【特刊征稿】

Physics-Informed Machine Learning for Structural Safety, Reliability, and Lifecycle Management

（面向结构安全、可靠性与全生命周期管理的物理信息机器学习）

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Prof. Dr. Qiang Zhang

Email: 2t@gxu.edu.cn

Affiliation: School of Civil Engineering and Architecture, Guangxi University, Nanning, China

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Assist. Prof. Luchuan DING （丁陆川）

Email: luchuanding@tongji.edu.cn

Affiliation: Department of Structural Engineering, Tongji University, Shanghai, China

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Prof. Dr. Lei Xu

Email: leix_2013@163.com

Affiliation: School of Civil Engineering, Central South University, Changsha, China

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Dr. Wenliu Xu

Email: xuwenliu92@qq.com

Affiliation: School of Urban Construction, Jiangxi Normal University, Nanchang, China

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Dr. Xiangwei Li

Email: lxwlxw123666@163.com

Affiliation: Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, China

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网址：https://www.techscience.com/sdhm/special_detail/structural-safety

Summary

Modern engineering structures face escalating demands across structural safety, reliability, lifecycle management and economic viability, rendering traditional methodologies increasingly insufficient. In this evolving landscape, advanced machine learning techniques offer opportunities to reshape structural engineering—enabling smarter, highly adaptive, and sustainable design, safety assessment, reliability-based optimization, lifecycle management, and decision-making.Contributions may cover theoretical developments, AI-driven computational models, material modelling and multi-scale simulations, disaster prevention for new energy infrastructure, structural strengthening and reinforcement technologies, and lifecycle smart maintenance strategies.By conducting high-quality research, the safety and resilience of infrastructure and urban communities will be enhanced, and lifecycle costs will also be reduced. Researchers are invited to submit work that addresses challenges and opportunities in this rapidly evolving field.The themes of this special issue include but not limited to:-Physics-informed machine learning methods for structural safety analysis-AI-driven computation models for structural reliability-Material modelling and multi-scale simulations-Disaster prevention for new energy infrastructure-Advanced numerical simulation of dynamic behavior of railway infrastructure-Building resilience and sustainability-Structural strengthening and reinforcement technologies-Lifecycle smart maintenance strategies

Keywords

AI, structural safety, reliability, lifecycle management

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