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

TL;DR

This paper introduces a novel fuzzy adaptive metaheuristic algorithm to optimize reinforced concrete cantilever retaining walls for seismic resilience, sustainability, and cost-effectiveness, outperforming classical methods.

Contribution

The study presents a new fuzzy-based metaheuristic algorithm that effectively explores the complex design space for earthquake-resistant RCC walls, integrating fuzzy inference to enhance optimization.

Findings

Achieved low-cost, lightweight RCC designs with reduced CO2 emissions.

Outperformed classical and existing optimization methods in seismic conditions.

Demonstrated robustness and efficiency in complex constraint optimization.

Abstract

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.