inspection of the components of nuclear power plants is important to improve their resilience. However, current inspection practices are time consuming, tedious, and subjective: they involve an operator manually locating cracks in metallic surfaces in the plant by watching videos. At the same time, prevalent automatic crack detection algorithms may not detect cracks in metallic surfaces because these are typically very small and have low contrast. Moreover, the existences of scratches, welds, and grind marks lead to a large number of false positives when state‐of‐the‐art vision‐based crack detection algorithms are used. In this study, a novel crack detection approach is proposed based on local binary patterns (LBP), support vector machine (SVM), and Bayesian decision theory. The proposed method aggregates the information obtained from different video frames to enhance the robustness and reliability of detection. The performance of the proposed approach is assessed by using several inspection videos. The results indicate that it is accurate and robust in cases where state‐of‐the‐art crack detection approaches fail. The experiments show that Bayesian data fusion improves the hit rate by 20% and the hit rate achieves 85% with only one false positive per frame.     

Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification

Abstract: This article presents a Beamlet transform‐based approach to automatically detect and classify pavement cracks in digital images. The proposed method uses a pavement distress image enhancement algorithm to correct the nonuniform background illumination by calculating the multiplicative factors that eliminate the background lighting variation. To extract linear features such as surface cracks from the pavement images, the image is partitioned into small windows and a Beamlet transform‐based algorithm is applied. The crack segments are then linked together and classified into four types: vertical, horizontal, transversal, and block. Simulation results show that the method is effective and robust in the extraction of cracks on a variety of pavement images.