Yong Deng: Washington State University, USA
Qiao Dong: Southeast University, China
Submission Status: Open | Submission Deadline: Ongoing
Journal of Infrastructure Preservation and Resilience is calling for submissions to our Collection on Machine learning for infrastructure durability and resilience.
For infrastructure systems, durability refers to the ability to withstand anticipated service conditions including environmental stresses and traffic loads, while resilience relates to the capacity to recover from unexpected disturbances including natural disasters and disruptions caused by human activities. Durability and resilience are fundamental considerations throughout the stages of planning, design, construction, and management of infrastructure systems, with the goal of ensuring the sustained integrity, functionality, and operational performance.
Durability and resilience have significant importance to modern infrastructure systems, considering the variety of factors they influence including safety, economy, environment and society. Moreover, the escalating uncertainty and complexity in infrastructure systems and their operational environment pose additional challenges in conducting comprehensive analysis and achieving effective management of durability and resilience.
Machine learning has demonstrated exceptional capabilities in data analysis, system identification, and optimized decision-making processes, highlighting its substantial potential as a valuable tool for analyzing and enhancing infrastructure durability and resilience throughout the entire life cycle. It is capable of making infrastructure more sustainable and safer, in a smart manner. The purpose of this collection is to present the latest advancements in machine learning techniques that contribute to the evaluation, prediction, and improvement of durability and resilience in diverse infrastructure domains, including but not limited to bridges, buildings, roads, dams, and utility networks.