Prediction of the transfer length of prestressing strands with neural networks

Martí‐Vargas, José R.; Ferri, Francesc J.; Piqueras, Víctor Yepes

doi:10.12989/cac.2013.12.2.187

Cited by 38 publications

(17 citation statements)

References 30 publications

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“…Certainly, this is a very hard problem due to complex dependence relations among the different parameters. As in another study (Martí-Vargas et al 2013), this could help civil engineers from the point of view of the engineering problem and applications.…”

mentioning

confidence: 78%

Discussion of “Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression” by Jui-Sheng Chou, Ngoc-Tri Ngo, and Anh-Duc Pham

Martí‐Vargas

2016

J. Comput. Civ. Eng.

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mentioning

confidence: 78%

Discussion of “Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression” by Jui-Sheng Chou, Ngoc-Tri Ngo, and Anh-Duc Pham

Martí‐Vargas

2016

J. Comput. Civ. Eng.

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“…The most widespread metamodels are polynomial regression, artificial neural networks (ANN) and kriging. ANN has been used in different works related to structural engineering [21,22]. However, the kriging model has been demonstrated to be useful to obtain great reliability in the assessment of the response due to its predictive accuracy in non-linear functions [23].…”

Section: Introductionmentioning

confidence: 99%

Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge

2020

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The design of a structure is generally carried out according to a deterministic approach. However, all structural problems have associated initial uncertain parameters that can differ from the design value. This becomes important when the goal is to reach optimized structures, as a small variation of these initial uncertain parameters can have a big influence on the structural behavior. The objective of robust design optimization is to obtain an optimum design with the lowest possible variation of the objective functions. For this purpose, a probabilistic optimization is necessary to obtain the statistical parameters that represent the mean value and variation of the objective function considered. However, one of the disadvantages of the optimal robust design is its high computational cost. In this paper, robust design optimization is applied to design a continuous prestressed concrete box-girder pedestrian bridge that is optimum in terms of its cost and robust in terms of structural stability. Furthermore, Latin hypercube sampling and the kriging metamodel are used to deal with the high computational cost. Results show that the main variables that control the structural behavior are the depth of the cross-section and compressive strength of the concrete and that a compromise solution between the optimal cost and the robustness of the design can be reached.

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“…From the research point of view, these problems present interesting challenges in the areas of operations research, computational complexity, and algorithm theory. Examples of combinatorial problems are found in, scheduling problems [1,2], transport [2], machine learning [3], facility layout design [4], logistics [5], allocation resources [6,7], routing problems [8,9], robotics applications [10], civil engineering problem [11][12][13], engineering design problem [14], fault diagnosis of machinery [15], and social sustainability of infrastructure projects [16], among others. Combinatorial optimization algorithms should explore the solutions space to find optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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Prediction of the transfer length of prestressing strands with neural networks

Cited by 38 publications

References 30 publications

Discussion of “Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression” by Jui-Sheng Chou, Ngoc-Tri Ngo, and Anh-Duc Pham

Discussion of “Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression” by Jui-Sheng Chou, Ngoc-Tri Ngo, and Anh-Duc Pham

Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

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