A fuzzy adaptive metaheuristic algorithm for identifying sustainable, economical, and earthquake-resistant reinforced concrete cantilever retaining walls

In earthquake-prone zones, the seismic performance of reinforced concrete cantilever (RCC) retaining walls is a critical factor. In this study, the seismic performance was investigated using horizontal and vertical pseudo-static coefficients. To tackle RCC weights and forces resulting from these earth pressures, 26 constraints for structural strengths and geotechnical stability along with 12 geometric variables are associated with each design. These constraints and design variables form a constraint optimization problem with a 12-dimensional solution space. To conduct effective search and produce sustainable and economical RCC designs that are robust against earthquake hazards, a novel adaptive fuzzy-based metaheuristic algorithm is proposed. The proposed method divides the search space into sub-regions and establishes exploration, information sharing, and exploitation search capabilities based on its novel search components. Further, fuzzy inference systems are employed to address parameterization and computational cost evaluation issues. It was found that the proposed algorithm can achieve low-cost, low-weight, and low-CO2 emission RCC designs under nine seismic conditions when compared with several classical and best-performing design optimizers.