Machine learning and genetic algorithm-based framework for the life-cycle cost-based optimal design of self-centering building structures

Abstract

Self-centering systems have emerged as innovative lateral force-resisting mechanisms aimed at improving the earthquake resilience of building structures. The fully self-centering behavior effectively eliminates the residual deformation, thereby improving the post-earthquake repairability of building structures. However, this behavior does intensify floor acceleration responses that contribute to nonstructural damage and lead to increased initial costs. This research aims to develop a general machine learning and genetic algorithm-based framework for optimizing the design of self-centering building structures and to strike a balance between the initial costs and seismic losses by minimizing the overall life-cycle cost. The artificial neural network (ANN) based StructureNet model was developed for accelerating the calculation of life-cycle costs associated with self-centering structures. The genetic algorithm was adopted to determine the optimal design parameters for self-centering structures. The framework was applied to 8 design cases, with the analysis centering on two factors: the maximum allowable residual inter-story drift for controlling demolition and the initial construction cost of self-centering members. The study examined their influences on the optimization design results of self-centering building structures. The findings highlight a preference for a design approach that leans toward partially self-centering behavior as opposed to fully self-centering behavior, indicating the higher life-cycle benefits of partially self-centering behavior.