A Numerical Analysis of the Leakage Characteristics of an Embankment Dam Slope With Internal Erosion

Yu, Jing; Ding, Yu; Wang, Xuan; Zhang, Jiasheng; Chen, Xiaobin

doi:10.3389/feart.2022.866238

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Since seepage is generally considered a key factor in the design, construction, and safe operation of floodgates, the seepage prediction analysis of floodgates has attracted more and more attention. Since the development of finite element software has accelerated in recent years, many scholars have used different finite element software to simulate, analyze, and calculate seepage flow in hydraulic structure engineering, for example, PLAXIS [9], COM-SOL Multiphysics [10][11][12], GeoStudio [13], FLAC3D [14,15], SEEP/W [16], ABAQUS [17,18], FEFLOW [19], FLUENT [20], FLOW3D [21], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

Huang,

Gu,

Peng

et al. 2024

Water

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The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and dependency relationship in the sequence and uses the multi-strategy improved Harris Hawks optimization algorithm (MHHO) to analyze its two hyperparameters: By optimizing the number of forward and backward neurons, the overfitting and long-term dependence problems of the neural network are solved, and the convergence rate is accelerated. Based on this, the MHHO-BiLSTM statistical prediction model of sluice seepage is established in this paper. To begin with, the prediction model uses water pressure, rainfall, and aging effects as input data. Afterward, the bidirectional long short-term memory neural network parameters are optimized using the multi-strategy improved Harris Hawks optimization algorithm. Then, the statistical prediction model based on the optimization algorithm proposed in this paper for sluice seepage is proposed. Finally, the seepage data of a sluice and its influencing factors are used for empirical analysis. The calculation and analysis results indicate that the optimization algorithm proposed in this paper can better search the optimal parameters of the bidirectional long short-term memory neural network compared with the original Harris Eagle optimization algorithm, optimizing the bidirectional long short-term memory neural network (HHO-BiLSTM) and the original bidirectional long short-term memory neural network (BiLSTM). Meanwhile, the bidirectional long and short-term neural network (BiLSTM) model shows higher prediction accuracy and robustness.

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