A fuzzy adaptive metaheuristic algorithm for identifying sustainable, economical, lightweight, and earthquake-resistant reinforced concrete cantilever retaining walls
This addresses the need for sustainable and economical earthquake-resistant infrastructure in earthquake-prone zones, representing an incremental improvement in optimization methods for structural engineering.
The study tackled the design optimization of reinforced concrete cantilever retaining walls for earthquake resistance by applying a novel adaptive fuzzy-based metaheuristic algorithm, achieving low-cost, low-weight, and low CO2 emission designs under nine seismic conditions compared to existing optimizers.
In earthquake-prone zones, the seismic performance of reinforced concrete cantilever (RCC) retaining walls is significant. 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 twelve-dimensional solution space. To conduct effective search and produce sustainable, economical, lightweight RCC designs robust against earthquake hazards, a novel adaptive fuzzy-based metaheuristic algorithm is applied. The proposed method divides the search space to sub-regions and establishes exploration, information sharing, and exploitation search capabilities based on its novel search components. Further, fuzzy inference systems were 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 in comparison with several classical and best-performing design optimizers.