Updated Kriging-Assisted Shape Optimization of a Gravity Dam

Wang, Yongqiang and Liu, Ye and Ma, Xiaoyi (2021) Updated Kriging-Assisted Shape Optimization of a Gravity Dam. Water, 13 (1). p. 87. ISSN 2073-4441

[thumbnail of water-13-00087.pdf] Text
water-13-00087.pdf - Published Version

Download (3MB)

Abstract

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

Item Type: Article
Uncontrolled Keywords: updated Kriging surrogate model; genetic algorithm; gravity dam
Subjects: STM Repository > Biological Science
Depositing User: Managing Editor
Date Deposited: 16 Mar 2024 04:49
Last Modified: 16 Mar 2024 04:49
URI: http://classical.goforpromo.com/id/eprint/813

Actions (login required)

View Item
View Item