Optimum design of reinforced earth walls using evolutionary optimization algorithms

Kashani, Ali R.; Saneirad, Ali; Gandomi, Amir H.

doi:10.1007/s00521-019-04264-z

Cited by 8 publications

(1 citation statement)

References 30 publications

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“…They conducted a sensitivity analysis to examine the influence of effective parameters on the final designs. Kashani et al 44 applied several algorithms GA, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm ̶ to the design of MSE walls with geosynthetic reinforcement. The final cost was estimated based on two design variables of vertical space between geosynthetics layers and the length of reinforcements.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimization of mechanically stabilized earth retaining wall using evolutionary algorithms

Kashani

Camp

Azizi

et al. 2022

Num Anal Meth Geomechanics

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This paper uses evolutionary optimization algorithms to study the multi‐objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi‐objective optimization algorithms, including the non‐dominated sorting genetic algorithm II (NSGA‐II), strength Pareto evolutionary algorithm II (SPEA‐II), multi‐objective particle swarm optimization (MOPSO), multi‐objective multi‐verse optimization (MVO), and Pareto envelope‐based selection algorithm II (PESA‐II), are applied to the design procedure. MSE wall design requires two major requirements: external stability and internal stability. In this study, the optimality criterion is to minimize cost and its trade‐off with the factor of safety (FOS). To this end, two objectives are defined: (1) minimum cost, (2) maximum FOS. Three different strategies are considered for reinforcement combinations in the numerical simulations. Moreover, a sensitivity analysis was conducted on the variation of significant parameters, including backfill slope, wall height, horizontal earthquake coefficient, and surcharge load. The efficiency of the utilized algorithms was assessed through three well‐known coverage set measures, diversity, and hypervolume. These measures were further examined using basic statistical measures (i.e., min, max, standard deviation) and the Friedman test with a 95% confidence level.

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Section: Introductionmentioning

confidence: 99%