Optimum design of combined footings using swarm intelligence-based algorithms

Abstract

This study considers the optimum design of reinforced concrete combined footings. Analysis and design procedures are developed based on the American Concrete Institute (ACI 318-05) regulations and several geotechnical considerations for providing stability to the structure. Low-cost designs are generated using five swarm intelligence algorithms [i.e., particle swarm optimization (PSO), accelerated particle swarm optimization (APSO), whale optimization algorithm (WOA), ant lion optimizer (ALO), and moth flame optimization (MFO)]. The performance of these algorithms is examined through two numerical simulations. First, a combined footing design from the literature is optimized using the five swarm intelligence algorithms. The impacts of concrete compressive strength (fc) on the final cost are studied through a sensitivity analysis. Second, rectangular and trapezoidal combined footings are optimized using the best algorithm in the first example (i.e., PSO) to explore the effect of shape on the final design. Also, a sensitivity analysis is performed on the effects of the design parameters on the cost.