Machine learning review of composite structural member performance assessment

Abstract

Performance estimation of composite structural members like slabs, beams and columns is crucial for structural integrity, durability and economy. However, conventional methods including testing, analytical formulations and finite element analysis are known to be inefficient, computation-intensive and limited in terms of capturing the highly non-linear behaviour of composite structures. Recently, machine learning (ML) has been considered as a fast and effective data-driven technique for modelling structural responses based on experimental, numerical and monitoring data. In this context, the current work presents a comprehensive review of the recent advancements in the application of ML techniques to composite structural members. Various methods used by researchers including shallow learning, deep learning and hybrid methods are summarised, and their capability in predicting the load-bearing capacity, deflection, fatigue response, cracking behaviour and fire resistance is investigated in detail. Contrary to other previous works that have reviewed either specific components or specific ML algorithms, this work provides a comparative evaluation of the existing ML-based methodologies in the context of composite slabs, beams and columns.