报告题目：极端气候条件下的铁路轨道韧性研究
主讲人：Chayut Ngamkhanong
时间：2024年11月18日（星期一）10:00--12:00
地点：机械楼一层多功能厅
主办：研究生工作部
承办：土木建筑工程学院
面向对象：北京交通大学全体研究生
Asst. Prof. Chayut Ngamkhanong is the head of the Advanced Railway Infrastructure, Innovation, and Systems Engineering (ARIISE) Research Unit at Chulalongkorn University, Thailand. He is also the Deputy Head of Research and International Affairs at the Department of Civil Engineering, Chulalongkorn University. He received his Ph.D. from the School of Civil Engineering at the University of Birmingham, where he was awarded the UoB's Ratcliffe Prize for the best postgraduate research student in the science schools. His research focuses on a wide range of civil and railway engineering topics, including structural engineering, structural dynamics and vibrations, railway track engineering, railway infrastructure, construction materials etc. He has published more than 80 scientific papers in the fields of civil engineering and railway engineering.
Chayut Ngamkhanong副教授是泰国朱拉隆功大学先进铁路基础设施、创新和系统工程(ARIISE)研究单位的负责人，也是朱拉隆功大学土木工程系研究和国际事务的副主任。他在英国伯明翰大学土木工程学院获得了博士学位，并荣获伯明翰大学拉特克利夫奖（Ratcliffe Prize），以表彰该校理学院最优秀的研究生。Chayut副教授的研究涵盖广泛的土木及铁路工程领域，涉及结构工程、结构动力学与振动、铁路轨道工程、铁路基础设施和建筑材料等。在土木工程和铁路工程领域，已发表超过80篇科学论文。
此次讲座主要内容为热带气候地区条件下的铁路轨道韧性研究，将为参与者提供一个全面了解如何通过科技和创新应对铁路系统中气候变化带来的挑战，强调理论与实践的结合，以及持续研究和技术开发的重要性。
讲座内容包含讨论热带气候地区铁路基础设施面临的洪水和极端高温风险，分析这些气候风险如何影响轨道稳定性和增加脱轨的可能性；展示如何通过模拟和现场数据收集来评估铁路轨道的质量状态；介绍为应对洪水诱发的失稳和极端温度下的轨道变形所提供的策略；介绍如何利用机器学习模型预测高风险区域的轨道恶化情况。
With increasing climate variability, railway infrastructure in tropical climates faces heightened risks from flooding and extreme heat, which can destabilize tracks and increase the likelihood of derailments. This research bridges analytical modeling with real-world applications to develop resilience strategies specifically targeting flood-induced instability and track buckling under extreme temperature. Through a combination of simulations and field data collection, critical vulnerabilities in railway tracks are assessed, with findings directly linked to practical, field-based solutions. A machine learning-driven predictive maintenance model has been developed to forecast deterioration in high-risk areas, enabling proactive maintenance and risk mitigation. Analytical results are validated with real-world case studies to confirm the effectiveness of proposed solutions, including strengthening strategies and maintenance plans, under operational conditions. This approach equips track engineers with actionable insights to minimize accidental risks and enhance railway resilience against climate-induced disruptions, supporting the reliability and safety of rail networks in increasingly extreme environments.
发布人 ：张家硕
审 核：王勇
