Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

Methodologies based on metaheuristic algorithms such as particle swarm optimization, harmony search algorithm, and teaching-learning-based optimization are proposed for optimum design of reinforced concrete cantilever retaining walls. The objective function of optimization is to find a design providing minimum cost, including material and construction costs. For this purpose, the best combination of 11 design variables (heel and toe projections, stem thickness at the top and bottom of a wall, slab thickness and rebar diameters, and spacing between the bars) that satisfy 29 design constraints including stability (overturning, sliding, and bearing) and reinforced concrete design of the wall are searched during the optimization process. The rules of ACI 318 14 (building code requirements for structural concrete) are used for the reinforced concrete design. In order to determine the strengths and weaknesses of algorithms, several different cases are investigated. As conclusions, some suggestions have been obtained that will lead to future work in this field.

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Eco-Friendly Design of Reinforced Concrete Retaining Walls: Multi-objective Optimization with Harmony Search Applications

Sustainability ◽

10.3390/su12156087 ◽

2020 ◽

Vol 12 (15) ◽

pp. 6087 ◽

Cited By ~ 1

Author(s):

Aylin Ece Kayabekir ◽

Zülal Akbay Arama ◽

Gebrail Bekdaş ◽

Sinan Melih Nigdeli ◽

Zong Woo Geem

Keyword(s):

Reinforced Concrete ◽

Co2 Emission ◽

Search Algorithm ◽

Harmony Search ◽

Retaining Walls ◽

Unit Weight ◽

Flower Pollination Algorithm ◽

Objective Functions ◽

Multi Objective ◽

The Cost

In this study, considering the eco-friendly design necessities of reinforced concrete structures, the acquirement of minimizing both the cost and the CO2 emission of the reinforced concrete retaining walls in conjunction with ensuring stability conditions has been investigated using harmony search algorithm. Optimization analyses were conducted with the use of two different objective functions to discover the contribution rate of variants to the cost and CO2 emission individually. Besides this, the integrated relationship of cost and CO2 emission was also identified by multi-objective analysis in order to identify an eco-friendly and cost-effective design. The height of the stem and the width of the foundation were treated as design variables. Several optimization cases were fictionalized in relation with the change of the depth of excavation, the amount of the surcharge applied at the top of the wall system at the backfill side, the unit weight of the backfill soil, the costs, and CO2 emission amounts of both the concrete and the reinforcement bars. Consequently, the results of the optimization analyses were arranged to discover the possibility of supplying an eco-friendly design of retaining walls with the minimization of both cost and gas emission depending upon the comparison of outcomes of the identified objective functions. The proposed approach is effective to find both economic and ecological results according to hand calculations and flower pollination algorithm.

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Metaheuristic approaches for optimum design of cantilever reinforced concrete retaining walls

10.20528/cjsmec.2016.11.031 ◽

2016 ◽

Cited By ~ 1

Keyword(s):

Reinforced Concrete ◽

Optimum Design ◽

Retaining Walls

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Grey wolf optimizer for optimum design of reinforced concrete cantilever retaining walls

10.1063/1.5043893 ◽

2018 ◽

Cited By ~ 3

Author(s):

Gebrail Bekdaş ◽

Rasim Temür

Keyword(s):

Reinforced Concrete ◽

Optimum Design ◽

Retaining Walls ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Cantilever Retaining Walls

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Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls

Sustainability ◽

10.3390/su13041639 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1639

Author(s):

Melda Yücel ◽

Aylin Ece Kayabekir ◽

Gebrail Bekdaş ◽

Sinan Melih Nigdeli ◽

Sanghun Kim ◽

...

Keyword(s):

Reinforced Concrete ◽

Structural Engineering ◽

Optimization Problem ◽

Search Algorithm ◽

Harmony Search ◽

Retaining Walls ◽

Comparative Investigation ◽

Flower Pollination Algorithm ◽

Local Optimum ◽

Population Number

In the optimum design of reinforced concrete (RC) structural members, the robustness of the employed method is important as well as solving the optimization problem. In some cases where the algorithm parameters are defined as non-effective values, local-optimum solutions may prevail over the existing global optimum results. Any metaheuristic algorithm can be effective to solve the optimization problem but must give the same results for several runs. Due to the randomization nature of these algorithms, the performance may vary with respect to time. The essential and novel work done in this study is the comparative investigation of 10 different metaheuristic algorithms and two modifications of harmony search (HS) algorithm on the optimum cost design of RC retaining walls constrained with geotechnical and structural state limits. The employed algorithms include classical ones (genetic algorithm (GA), differential evaluation (DE), and particle swarm optimization (PSO)), proved ones on structural engineering applications (harmony search, artificial bee colony, firefly algorithm), and recent algorithms (teaching–learning-based optimization (TLBO), flower pollination algorithm (FPA), grey wolf optimization, Jaya algorithm (JA)). The modifications of HS include adaptive HS (AHS) concerning the automatic change of algorithm parameters and hybridization of AHS with JA that is developed for the investigated problem. According to the numerical investigations, recent algorithms such as TLBO, FPA, and JA are generally the best at finding the optimum values with less deviation than the others. The adaptive-hybrid HS proposed in this study is also competitive with these algorithms, while it can reach the best solution by using a lower population number which can lead to timesaving in the optimization process. By the minimization of material used in construction via best optimization, sustainable structures that support multiple types of constraints are provided.

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