An automated approach for the design of Mechanically Stabilized Earth Walls incorporating metaheuristic optimization algorithms


APPLIED SOFT COMPUTING, vol.74, pp.547-566, 2019 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 74
  • Publication Date: 2019
  • Doi Number: 10.1016/j.asoc.2018.09.039
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.547-566
  • Keywords: Mechanically Stabilized Earth Walls, Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Differential Evolution, GLOBAL OPTIMIZATION, SEARCH, PERFORMANCE, BEHAVIOR, TESTS


Considered as cost-efficient, reliable and aesthetic alternatives to the conventional retaining structures, Mechanically Stabilized Earth Walls (MSEWs) have been increasingly used in civil engineering practice over the previous decades. The design of these structures is conventionally based on engineering guidelines, requiring the use of trial and error approaches to determine the design variables. Therefore, the quality and cost effectiveness of the design is limited with the effort, intuition, and experience of the engineer while the process transpires to be time-consuming, both of which can be solved by developing automated approaches. In order to address these issues, the present study introduces a novel framework to optimize the (i) reinforcement type, (ii) length, and (iii) layout of MSEWs for minimum cost, integrating metaheuristic optimization algorithms in compliance with the Federal Highway Administration guidelines. The framework is conjoined with optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE) and tested with a set of benchmark design problems that incorporate various types of MSEWs with different heights. The results are comparatively evaluated to assess the most effective optimization algorithm and validated using a well-known MSEW analysis and design software. The outcomes indicate that the proposed framework, implemented with a powerful optimization algorithm, can effectively produce the optimum design in a matter of seconds. In this sense, DE algorithm is proposed based on the improved results over GA, PSO, and ABC. (C) 2018 Elsevier B.V. All rights reserved.