Bridge Optimization - Inspection and Condition Monitoring
Yunlai ZhouMagd Abdel WahabLinya LiuMitsuru UesakaYuki MitsuyaKatsuhiro DobashiJoichi KusanoEiji YoshidaYoshinobu OshimaMasahiro IshidaTimothy SaadChung FuGengwen ZhaoChaoran Xu
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This is a collection of several applications for condition monitoring and damage identification in bridge structures. Bridge structural condition monitoring is essential since it can provide early warning of potential defects in bridges, which may induce catastrophic accidents and result in huge economic loss. Such bridge condition monitoring relies on sensing techniques, especially advanced sensing techniques that can provide detailed information on bridge structures. Additionally, postprocessing systems can interpret the captured data and warn of any potential faults. This book will give students a thorough understanding of bridge condition monitoring.Keywords:
Bridge (graph theory)
Identification
Condition Monitoring
Structural Health Monitoring
Bridge information modeling (BrIM) has widely become an efficient tool in the bridge engineering and construction industry. It has been used in pre-fabrication, obtaining accurate quantity surveys, and creating accurate shop drawings. This article presents the utilization of bridge information modeling (BrIM) in determining the optimum construction methods of concrete bridges in bridge projects in Egypt using systematic procedures taking into account: bridge physical properties, construction cost, and site conditions. Bridge information modeling (BrIM) has proven to be an effective tool in determining the optimum construction methods of concrete bridges. The proposed BrIM approach is capable of obtaining feasible construction methods and associated construction costs based on bridge physical characteristics.
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Structural Health Monitoring (SHM) systems have been installed on bridges across the world at an increasing rate in recent years, providing vital data for bridge assessment and maintenance. Machine Learning (ML) is efficient in data analyses such as classification and regression, and capable of improving its accuracy by learning from data without the need for step-to-step programming. The implementation of ML methods in bridge SHM studies has become more popular in recent years for its ability to detect damages on concrete and steel caused by material deterioration and to perform condition assessment on bridge structures. There have been several review articles discussing ML applications in SHM which mostly provide broad discussions across different civil engineering structures. In this article, different ML applications in the bridge SHM study are summarised and discussed. Detailed critiques of each types of ML applications are provided. Finally, recommendations are made for the future study of ML applications in bridge SHM to fill the current research gaps.
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Abstract Along with the advancement in sensing and communication technologies, the explosion in the measurement data collected by structural health monitoring (SHM) systems installed in bridges brings both opportunities and challenges to the engineering community for the SHM of bridges. Deep learning (DL), based on deep neural networks and equipped with high-end computer resources, provides a promising way of using big measurement data to address the problem and has made remarkable successes in recent years. This paper focuses on the review of the recent application of DL in SHM, particularly damage detection, and provides readers with an overall understanding of the missions faced by the SHM of the bridges. The general studies of DL in vibration-based SHM and vision-based SHM are respectively reviewed first. The applications of DL to some real bridges are then commented. A summary of limitations and prospects in the DL application for bridge health monitoring is finally given.
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The article proposes a method to develop bridge deterioration model in bridge manage ment systemby Markov Process or Semi- Markov Process, and programfor themas well. We can predict bridge future con- dition through the bridge deterioration model. Its result can help the bridge manager to make decisions, and also can be the foundations for bridge decision optimization. When bridge condition data are not enough, its procedure can be an instruction for the data collection ofbridge management system.
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