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

Mergos, Panagiotis E.; Mantoglou, Fotios

doi:10.1007/s00158-019-02380-x

Cited by 53 publications

(28 citation statements)

References 31 publications

(33 reference statements)

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“…In the context of this research, all the design steps of the wall are controlled by the use of Flower Pollination Algorithm (FPA) with the analysis performed by Matlab Software. The algorithm is a population-based metaheuristic algorithm confirmed for its simple formulation and the effectiveness in terms of computational performance [18,19]. The logic of the Flower Pollination Algorithm is based on the evolutionary mechanism of biological flowering plant systems via pollination either biotic or biotic and developed by Yang (2012).…”

Section: Methodsmentioning

confidence: 99%

Effects of Soil Geotechnical Properties on the Prediction of Optimal Dimensions of Restricted Reinforced Concrete Retaining Walls

Kayabekir

Arama

Bekdaş

et al. 2020

Hittite J. Sci. Eng.

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R einforced concrete retaining walls are the most preferred type of supporting structures that are constructed in order to resist lateral soil forces, especially activated due to the excavation works which the soil properties of the construction field cannot permit to hold the unbalanced soil mass with slopes. The shape and dimensions of the retaining wall system can be changed due to the project requirements, soil conditions, land ownership situation, infrastructure locations or environmental restrictions and it can be essential to build the sections of the wall with restrictions. The most known type of restricted reinforced concrete retaining walls is L-shaped type. L-shaped retaining walls (LSRW) are generally used for simple loading applications where the foundation length is extended only on the heel side of the wall. In this study, the mentioned L-Shaped type of reinforced concrete retaining walls was considered due to the preferability rather than other restricted type of supporting structures, according to the easiness of their construction works, the attainability of used materials and easiness of the mobilization of necessitated equipment for construction. The design process of

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Section: Methodsmentioning

confidence: 99%

Effects of Soil Geotechnical Properties on the Prediction of Optimal Dimensions of Restricted Reinforced Concrete Retaining Walls

Kayabekir

Arama

Bekdaş

et al. 2020

Hittite J. Sci. Eng.

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“…Today, it becomes increasingly important to take various objectives into account to improve buildability and performance of structures and reduce their economic, environmental, and social impacts in a life cycle perspective. Nevertheless, in the early structural design process, when the possibility to influence the outcome is the largest, the traditional practice is to focus solely on the lowest initial costs, see, e.g., International Federation for Structural Concrete (2009), Mathern et al (2013), and World Economic Forum (2016). Cost optimization is often conducted by means of employing a point-based incremental improvement strategy, relying on trial-and-error and the designer's intuition, see, e.g., Parrish (2009) and Maher (1987).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective constrained Bayesian optimization for structural design

Mathern

Steinholtz

Sjöberg

et al. 2020

Struct Multidisc Optim

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The planning and design of buildings and civil engineering concrete structures constitutes a complex problem subject to constraints, for instance, limit state constraints from design codes, evaluated by expensive computations such as finite element (FE) simulations. Traditionally, the focus has been on minimizing costs exclusively, while the current trend calls for good trade-offs of multiple criteria such as sustainability, buildability, and performance, which can typically be computed cheaply from the design parameters. Multi-objective methods can provide more relevant design strategies to find such trade-offs. However, the potential of multi-objective optimization methods remains unexploited in structural concrete design practice, as the expensiveness of structural design problems severely limits the scope of applicable algorithms. Bayesian optimization has emerged as an efficient approach to optimizing expensive functions, but it has not been, to the best of our knowledge, applied to constrained multi-objective optimization of structural concrete design problems. In this work, we develop a Bayesian optimization framework explicitly exploiting the features inherent to structural design problems, that is, expensive constraints and cheap objectives. The framework is evaluated on a generic case of structural design of a reinforced concrete (RC) beam, taking into account sustainability, buildability, and performance objectives, and is benchmarked against the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a random search procedure. The results show that the Bayesian algorithm performs considerably better in terms of rate-of-improvement, final solution quality, and variance across repeated runs, which suggests it is well-suited for multi-objective constrained optimization problems in structural design.

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“…The social sustainability of infrastructure projects was tackled in [14] using Bayesian methods. In recent works [19,20], different meta-heuristics algorithms were used for optimal design of RC retaining walls. The life cycle assessment of earth-retaining walls was analyzed in [21,22].…”

Section: Introductionmentioning

confidence: 99%

The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

2020

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The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

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

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

Cited by 53 publications

References 31 publications

Effects of Soil Geotechnical Properties on the Prediction of Optimal Dimensions of Restricted Reinforced Concrete Retaining Walls

Effects of Soil Geotechnical Properties on the Prediction of Optimal Dimensions of Restricted Reinforced Concrete Retaining Walls

Multi-objective constrained Bayesian optimization for structural design

The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

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