Predictive Modeling of Structural Performance Using Machine Learning: A Comprehensive Review

Abstract

Predicting structural performance is a critical aspect of civil engineering, ensuring the safety, efficiency, and durability of buildings and infrastructure. Traditional methods, such as finite element analysis and empirical modeling, often fall short in addressing the complexities of modern structural systems. The advent of machine learning (ML) has revolutionized this domain by offering data-driven approaches capable of handling non-linear relationships and large datasets, enhancing the accuracy and efficiency of structural performance predictions. This review paper examines the applications of ML techniques, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Decision Tree Regression (DTR), and hybrid models, in predicting structural metrics such as load-bearing capacity, deflection, durability, and seismic performance. The paper synthesizes findings from recent studies, highlighting key achievements and challenges, such as limited real-world validation, the need for hybrid approaches, and barriers to integrating ML into engineering workflows. By identifying critical research gaps and proposing future directions, this review aims to provide a comprehensive framework for advancing ML applications in structural engineering. The findings emphasize the transformative potential of ML to optimize design processes, enhance safety, and promote sustainable practices in civil engineering projects.