Reliability analysis of high core rockfill dam against seepage failure considering spatial variability of hydraulic parameters

Li, Yanlong; Liu, Hangfei; Wen, Lifeng; Xu, Zengguang; Zhang, Ye

doi:10.1007/s11440-023-02159-x

Cited by 8 publications

(2 citation statements)

References 62 publications

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“…(3) In order to make up for the shortcomings of the DBO algorithm, multiple strategies are fused to improve it from multiple perspectives, thereby establishing the improved dung beetle optimization (IDBO) algorithm with superior optimization performance. (4) The key parameters (penalty factor, insensitive loss factor, and kernel function variance) of the SVR model are optimized by the IDBO algorithm to accurately establish the nonlinear mapping relationship between the permeability coefficient and hydraulic head calculated by a finite element model. (5) The fully optimized and trained SVR surrogate model is employed to replace the finite element calculation model, and the mean square error between the observed and predicted hydraulic head at the monitoring point is used as the optimization objective function to determine the optimal combination of permeability coefficients.…”

Section: Constructing the Sobol-idbo-svr Fusion Surrogate Modelmentioning

confidence: 99%

“…With the accumulation of dam construction experience and improvements at the theoretical level, China has successively built several ultra-high concrete face rockfill dams with heights exceeding 200 m and is gradually transitioning from the 200 m grade to the 300 m grade [2]. At the same time, with the increasing dam heights, concrete face rockfill dams will inevitably encounter more complex technical challenges and more severe service conditions [3]; among these, the seepage safety issue of dams is the key issue restricting their long-term stable operation [4,5]. Therefore, it is of important engineering significance to accurately predict the seepage behavior of concrete face rockfill dams based on fully understanding the permeability characteristics of the materials.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Inversion Method for Permeability Coefficients of Concrete Face Rockfill Dam Based on Sobol-IDBO-SVR Fusion Surrogate Model

Xiong,

Shen,

et al. 2024

Mathematics

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The accurate and efficient inversion of permeability coefficients is significant for the scientific assessment of seepage safety in concrete face rockfill dams. In addressing the optimization challenge of permeability coefficients with few samples, multiple parameters, and strong nonlinearity, this paper proposes a novel intelligent inversion method based on the Sobol-IDBO-SVR fusion surrogate model. Firstly, the Sobol sequence sampling method is introduced to extract high-quality combined samples of permeability coefficients, and the equivalent continuum seepage model is utilized for the forward simulation to obtain the theoretical hydraulic heads at the seepage monitoring points. Subsequently, the support vector regression surrogate model is used to establish the complex mapping relationship between the permeability coefficients and hydraulic heads, and the convergence performance of the dung beetle optimization algorithm is effectively enhanced by fusing multiple strategies. On this basis, we successfully achieve the precise inversion of permeability coefficients driven by multi-intelligence technologies. The engineering application results show that the permeability coefficients determined based on the inversion of the Sobol-IDBO-SVR model can reasonably reflect the seepage characteristics of the concrete face rockfill dam. The maximum relative error between the measured and the inversion values of the hydraulic heads at each monitoring point is only 0.63%, indicating that the inversion accuracy meets the engineering requirements. The method proposed in this study may also provide a beneficial reference for similar parameter inversion problems in engineering projects such as bridges, embankments, and pumping stations.

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Section: Constructing the Sobol-idbo-svr Fusion Surrogate Modelmentioning

confidence: 99%