Methodology for the damage detection of aging bridges based on multi-data and deep learning

摘要：

　　The number of bridges over 30 years old is rapidly increasing worldwide.As a result,the maintenance management and performance evaluation of bridges are of great interest to society and structural health monitoring(SHM)systems are attracting significant attention as a technology to facilitate these actions.SHM systems are a method of collecting and analyzing measurement data from sensors and monitoring structural responses in real time.Most SHM systems collect various types of response data,but only use limited types of data for analysis.Therefore,in this paper,we propose a method combining a deep learning model for bridge deterioration estimation and a damage localization model using multi-data.Both models use a common convolutional neural network,but the purpose of each model is different based on its learning method.We expect that the proposed method will be widely used in the analysis of real multi-data from aging bridges and lead to the automation of maintenance and performance evaluation.

关键词： Multi-Data Machine Learning Damage Detection Aging Bridge SHM

作者: Kang Hyeok.Lee Joo Hwan.Park Min Woong.Jung Do Hyoung.Shin

作者单位: Department of Civil Engineering,Inha University,Incheon,Republic of Korea Corresponding Author,Department of Civil Engineering,Inha University,Incheon,Republic of Korea

会议类型: 国际会议

会议名称: The 7th World Conference on Structural Control and Monitoring(7WCSCM)(第七届结构控制与监测世界大会)

会议地点: 青岛

会议语种:英文

页码: 1725-1731

在线出版日期: 2018-07-22（万方平台首次上网日期，不代表论文的发表时间）