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CONTENTS
Volume 24, Number 6, December 2019
 

Abstract
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Key Words
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Address
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Abstract
Traditional damage detection methods for nonlinear structures are often based on simplified models, such as the mass-spring-damper and shear-building models, which are insufficient for predicting the vibration responses of a real structure. Conventional global nonlinear finite element model updating methods are computationally intensive and time consuming. Thus, they cannot be applied to practical structures. A decentralized approach for identifying the nonlinear material parameters is proposed in this study. With this technique, a structure is divided into several small zones on the basis of its structural configuration. The unknown material parameters and measured vibration responses are then divided into several subsets accordingly. The structural parameters of each subset are then updated using the vibration responses of the subset with the Newton-successive-over-relaxation (SOR) method. A reinforced concrete and steel frame structure subjected to earthquake loading is used to verify the effectiveness and accuracy of the proposed method. The parameters in the material constitutive model, such as compressive strength, initial tangent stiffness and yielding stress, are identified accurately and efficiently compared with the global nonlinear model updating approach.

Key Words
nonlinear finite element method; model updating; system identification; decentralized approach

Address
P.H. Ni: Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education,
Beijing University of Technology, Beijing 100124, China
X.W. Ye: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China


Abstract
This paper proposes an integrated safety assessment method that can take multiple sources data into consideration based on a data fusion approach. Data cleaning using the Kalman filter method (KF) was conducted first for monitoring data from each sensor. The inclination data from the four tilt sensors of the same monitoring section have been associated to synchronize in time. Secondly, the finite element method (FEM) model was established to physically correlate the external forces with various structural responses of the shield tunnel, including the measured inclination. Response surface method (RSM) was adopted to express the relationship between external forces and the structural responses. Then, the external forces were updated based on the in situ monitoring data from tilt sensors using the extended Kalman filter method (EKF). Finally, mechanics parameters of the tunnel lining were estimated based on the updated data to make an integrated safety assessment. An application example of the proposed method was presented for an urban tunnel during a nearby deep excavation with multiple source monitoring plans. The change of tunnel convergence, bolt stress and segment internal forces can also be calculated based on the real time deformation monitoring of the shield tunnel. The proposed method was verified by predicting the data using the other three sensors in the same section. The correlation among different monitoring data has been discussed before the conclusion was drawn.

Key Words
shield tunnel; data fusion; extended Kalman filter; safety assessment

Address
Hongwei Huang, Xin Xie and Dongming Zhang: Department of Geotechnical Engineering, Tongji University, 1239 Siping Rd, Shanghai 200092, China
Zhongqiang Liu and Suzanne Lacasse: Natural Hazards, Norwegian Geotechnical Institute (NGI), 3930 Ullevaal St., NO-0806 Oslo, Norway

Abstract
This paper investigates the framework of vision-based dense displacement and strain measurement of miter gates with the approach for the quantitative evaluation of the expected performance. The proposed framework consists of the following steps: (i) Estimation of 3D displacement and strain from images before and after deformation (water-fill event), (ii) evaluation of the expected performance of the measurement, and (iii) selection of measurement setting with the highest expected accuracy. The framework first estimates the full-field optical flow between the images before and after water-fill event, and project the flow to the finite element (FE) model to estimate the 3D displacement and strain. Then, the expected displacement/strain estimation accuracy is evaluated at each node/element of the FE model. Finally, methods and measurement settings with the highest expected accuracy are selected to achieve the best results from the field measurement. A physics-based graphics model (PBGM) of miter gates of the Greenup Lock and Dam with the updated texturing step is used to simulate the vision-based measurements in a photo-realistic environment and evaluate the expected performance of different measurement plans (camera properties, camera placement, post-processing algorithms). The framework investigated in this paper can be used to analyze and optimize the performance of the measurement with different camera placement and post-processing steps prior to the field test.

Key Words
Miter gate; vision-based structural health monitoring; displacement and strain measurement; physics-based graphics model; optical flow, finite element analysis; graphics modeling

Address
Yasutaka Narazaki, Vedhus Hoskere, Brian A. Eick, and Billie F. Spencer: Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign,
205 N Mathews Ave, Urbana, IL 61801, USA
Matthew D. Smith: Army Engineer Research and Development Center, 3909 Halls Ferry Rd., Vicksburg, Mississippi, 39180, USA


Abstract
Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

Key Words
computer vision; multicamera; deep learning; structural health monitoring

Address
Darragh Lydon, S.E. Taylor, Myra Lydon and David Hester: School of Natural and Built Environment, Queen

Abstract
Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

Key Words
structural health monitoring; wind speed prediction; machine learning; optimization algorithm; finite mixture method

Address
X.W. Ye, Y. Ding and H.P. Wan: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China

Abstract
Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

Key Words
pothole detection; deep learning; faster-RCNN; road condition monitoring

Address
Longxi Luo and Jianping Wu: Department of Civil Engineering, Tsinghua University, Beijing, China;
Jiangsu Province Collaborative Innovation Center of Modern Urban, Traffic Technologies, Southeast University Road #2, Nanjing, China
Maria Q. Feng and Ryan Y. Leung: Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, USA

Abstract
In nuclear fusion reactors, the key structural component (i.e., the plasma-facing component) undergoes high heat flux cyclic loading. To ensure the safety of fusion reactors, an experimental study on the temperature-induced creep of stainless steel under heat flux cyclic loading was performed in the present work. The strains were measured using a stereo digital image correlation technique (3D-DIC). The influence of the heat haze was eliminated, owing to the use of a vacuum environment. The specimen underwent heat flux cycles (500 C-1000 C) with different mechanical preloads (0 kN, 10 kN, 30 kN, and 50 kN). The results revealed that, for a relatively large preload (for example, 50 kN), a single temperature cycle can induce a residual strain of up to 15000.

Key Words
digital image correlation; nuclear fusion; high temperature measurement; creep; vacuum chamber

Address
Yong Su: CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics,
University of Science and Technology of China, Hefei 230027, China;
IAT-Chungu Joint Laboratory for Additive Manufacturing,
Anhui Chungu 3D Printing Institute of Intelligent Equipment and Industrial Technology, Wuhu 241200, China
Zhiwei Pan, Yongpei Peng, Shenghong Huang and Qingchuan Zhang: CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics,
University of Science and Technology of China, Hefei 230027, China



Abstract
Dynamic displacement response of civil structures is an important index for in-construction and in-service structural condition assessment. However, accurately measuring the displacement of large-scale civil structures such as high-rise buildings still remains as a challenging task. In order to cope with this problem, a vision-based system with the use of industrial digital camera and image processing has been developed for long-distance, remote, and real-time monitoring of dynamic displacement of supertall structures. Instead of acquiring image signals, the proposed system traces only the coordinates of the target points, therefore enabling real-time monitoring and display of displacement responses in a relatively high sampling rate. This study addresses the in-situ experimental verification of the developed vision-based system on the Canton Tower of 600 m high. To facilitate the verification, a GPS system is used to calibrate/verify the structural displacement responses measured by the vision-based system. Meanwhile, an accelerometer deployed in the vicinity of the target point also provides frequency-domain information for comparison. Special attention has been given on understanding the influence of the surrounding light on the monitoring results. For this purpose, the experimental tests are conducted in daytime and nighttime through placing the vision-based system outside the tower (in a brilliant environment) and inside the tower (in a dark environment), respectively. The results indicate that the displacement response time histories monitored by the vision-based system not only match well with those acquired by the GPS receiver, but also have higher fidelity and are less noise-corrupted. In addition, the low-order modal frequencies of the building identified with use of the data obtained from the vision-based system are all in good agreement with those obtained from the accelerometer, the GPS receiver and an elaborate finite element model. Especially, the vision-based system placed at the bottom of the enclosed elevator shaft offers better monitoring data compared with the system placed outside the tower. Based on a wavelet filtering technique, the displacement response time histories obtained by the vision-based system are easily decomposed into two parts: a quasi-static ingredient primarily resulting from temperature variation and a dynamic component mainly caused by fluctuating wind load.

Key Words
structural health monitoring; supertall structure; displacement measurement; vision-based system; real-time; long-distance; remote sensing

Address
Yi-Qing Ni and You-Wu Wang: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University,
Hung Hom, Kowloon, Hong Kong;
Hong Kong Branch of Chinese National Rail Transit Electrification and Automation
Engineering Technology Research Center, Hong Kong
Wei-Yang Liao: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University,
Hung Hom, Kowloon, Hong Kong
Wei-Huan Chen: Department of Applied Mechanics and Engineering, Sun Yat-sen University, Guangzhou 510275, China


Abstract
Prefabricated reinforced-concrete shear walls are used extensively in building structures because they are convenient to construct and environmentally sustainable. To make large walls easier to transport, they are divided into smaller segments and then assembled at the construction site using a variety of connection methods. The present paper proposes a precast shear wall assembled using steel shear keys, wherein the shear keys are fixed on the embedded steel plates of adjacent wall segments by combined plug and fillet welding. The anchoring strength of shear keys is known to affect the mechanical properties of the wall segments. Loading tests were therefore performed to observe the behavior of precast shear wall specimens with different anchoring components for shear keys. The specimen with insufficient strength of anchoring components was found to have reduced stiffness and lateral resistance. Conversely, an extremely high anchoring strength led to a short-column effect at the base of the wall segments and low deformation ability. Finally, for practical engineering purposes, a design approach involving the safety coefficient of anchoring components for steel shear keys is suggested.

Key Words
prefabricated shear wall; quasi-static test; embedded components; anchoring strength

Address
Shao-Dong Shen and Run-Hua Gong: Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Peng Pan: Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Tsinghua University, Beijing 100084, China
Wen-Feng Li and Qi-Song Miao: Complex Structural Design Division, Beijing Institute of Architectural Design, Beijing 100045, China

Abstract
The centrifuge model test is usually used for two-dimensional deformation and instability study of the soil slopes. As a typical loose slope, the municipal solid waste (MSW) landfill is easy to slide with large deformation, under high water levels or large earthquakes. A series of centrifuge model tests of landfill slide induced by rising water level and earthquake were carried out. The particle image velocimetry (PIV), laser displacement transducer (LDT) and marker tracer (MT) methods were used to measure the deformation of the landfill under different centrifugal accelerations, water levels and earthquake magnitudes. The PIV method realized the observation of continuous deformation of the landfill model, and its results were consistent with those by LDT, which had higher precision than the MT method. The deformation of the landfill was mainly vertically downward and increased linearly with the rising centrifugal acceleration. When the water level rose, the horizontal deformation of the landfill developed gradually due to the seepage, and a global slide surface formed when the critical water level was reached. The seismic deformation of the landfill was mainly vertical at a low water level, but significant horizontal deformation occurred under a high water level. The results of the tests and analyses verified the applicability of PIV in the two-dimensional deformation measurement in the centrifuge model tests of the MSW landfill, and provide an important basis for revealing the instability mechanism of landfills under extreme hydraulic and seismic conditions.

Key Words
particle image velocimetry; deformation measurement; landfill; centrifuge model test

Address
J.C. Li ,2a, B. Zhu and Y.M. Chen: Center for Hypergravity Experimental and Interdisciplinary Research, Hangzhou 310058, China;
MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering , Hangzhou 310058, China
X.W. Ye: MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering , Hangzhou 310058, China;
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
T.W. Liu: MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering , Hangzhou 310058, China


Abstract
Triplex composite is an epoxy-bonded joint structure, which constitutes the secondary barrier in a liquefied natural gas (LNG) carrier. Defects in the triplex composite weaken its shear strength and may cause leakage of the LNG, thus compromising the structural integrity of the LNG carrier. This paper proposes an autonomous triplex composite inspection (ATCI) system for visualizing and classifying hidden defects in the triplex composite installed inside an LNG carrier. First, heat energy is generated on the surface of the triplex composite using halogen lamps, and the corresponding heat response is measured by an infrared (IR) camera. Next, the region of interest (ROI) is traced and noise components are removed to minimize false indications of defects. After a defect is identified, it is classified as internal void or uncured adhesive and its size and shape are quantified and visualized, respectively. The proposed ATCI system allows the fully automated and contactless detection, classification, and quantification of hidden defects inside the triplex composite. The effectiveness of the proposed ATCI system is validated using the data obtained from actual triplex composite installed in an LNG carrier membrane system.

Key Words
liquefied natural gas (LNG) carrier; active thermography; defect classification and quantification; triplex composites; internal void; uncured adhesive; image processing

Address
Soonkyu Hwang, Ikgeun Jeon, Gayoung Han and Hoon Sohn: Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
Wonjun Yun: Advanced Research Center, Korea Shipbuilding and Offshore Engineering Co. Ltd., Seoul 03058, South Korea




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