A new approach for seepage parameter inversion of earth–rockfill dams based on an improved sparrow search algorithm

Zhou, Yang; Li, Chuyin; Pang, Rui; Li, Yichuan; Xu, Yongsheng; Chen, Jiansheng

doi:10.1016/j.compgeo.2023.106036

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…Simultaneously, when an individual sparrow detects danger, it emits an alarm signal, prompting the population to immediately engage in anti-predatory behavior. Within the sparrow population, producers offer foraging directions to other individuals; scroungers accompany producers in foraging activities; and sentinels are tasked with monitoring and alerting [29]. In SSA, sparrows are categorized into three roles: producers, scroungers, and sentinels.…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

Prediction of dike seepage pressure based on ISSA-BiLSTM

Chen,

Liu,

Xing

et al. 2024

Preprint

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Seepage behavior is one of the critical factor in the operational safety of dams, and predicting dam seepage is the key content in dam monitoring and safety assessment research. The existing traditional dam seepage pressure prediction models have problems such as easy to fall into local optimum and limited predictive efficiency. The sparrow search algorithm(SSA) was improved as ISSA using both methods nonlinear Sine Cosine optimization algorithm and adaptive producer and scrounger ratio. We combined the Bidirectional Long Short-Term Memory (BiLSTM) neural network model with ISSA to develop the ISSA-BiLSTM seepage pressure prediction model. And the critical feature factors were extracted based on LightGBM to construct the input layer for seepage pressure prediction. The results show that the ISSA-BiLSTM model's fitting outcomes are generally consistent with the observed changes in seepage pressure observations, achieving an R2 of 0.987. In comparison to SSA-BiLSTM and BiLSTM, the model exhibits a substantial reduction in errors, decreasing by approximately 20% and 30%, respectively. This model can provide technical support and insights for accurately predicting dam seepage, contributing to the advancement of this field.

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Section: Sparrow Search Algorithmmentioning

confidence: 99%

Prediction of dike seepage pressure based on ISSA-BiLSTM

Chen,

Liu,

Xing

et al. 2024

Preprint

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“…Song [23] established a fast seepage parameter inversion method for core earth-rock dams based on the fusion surrogate model. Zhou [24] presented a seepage parameter inversion approach for earth-rock dams based on the radial basis function and improved sparrow algorithm. Kang [25] proposed a response surface model for the parameter inversion of concrete dams based on the kernel extreme learning machine.…”

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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