• Computers and Concrete
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• 제20권5호
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Smart Structures and Systems
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• 제18권2호
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• pp.317-334
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• 2016

Structural Health Monitoring System (SHMS) works as an efficient platform for monitoring the health status and performance deterioration of engineering structures during long-term service periods. The objective of its installation is to provide reasonable suggestions for structural maintenance and management, and therefore ensure the structural safety based on the information extracted from the real-time measured data. In this paper, the SHMS implemented on a world-famous kilometer-level cable-stayed bridge, named as Sutong Cable-stayed Bridge (SCB), is introduced in detail. The composition and core functions of the SHMS on SCB are elaborately presented. The system consists of four main subsystems including sensory subsystem, data acquisition and transmission subsystem, data management and control subsystem and structural health evaluation subsystem. All of the four parts are decomposed to separately describe their own constitutions and connected to illustrate the systematic functions. Accordingly, the main techniques and strategies adopted in the SHMS establishment are presented and some extension researches based on structural health monitoring are discussed. The introduction of the SHMS on SCB is expected to provide references for the establishment of SHMSs on long-span bridges with similar features as well as the implementation of potential researches based on structural health monitoring.

• Smart Structures and Systems
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• 제5권4호
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• pp.381-395
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• 2009

The paper presents a case study in which the structural condition assessment of the East Bay bridge in Gibsonton, Florida is evaluated with the help of remote health monitoring techniques. The bridge is a four-span, continuous, deck-type reinforced concrete structure supported on prestressed pile bents, and is instrumented with smart Fiber Optic Sensors. The sensors used for remote health monitoring are the newly emerged Fabry-Perot (FP), and are both surface-mounted and embedded in the deck. The sensing system can be accessed remotely through fast Digital Subscriber Lines (DSL), which permits the evaluation of the bridge behavior under live traffic loads. The bridge was open to traffic since March 2005, and the collected structural data have been continuously analyzed since. The data revealed an increase in strain readings, which suggests a progression in damage. Recent visual observations also indicated the presence of longitudinal cracks along the bridge length. After the formation of these cracks, the sensors readings were analyzed and used to extrapolate the values of the maximum stresses at the crack location. The data obtained were also compared to initial design values of the bridge under factored gravity and live loads. The study showed that the proposed structural health monitoring technique proved to provide an efficient mean for condition assessment of bridge structures providing it is implemented and analyzed with care.

• Smart Structures and Systems
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• 제28권6호
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2006년도 추계학술대회 논문집
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• pp.336-340
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• 2006

The cases of using new methods of big blocks are largely increasing on Recent large-scale bridge structures. So the accurate data of responses of bridges following environmental causes are required to be quickly recorded in order to predict. For this reason described above, the research on measuring system should be conducted for more knowledge of the details on application and stability of new methods. In this study, the new health monitoring system that can monitor the real behavior and damages of the bridge during all processes of construction is presented by analyzing cases of domestic and overseas bridge health monitoring system, and applied methods of following bridges.

• Smart Structures and Systems
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• 제14권5호
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.439-459
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• 2010

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

• 비파괴검사학회지
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• 제27권6호
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• pp.556-562
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• 2007

This paper presents the use of a novel fiber optic accelerometer system to monitor ambient vibration (both wind-induced one and vehicle-induced) of a real bridge structure. This sensor system integrates the $Moir\'{e}$ fringe phenomenon with fiber optics to achieve accurate and reliable measurements. A low-cost signal processing unit implements unique algorithms to further enhance the resolution and increase the dynamic bandwidth of the sensors. The fiber optic accelerometer has two major benefits in using this fiber optic accelerometer system for monitoring civil engineering structures. One is its immunity to electromagnetic (EM) interference making it suitable for difficult applications in such environments involving strong EM fields, electrical spark-induced explosion risks, and cabling problems, prohibiting the use of conventional electromagnetic accelerometers. The other is its ability to measure both low- and high-amplitude vibrations with a constantly high resolution without pre-setting a gain level, as usually required in a conventional accelerometer. The second benefit makes the sensor system particularly useful for real-time measurement of both ambient vibration (that is often used for structural health monitoring) and strong motion such as earthquake. Especially, the semi-strong motion and the small ambient one are successfully simulated and measured by using the new fiber optic accelerometer in the experiment of the structural health monitoring of a real bridge.

• Smart Structures and Systems
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• 제26권5호
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• pp.667-680
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• 2020

This study aims at reporting a systematic procedure for evaluating the static and dynamic structural performance of steel bridges based on a short-period structural health monitoring measurement. Sungsu bridge located in Korea is considered as a case study presenting the most recent tests carried out to examine the bridge condition. Short-period measurements of Structural Health Monitoring (SHM) system were used during the bridge testing phase. A novel symmetry index is introduced using statistical analyses of deflection and strain measurements. Frequency Domain Decomposition (FDD) is implemented to the strain measurements to estimate the bridge mode shapes and damping ratios. Furthermore, Markov Chain Monte Carlo (MCMC) is also implemented to examine the reliability of bridge performance while ambient design trucks are in static or moving at different speeds. Strain, displacement and acceleration were measured at selected locations on the bridge. The results show that the symmetry index can be an efficient and useful measure in assessing the steel bridge performance. The results from the used method reveal that the performance of the Sungsu bridge is safe under operational conditions.

• Smart Structures and Systems
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• 제19권3호
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• pp.259-268
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• 2017

System identification of structures is one of the important aspects of structural health monitoring. The accuracy and efficiency of identification results is affected severely by measurement noises, especially when the structure system is large, such as bridge structures, and when online system identification is required. In this paper, the least square estimation (LSE) method is used combined with the substructure approach for identifying structural parameters of a cable-stay bridge with large degree of freedoms online. Numerical analysis is carried out by first dividing the bridge structure into smaller substructures and then estimates the parameters of each substructure online using LSE method. Simulation results demonstrate that the proposed approach is capable of identifying structural parameters, however, the accuracy and efficiency of identification results depend highly on the noise sensitivities of loading region, loading pattern as well as element size.