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In earthquake-prone zones, the seismic performance of reinforced concretecantilever (RCC) retaining walls is significant. In this study, the seismicperformance was investigated using horizontal and vertical pseudo-staticcoefficients. To tackle RCC weights and forces resulting from these earthpressures, 26 constraints for structural strengths and geotechnical stabilityalong with 12 geometric variables are associated with each design. Theseconstraints and design variables form a constraint optimization problem with atwelve-dimensional solution space. To conduct effective search and producesustainable, economical, lightweight RCC designs robust against earthquakehazards, a novel adaptive fuzzy-based metaheuristic algorithm is applied. Theproposed method divides the search space to sub-regions and establishesexploration, information sharing, and exploitation search capabilities based onits novel search components. Further, fuzzy inference systems were employed toaddress parameterization and computational cost evaluation issues. It was foundthat the proposed algorithm can achieve low-cost, low-weight, and low CO2emission RCC designs under nine seismic conditions in comparison with severalclassical and best-performing design optimizers.

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