Forecasting Mechanical Properties of Steel Structures Through Dynamic Metaheuristic Optimization for Adaptive Machine Learning

Abstract

Machine learning (ML) presents a promising method for predicting mechanical properties in structural engineering, particularly within complex nonlinear structures under extreme conditions. Despite its potential, research has shown a disproportionate focus on concrete structures, leaving steel structures less explored. Furthermore, the prevalent combination of metaheuristic optimization (MO) and ML in existing studies is often subjective, pointing to a significant gap in identifying and leveraging more effective hybrid models. To bridge these gaps, this study introduces a novel system named the Multiple Metaheuristic Optimizers – Multiple Machine Learners (MMOMML) system, designed for predicting mechanical strength in steel structures. The MMOMML system amalgamates 17 MO algorithms with 15 ML techniques, generating 255 hybrid models, including numerous novel configurations not previously examined. With a user-friendly interface, MMOMML enables structural engineers to tackle inference challenges efficiently, regardless of their coding proficiency. This capability is convincingly demonstrated through two practical applications: steel beams’ shear strength and steel cellular beams’ elastic buckling. By offering a versatile and robust tool, the MMOMML system meets construction engineers’ and researchers’ practical and research needs, marking a significant advancement in the field.