Machine learning for structural engineering: A state-of-the-art review

Machine learning (ML) has become the most successful branch of artificial intelligence (AI). It provides a unique opportunity to make structural engineering more predictable due to its ability in handling complex nonlinear structural systems under extreme actions. Currently, there is a boom in implementing ML in structural engi-neering, especially over the last five years thanks to recent advances in ML techniques and computational ca-pabilities as well as the availability of large datasets. This paper provides an ambitious and comprehensive review on the growing applications of ML algorithms for structural engineering. An overview of ML techniques for structural engineering is presented with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets. The review covers a wide range of structural engineering applications of ML including: (1) structural analysis and design, (2) structural health monitoring and damage detection, (3) fire resistance of structures; (4) resistance of structural members under various actions, and (5) mechanical properties and mix design of concrete. Both isolated members and whole systems made from steel, concrete and composite materials are explored. Findings from the reviewed literature, challenges and future commendations are highlighted and discussed. With available databases and ML codes provided, this review paper serves as a useful reference for structural engineering practitioners and researchers who are not familiar with ML but wish to enter this field of research.

Automated summary

This paper provides a comprehensive review on the applications of machine learning in structural engineering, focusing on basic concepts, tools, and datasets. The research covers various aspects of structural analysis, health monitoring, and fire resistance. The paper summarizes the findings and discusses challenges and future recommendations.