Hybridizing two-stage meta-heuristic optimization model with weighted least squares support vector machine for optimal shape of double-arch dams

Mahani, Amir Saber; Shojaee, Saeed; Salajegheh, E.; Khatibinia, Mohsen

doi:10.1016/j.asoc.2014.11.014

Cited by 32 publications

(5 citation statements)

References 32 publications

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“…Collected data relative to sediments sub-system. Substituting specific numerical values in the equations it is possible to find the solutions [103] BEM [19,53] a Differential equations Displacements.…”

Section: Dam Namementioning

confidence: 99%

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Reviewing Arch-Dams’ Building Risk Reduction Through a Sustainability–Safety Management Approach

Zacchei

Molina

2020

Sustainability

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The importance of dams is rapidly increasing due to the impact of climate change on increasing hydrological process variability and on water planning and management need. This study tackles a review for the concrete arch-dams’ design process, from a dual sustainability/safety management approach. Sustainability is evaluated through a design optimization for dams´ stability and deformation analysis; safety is directly related to the reduction and consequences of failure risk. For that, several scenarios about stability and deformation, identifying desirable and undesirable actions, were estimated. More than 100 specific parameters regarding dam-reservoir-foundation-sediments system and their interactions have been collected. Also, a summary of mathematical modelling was made, and more than 100 references were summarized. The following consecutive steps, required to design engineering (why act?), maintenance (when to act) and operations activities (how to act), were evaluated: individuation of hazards, definition of failure potential and estimation of consequences (harm to people, assets and environment). Results are shown in terms of calculated data and relations: the area to model the dam–foundation interaction is around 3.0 Hd2, the system-damping ratio and vibration period is 8.5% and 0.39 s. Also, maximum elastic and elasto-plastic displacements are ~0.10–0.20 m. The failure probability for stability is 34%, whereas for deformation it is 29%.

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Section: Dam Namementioning

confidence: 99%

“…N/A = Not applicable. a Coupled BEM-FEM is used to study the fluid-structure interactions [19]. Also, accurate computation of fluid-structure nonlinear interaction is analysed by the immersed boundary method (IBM) proposed in [41].…”

Section: Photos Animations 3dmentioning

confidence: 99%

Reviewing Arch-Dams’ Building Risk Reduction Through a Sustainability–Safety Management Approach

Zacchei

Molina

2020

Sustainability

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“…The Bayesian distribution is here applied to calculate the volume and area of double arch dams. From literature, five papers have been considered [5][6][7][8][9] and the values of the results in these works have been used to develop the Bayesian theorem. The samples are formed by 770 collected date.…”

Section: Bayesian Distributionmentioning

confidence: 99%

“…During the equations implementation, it is necessary to verify the constraints. From collected data in literature [5][6][7][8][9] and considering the results of the distribution posteriori (Table 1), it is possible to verify some constraints: 3.1 ≤ t1 ≤ 11.9, 8.0 ≤ t2 ≤ 16.4, 12.0 ≤ t3 ≤ MATEC Web of Conferences 211, 14002 (2018) https://doi.org/10.1051/matecconf/201821114002 VETOMAC XIV 35.0. The maximum values of the slope s and the ratio β are 0.36 and 1.0, respectively.…”

Section: Geometrical Model Analysesmentioning

confidence: 99%

Estimation of optimal area and volume for double arch-dams

Zacchei

Molina

2018

MATEC Web Conf.

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This research is focused on the optimum area and volume estimation of double arch dams. The first stage of the methodology refers to defining issues about Bayesian estimators to obtain the value for designing the optimum dam shape. After that, the shape equations are iterated step-bystep to obtain the optimal solution. From the inventory of existing dams, it is possible to extract many important values although they are not sufficient. To obtain the non-available data, the Gaussian distribution under the Bayesian theorem hypotheses has been employed. This theorem converts the prior distribution using unknown parameters into the posterior distribution which provides expected estimators. The choice of the dam shape is strongly based on the experience, therefore by knowing and applying real information of existing dams it is possible to carry out a more precise analysis.

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“…However, when the experiments required by the GA are performed in ANSYS to optimize the geometric shape of a single-curvature arch dam, numerous finite element numerical simulations are usually required. Several scholars have attempted to reduce the number of finite element method (FEM) iterations by using surrogate models to improve computational efficiency [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm

et al. 2019

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Shape optimization of single-curvature arch dams using the finite element method (FEM) is often computationally expensive. To reduce the computational burden, this study introduces a new optimization method, combining a genetic algorithm with a sequential Kriging surrogate model (GA-SKSM), for determining the optimal shape of a single-curvature arch dam. At the start of genetic optimization, a KSM was constructed using a small sample set. In each iteration of optimization, the minimizing predictor criterion and low confidence bound criterion were used to collect samples from the domain of interest and accumulate them into a small sample set to update the KSM until the optimization process converged. A practical problem involving the optimization of a single-curvature arch dam was solved using the introduced GA-SKSM, and the performance of the method was compared with that of GA-KSM and GA-FEM methods. The results revealed that the GA-SKSM method required only 5.40% and 12.40% of the number of simulations required by the GA-FEM and GA-KSM methods, respectively. The GA-SKSM method can significantly improve computational efficiency and can serve as a reference for effective optimization of the design of single-curvature arch dams.

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Hybridizing two-stage meta-heuristic optimization model with weighted least squares support vector machine for optimal shape of double-arch dams

Cited by 32 publications

References 32 publications

Reviewing Arch-Dams’ Building Risk Reduction Through a Sustainability–Safety Management Approach

Reviewing Arch-Dams’ Building Risk Reduction Through a Sustainability–Safety Management Approach

Estimation of optimal area and volume for double arch-dams

Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm

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