2022
DOI: 10.1155/2022/4760175
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Design Optimization of Reinforced Concrete Cantilever Retaining Walls: A State‐of‐the‐Art Review

Abstract: The booming growth of computational abilities in the 21st century has led to its assimilation and benefit in all horizons of engineering. For civil engineers, these advancements have led to groundbreaking technologies such as BIM, automation, and optimization. Unfortunately, even in an era of dwindling resources and dire need for sustainability, optimization has failed to attract implementation in practice. Despite an exponential growth as an area of research interest, the optimization of engineering structure… Show more

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Cited by 8 publications
(2 citation statements)
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“…In contrast to these, GAs search for optimal solutions by selection, crossover and mutation of populations of feasible solutions. Hybridizing both typologies allows for high-quality results in optimizing structures such as composite bridges [20,21], retaining walls [15,[22][23][24] or prestressed box girder bridges [21,25].…”
Section: Introductionmentioning
confidence: 99%
“…In contrast to these, GAs search for optimal solutions by selection, crossover and mutation of populations of feasible solutions. Hybridizing both typologies allows for high-quality results in optimizing structures such as composite bridges [20,21], retaining walls [15,[22][23][24] or prestressed box girder bridges [21,25].…”
Section: Introductionmentioning
confidence: 99%
“…Although there have been significant advancements in engineering design proce-dures, optimizing engineering structures is still a difficult task that requires a multidisci-plinary approach. Using different parameters to produce predictive models for optimum cost prediction of reinforced concrete cantilever walls been extensively studied in the re-cent literature [22]. There are few studies focused on predicting the costs of retaining walls using predictive equations, despite the fact that many researchers have investigated the optimization of retaining walls using different methodologies for specific design input parameters.…”
Section: Introductionmentioning
confidence: 99%