关于举办美国关岛大学孔祥雄博士学术讲座的通知
发布时间: 2019-07-04

  目:Monitoring Fatigue Cracks in Steel Bridges using Advanced Structural Health Monitoring Technologies

  间:2019799301100

  点: 交通大楼604会议室

报告人:孔祥雄博士(关岛大学)

欢迎广大师生参加

                         土木与交通学院

                          2019年7月4日



报告人简介:

孔祥雄博士于2006年毕业于浙江大学土木工程系,2009年于中国建筑科学研究院获结构工程硕士学位。毕业后在中国建筑科学研究院工作五年,主要从事建筑结构地震监测、设计研究工作,20187月于美国堪萨斯大学获土木工程博士学位,目前任美国关岛大学土木工程系助理教授。

孔祥雄博士的研究工作主要集中于结构健康监测、结构地震工程及计算机视觉在土木工程领域的应用等,目前其作为共同作者已发表国际期刊及会议论文近20余篇,其近期发表的论文“Vision-based fatigue crack detection of steel structures using video feature tracking”被《Computer-Aided Civil and Infrastructure Engineering(IF 5.475)期刊评选为高下载期刊论文。

报告摘要:

Fatigue cracks that develop in steel highway bridges under repetitive traffic loads are one of the major mechanisms that degrade structural integrity. If bridges are not appropriately inspected and maintained, fatigue cracks can eventually lead to catastrophic failures, in particular for fracture-critical bridges. Despite various levels of success of crack monitoring methods over the past decades in the fields of structural heath monitoring (SHM) and non-destructive evaluation (NDE), monitoring fatigue cracks in steel bridges is still challenging due to the complex structural joint layout and unpredictable crack propagation paths. In this presentation, advanced SHM technologies are proposed for detecting and monitoring fatigue cracks in steel bridges. These technologies are categorized as: 1) a large-area strain sensing technology based on soft elastomeric capacitor (SEC) sensors; and 2) a non-contact computer vision-based fatigue crack detection approach. In SEC-based fatigue crack sensing, the research focuses are placed on numerical prediction of the SEC’s response under fatigue cracking and experimental validations of sensing algorithms for monitoring fatigue cracks over long-term. In vision-based fatigue crack detection approach, a novel sensing methodology is established through video feature tracking. Laboratory test results verified that the proposed approaches can robustly identify the true fatigue crack from many non-crack edges. Overall, the proposed advanced SHM technologies show great promise for fatigue crack damage detection of steel bridges in laboratory configurations, hence form the basis for long-term fatigue sensing solutions in field applications.