Plasma Generation Optimization for Optimal Design of Reinforced Concrete Cantilever Retaining Wall Structures

Optimal design of cantilever retaining wall structures is among the most commonly geotechnical structures supporting vertical or near-vertical slopes of soil. These structures are used in many locations such as highways, bridges, and railway infrastructures. This study aims to employ the recently developed population-based metaheuristic algorithm which is called Plasma Generation Optimization (PGO) for the optimal design of reinforced concrete cantilever retaining wall structures. The design is based on the ACI 318–19 requirements, and optimizing construction cost is considered as the goal of the optimization problem. Two well-known theories including the Rankin and Coulomb theories are used in order to estimate the lateral earth pressure for the static loading condition. However, Mononobe–Okabe theory is utilized to determine this pressure for seismic loading condition. To investigate the efficiency of the PGO, two practical examples consisting of the cantilever retaining wall with shear key and cantilever retaining wall with sloped base are optimally designed based on the above-mentioned theories. Comparing the results acquired by the PGO with those of two well-known metaheuristics, namely Cuckoo Search and teaching–learning-based optimization, indicates the efficiency of PGO for the reinforced concrete cantilever retaining wall structures.