Optimization of weight and cost of cantilever retaining wall by a hybrid metaheuristic algorithm

Sharma, Sushmita; Saha, Apu Kumar; Lohar, Geetanjali

doi:10.1007/s00366-021-01294-x

Cited by 36 publications

(21 citation statements)

References 47 publications

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“…It is simply an amalgamation of weight of concrete sections and weight of reinforcement. In case of key the weight of key is also included in the formulation as done by Camp and Akin [42], Sable and Patil [45], Gandomi et al [52], Gandomi et al [61], Kalemci et al [78], Millán-Paramo et al [81], Kashani et al [82], Sharma et al [87], and Uray et al [102]. Recently the carbon emission minimization objective has also been an area of interest; optimization with such an objective has been paired under the umbrella of sustainable design.…”

Section: Objective Functionmentioning

confidence: 99%

Design Optimization of Reinforced Concrete Cantilever Retaining Walls: A State‐of‐the‐Art Review

Shakeel

Azam

Riaz

et al. 2022

Advances in Civil Engineering

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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 structures such as reinforced concrete (RC) is still a complex task that requires multidisciplinary knowledge, hindering its practicability. Although past review papers have delved into this topic, they have only been able to cover the breadth of information available by covering broader aspects of optimization of structures. This study on the other hand aims to cover this topic in depth to uncover problem specific trends and issues, by focusing only on optimization of RC cantilever retaining walls. Although there is an abundance of research studies on this topic, there is an absence of any critical review to tie them up, and concurrently with its broader scope, it suffers the same lack of applicability in the field. The in-depth review presents a summarization of all the online publications including research articles, conference papers, and theses to the best of authors’ knowledge on the topic of RC cantilever retaining wall optimization. Geographical trends, regional developments, and prominent journals have been identified. The design codes, problem formulation, objectives, constraints, variables, and their optimization techniques are tabulated for ease of understanding. Unique areas of development investigated by the different researchers have been highlighted. Lastly, comprehensive recommendations for future works have been detailed with a focus on improving its applicability and assimilation into the construction industry.

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Section: Objective Functionmentioning

confidence: 99%

Design Optimization of Reinforced Concrete Cantilever Retaining Walls: A State‐of‐the‐Art Review

Shakeel

Azam

Riaz

et al. 2022

Advances in Civil Engineering

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“…Genetic Algorithms was used to the optimization of cantilever reinforced concrete retaining wall [10] and to optimize reinforced concrete retaining walls giving heuristic rules for the proportioning of retaining walls [11]. The hybrid metaheuristic algorithm has also been applied for optimization for retaining walls [12]. Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) -the three heuristic optimization algorithms for the optimization of retaining walls were compared for efficiency and PSO was recommended to be used as it was found to be superior in terms of effectiveness and efficiency [13].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Reinforced Concrete Cantilever Retaining Wall using Particle Swarm Optimization

Srivastava

Pandey

Kumar

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The design of structures depends on designer’s experience and generally, the designer proceeds with trial and error until he arrives at a design which satisfies prescribed limit states. This is especially true for reinforced concrete structures in which the structural configuration is decided first and then reinforcement requirements are determined, resulting in higher cost. Retaining walls involve a large number of variables and therefore have been far from optimization. However, since retaining walls comprise of 20-30 percent of the cost of highways in hilly regions, their optimization is critical to economy of the project. Particle Swarm Optimization (PSO) does not require the objective function to be linear or differentiable and hence is ideally suited for optimization of retaining walls. This study uses PSO as a tool for the optimizing a Cantilever RC Retaining Wall. The objective function consists of two parts - cost function and penalty term. A program and GUI was developed to implement PSO. A reduction in cost was achieved from 8% -17% depending on the height of retaining wall, while the optimization in weight varied from 9%-34%.

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“…For optimizing shape and size of truss structures under multiple frequency constraints, Adaptive Hybrid Evolutionary Firefly Algorithm (AHEFA) is developed by Lieu et al [23]. Hybrid Butterfly Optimization Algorithm and Symbiosis Organism Search (h-BOASOS) [24] for weight and cost optimization of the cantilever retaining walls, hybrid Eagle Strategy with Differential Evolution (ES-DE) [25] for optimum design of frame structures, and hybrid multi-design variable configuration cascade optimization and vibrating particles system (MDVC-UVPS) [26] for large-scale dome truss structures optimization are some of other noteworthy hybrid methods in structural design field in recent years. A hybrid algorithm is presented in this paper that embeds the Biogeography-Based Optimization (BBO) [27] into Imperialist Competitive Algorithm (ICA) [28] to design optimum trusses with discrete and continuous variables.…”

Section: Introductionmentioning

confidence: 99%

Optimum structural design of spatial truss structures via migration-based imperialist competitive algorithm

Kaveh

Rajabi

2022

Scientia Iranica

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This paper presents a new hybrid algorithm generated by combining advantageous features of the Imperialist Competitive Algorithm (ICA) and Biogeography Based Optimization (BBO) to create an effective search technique. Although the ICA performs fairly well in the exploration phase, it is less effective in the exploitation stage. In addition, its convergence speed is problematic in some instances. Meanwhile, the BBO method's migration operator strongly emphasizes local search to focus on promising solutions and finds the optimum solution more precisely. The combination of these two algorithms leads to a robust hybrid algorithm that has both exploratory and exploitative functionalities. The proposed hybrid algorithm is named Migration-Based Imperialist Competitive Algorithm (MBICA). To validate its performance, MBICA is used to optimize a variety of benchmark truss structures. Compared to some other methods, this algorithm converges to better or at least identical solutions by reducing the number of structural analyses. Finally, the results of the standard BBO, ICA, and other recently developed metaheuristic optimization methods are compared with the results of this study.

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Optimization of weight and cost of cantilever retaining wall by a hybrid metaheuristic algorithm

Cited by 36 publications

References 47 publications

Design Optimization of Reinforced Concrete Cantilever Retaining Walls: A State‐of‐the‐Art Review

Design Optimization of Reinforced Concrete Cantilever Retaining Walls: A State‐of‐the‐Art Review

Optimization of Reinforced Concrete Cantilever Retaining Wall using Particle Swarm Optimization

Optimum structural design of spatial truss structures via migration-based imperialist competitive algorithm

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