A New Method for Inversion of Dam Foundation Hydraulic Conductivity Using an Improved Genetic Algorithm Coupled with an Unsaturated Equivalent Continuum Model and Its Application

Peng, Jiayi; Shen, Zhenzhong; Xu, Liqun; Gan, Lei; Tan, Jiacheng

doi:10.3390/ma16041662

Cited by 4 publications

(1 citation statement)

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“…By minimizing the mean square error between the measured values and inversion values at seepage monitoring points, the inversion calculation of permeability coefficients can be transformed into solving a complex nonlinear least squares optimization problem. To address this challenge, some classical optimization algorithms were initially applied in parameter inversion studies and achieved relatively satisfactory optimization results [15], but the implementation of such methods requires the large-scale repetition of numerical simulation calculations, resulting in high inversion costs but low efficiency [16]. Subsequently, some scholars began to introduce surrogate models based on machine learning algorithms to replace the cumbersome and time-consuming numerical models, which strongly promoted the intelligent development of parameter inversion.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%