报告题目： Reliability-based integrity assessments of pipelines under the threats of corrosion and third-party damage

报告人：周文星 教授

时间：2024年6月2日周日 上午9：30

地点：土木楼201

个人简介

周文星(Wenxing Zhou)，加拿大西安大略大学University of Western Ontario 土木与环境工程系，教授、博士生导师。Dr. Zhou’s main research interests are the reliability- and risk-based evaluation of structures and infrastructure systems with a particular focus on buried steel pipelines. He has published over 100 peer-reviewed journal papers and over 50 conference papers, and been one of World’s Top 2% Scientists (based on career publication data) according to Stanford University’s annual ranking since 2021. Dr. Zhou contributed to the calibration of load factors adopted in the National Building Code of Canada (NBCC) and has served as the Vice Chair of the Technical Committee for the Canadian Standards Association (CSA) Standard CSA Z260: “Pipeline system safety metrics” since 2017. Dr. Zhou received his BEng, MEng, PhD from Tongji University, Tsinghua University and UWO, respectively。

报告简介

Buried steel pipelines are the most efficient means to transport large quantity of hydrocarbons over long distances and part of the critical infrastructure systems in a modern society. Pipeline failures, although rare, can have serious consequences in terms of safety, environmental impact and economic loss. Common threats to the structural integrity of pipelines include corrosion, thirdparty damage, stress corrosion cracking, and natural forces such as landslides and earthquakes. This presentation will discuss probabilistic approaches developed at the University of Western Ontario, Canada to facilitate reliability- and risk-based structural integrity assessments of energy pipelines with respect to corrosion and third-party damage. Specifically, the presentation will discuss probabilistic corrosion growth models, random field-based analysis and synthesis of corrosion clusters, the Bayesian network model for quantifying the hit probability by third-party excavation activities and the development of the fitness-for-service assessment model using the Gaussian process regression.