A Two-Stage Genetic Algorithm for Beam–Slab Structure Optimization

Abstract

Beam–slab structures account for 50–65% of a building’s total dead load and contribute to 20% of the overall cost and CO2 emissions. Despite their importance, conventional beam–slab structural optimization methods often lack search efficiency and accuracy, making them less effective for practical engineering applications. Such limitations arise from the optimization problem involving a complex solution space, particularly when considering components’ arrangement, dimensions, and load transfer paths simultaneously. To address the research gap, this study proposes a novel two-stage genetic algorithm, optimizing beam–slab layout in the first stage and component topological relationships and dimensions in the second stage. Numerical experiments on the prototype case indicate that the algorithm can generate results that meet engineering accuracy requirements within 100 iterations, outperforming comparable algorithms in both efficiency and accuracy. Additionally, this heuristic approach stands out for its independence from prior dataset training and its minimal parameter adjustment requirement, making it highly accessible to engineers without programming expertise. Statistical analysis of the algorithm’s optimization process and case studies demonstrate its robustness and adaptability to various beam–slab structural optimization problems, revealing its significant potential for practical engineering scenarios.