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CONTENTS
Volume 24, Number 5, November 2019
 

Abstract
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Key Words
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Address
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Abstract
In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

Key Words
structural health monitoring; deep learning; convolutional neural network; structural damage detection; structural condition assessment; artificial intelligence; machine learning; computer vision

Address
X.W. Ye, T. Jin and C.B. Yun: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China

Abstract
Deformation measurement of large scale structures, such as the ground beds of high-rise buildings, tunnels, bridge, and railways, are important for insuring service quality and safety. The pose-relay videometrics method and displacement-relay videometrics method have already presented to measure the pose of non-intervisible objects and vertical subsidence of unstable areas, respectively. Both methods combine the cameras and cooperative markers to form the camera series networks. Based on these two networks, we propose two novel videometrics methods with closed-loop camera series network for deformation measurement of large scale structures. The closed-loop camera series network offers \"closed-loop constraints\" for the camera series network: the deformation of the reference points observed by different measurement stations is identical. The closed-loop constraints improve the measurement accuracy using camera series network. Furthermore, multiple closed-loops and the flexible combination of camera series network are introduced to facilitate more complex deformation measurement tasks. Simulated results show that the closed-loop constraints can enhance the measurement accuracy of camera series network effectively.

Key Words
videometrics; deformation measurement; large scale structure; closed-loop; camera series network

Address
Qifeng Yu, Banglei Guan, Yang Shang and Zhang Li: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China;
Hunan Key Laboratory of Videometrics and Vision Navigation, Changsha, China
Xiaolin Liu: Hunan Key Laboratory of Videometrics and Vision Navigation, Changsha, China


Abstract
Damage detection based on dynamic characteristics of a structure is one of important roles in structural damage identification. It is difficult to detect local structural damage using traditional dynamic experimental methods due to a limited number of sensors used in an experiment. In this work, a non-contact test stand of fan blades is established, and a full-field noncontact test method, combined with three-dimensional digital image correlation, Bayesian operational modal analysis, and damage indices, is used to detect local damage of a fan blade under ambient excitation without use of baseline information before structural damage. The methodology is applied to detect invisible local damage on the fan blade. Such a method has a seemingly high potential as an alternative to detect local damage of blades with complex high-precision surfaces under extreme working conditions because it is a noncontact test method and can be used under ambient excitation without human participation.

Key Words
three-dimensional digital image correlation; Bayesian operational modal analysis; local damage detection

Address
Yujia Hu, Xi Sun and Haolin Li: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Weidong Zhu: Department of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA

Abstract
The video deflectometer based on digital image correlation is a non-contacting optical measurement method which has become a useful tool for characterization of the vertical deflections of large structures. In this study, a novel imaging model has been established which considers the variations of pitch angles in the full image. The new model allows deflection measurement at a wide working distance with high accuracy. A monocular video deflectometer has been accordingly developed with an inclination sensor, which facilitates dynamic determination of the orientations and rotation of the optical axis of the camera. This layout has advantages over the video deflectometers based on theodolites with respect to convenience. Experiments have been presented to show the accuracy of the new imaging model and the performance of the monocular video deflectometer in outdoor applications. Finally, this equipment has been applied to the measurement of the vertical deflection of Yingwuzhou Yangtze River Bridge in real time at a distance of hundreds of meters. The results show good agreement with the embedded GPS outputs.

Key Words
vertical deflection; monocular vision; digital image correlation; pitch angle

Address
Shuo Wang and Xiaodong Li: Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai, 200444, China
Shuiqiang Zhang: School of Engineering, Huzhou university, Huzhou 313000, China
Yu Zou: Wuhan Sinorock Technology Co. ,Ltd, Wuhan, 430074, China
Dongsheng Zhang: Shanghai Institute of Applied Mathematics and Mechanics,
School of Mechanics and Engineering Science, Shanghai University, Shanghai, 200444, China;
Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai 200072, China




Abstract
Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

Key Words
structural health monitoring; displacement; unit influence line; computer vision; structural identification

Address
Chuan-Zhi Dong and F. Necati Catbas: Department of Civil, Environmental, and Construction Engineering, University of Central Florida,
12800 Pegasus Drive, Suite 211, Orlando, Florida 32816, USA
Selcuk Bas: Department of Civil, Environmental, and Construction Engineering, University of Central Florida,
12800 Pegasus Drive, Suite 211, Orlando, Florida 32816, USA;
2Department of Civil Engineering, Bartin University, Bartin, Turkey



Abstract
Finite element analysis is one of the important methods to study the structural performance. Due to the simplification, discretization and error of structural parameters, numerical model errors always exist. Besides, structural characteristics may also change because of material aging, structural damage, etc., making the initial finite element model cannot simulate the operational response of the structure accurately. Based on Bayesian methods, the initial model can be updated to obtain a more accurate numerical model. This paper presents the work on the field test, modal identification and model updating of a Chinese reinforced concrete pagoda. Based on the ambient vibration test, the acceleration response of the structure under operational environment was collected. The first six translational modes of the structure were identified by the enhanced frequency domain decomposition method. The initial finite element model of the pagoda was established, and the elastic modulus of columns, beams and slabs were selected as model parameters to be updated. Assuming the error between the measured mode and the calculated one follows a Gaussian distribution, the posterior probability density function (PDF) of the parameter to be updated is obtained and the uncertainty is quantitatively evaluated based on the Bayesian statistical theory and the Metropolis-Hastings algorithm, and then the optimal values of model parameters can be obtained. The results show that the difference between the calculated frequency of the finite element model and the measured one is reduced, and the modal correlation of the mode shape is improved. The updated numerical model can be used to evaluate the safety of the structure as a benchmark model for structural health monitoring (SHM).

Key Words
structural modal identification; model updating; Bayesian method; Markov Chain Monte Carlo algorithm; structural health monitoring

Address
F.L. Zhang: Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics,
China Earthquake Administration, Harbin 150086, China;
School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China;
College of Civil Engineering, Tongji University, Shanghai 200092, China
Y.P. Yang, J.H. Yang and B.K. Han: College of Civil Engineering, Tongji University, Shanghai 200092, China
X.W. Ye: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China

Abstract
The authors investigate the feasibility of applying a vision-based displacement-measurement technique in the characterization of a SMA damper recently introduced in the literature. The experimental campaign tests a steel frame on a uni-axial shaking table driven by sinusoidal signals in the frequency range from 1Hz to 5Hz. Three different cameras are used to collect the images, namely an industrial camera and two commercial smartphones. The achieved results are compared. The camera showing the better performance is then used to test the same frame after its base isolation. U-shaped, shape-memory-alloy (SMA) elements are installed as dampers at the isolation level. The accelerations of the shaking table and those of the frame basement are measured by accelerometers. A system of markers is glued on these system components, as well as along the U-shaped elements serving as dampers. The different phases of the test are discussed, in the attempt to obtain as much possible information on the behavior of the SMA elements. Several tests were carried out until the thinner U-shaped element went to failure.

Key Words
base isolation; camera; shaking table test; shape memory alloy; vision-based technology

Address
F. Casciati and L. Faravelli: Dicar, University of Pavia, via Ferrata 3, 27100 Pavia, Italy
S. Casciati, A. Colnaghi and L. Rosadini: SIART srl, via dei Mille 73, 27100 Pavia, Italy
S. Zhu: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China

Abstract
Recently, an advanced video deflectometer based on the principle of off-axis digital image correlation was presented and advocated for remote and real-time deflection monitoring of large engineering structures. In engineering practice, measurement accuracy is one of the most important technical indicators of the video deflectometer. However, it has been observed in many outdoor experiments that data drift often presents in the measured deflection-time curves, which is caused by the instability of imaging system and the unavoidable influences of ambient interferences (e.g., ambient light changes, ambient temperature variations as well as ambient vibrations) in non-laboratory conditions. The non-ideal unstable imaging conditions seriously deteriorate the measurement accuracy of the video deflectometer. In this work, to perform high-accuracy deflection monitoring, potential sources for the drift error are analyzed, and a drift error model is established by considering these error sources. Based on this model, a simple, easy-to-implement yet effective reference point compensation method is proposed for real-time removal of the drift error in measured deflections. The practicality and effectiveness of the proposed method are demonstrated by in-situ deflection monitoring of railway and highway bridges.

Key Words
off-axis digital image correlation; video deflectometer; reference point compensation method

Address
Tian Long: School of Science, China University of Geosciences, Beijing 100083, China
Zhang Xiaohong and Pan Bing: Institute of Solid Mechanics, Beihang University, Beijing 100191, China

Abstract
In digital image correlation, speckle image is closely related to the measurement accuracy. A practical global evaluation criterion for speckle image is presented. Firstly, based on the essential factors of the texture image, both the average particle size and image sharpness are used for the assessment of speckle image. The former is calculated by a simplified auto-covariance function and Gaussian fitting, and the latter by focusing function. Secondly, the computation of the average particle size and image sharpness is verified by numerical simulation. The influence of these two evaluation parameters on mean deviation and standard deviation is discussed. Then, a physical model from speckle projection to image acquisition is established. The two evaluation parameters can be mapped to the physical devices, which demonstrate that the proposed evaluation method is reasonable. Finally, the engineering application of the evaluation method is pointed out.

Key Words
digital image correlation; speckle image; particle size; image sharpness; autocovariance; focusing evaluation function

Address
Boxing Qian, Jin Liang and Chunyuan Gong: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering,
Xi\'an Jiaotong University, Xi\'an 710049, China


Abstract
Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV\'s flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

Key Words
bridge inspection; unmanned aerial vehicle (UAV); imaging device; condition assessment; deep learning algorithm

Address
Hyung-Jo Jung, Jin-Hwan Lee, Sungsik Yoon and In-Ho Kim: Department of Civil and Environmental Engineering, Korean Advanced Institute for Science and Technology,
291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea



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