Seepage Through Earth Dams with Internal Cut Off

Attia, Michael; Razek, Mohamed Abdel; Salam, A. Abdel

doi:10.1007/s10706-021-01865-1

Cited by 12 publications

(2 citation statements)

References 5 publications

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“…The length of a horizontal blanket filter is a controlling factor to reduce the pore water pressure rather than its thickness. Attia et al (2021) studied seepage through earth dams with internal cut-off, Seepage through an earth dam with internal cut-off is experimentally studied in the laboratory of Irrigation Engineering and Hydraulics Department, Faculty of Engineering, Alexandria University, Egypt, on a Hele-Shaw model. The effect of the relative penetration depth (d/D) and the relative distance of cut-off (X/D w ) on both the relative drop in the total head upstream (ΔH/H ) and relative length of filter (L f /H ) is studied.…”

Section: Introductionmentioning

confidence: 99%

Wavelet–ANN hybrid model evaluation in seepage prediction in nonhomogeneous earthen dams

Fatehi-Nobarian,

Fard Moradinia

2024

Water Practice &Amp; Technology

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In this study, novel methods such as wavelet–artificial neural network hybrid models and artificial neural network models were used to predict seepage from the Zonouz earthen dam. The dataset consisted of 972 piezometric data points. Statistical fitting methods such as root mean squared error, determination coefficient, scatter plots, and data distribution diagrams were used to evaluate the results. The findings indicated that the wavelet–artificial neural network hybrid model was more accurate than the artificial neural network model. Specifically, during training, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.820, 0.680, 743.39, and 792.52, while the artificial neural network model had 0.700, 0.600, 426.39, and 131.45. Similarly, during validation, the wavelet–artificial neural network hybrid model had determination coefficients and root mean squared errors of 0.700, 0.600, 426.39, and 131.45, while the artificial neural network model had 0.823, 0.680, 743.39, and 792.52. Therefore, the wavelet–artificial neural network hybrid model can be proposed as a precise method for predicting seepage in earthen dams and is more accurate than the artificial neural network model. This study highlights the importance of preventing dam failures and using advanced modeling techniques for better predictions and preventive measures.

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Section: Introductionmentioning

confidence: 99%

Wavelet–ANN hybrid model evaluation in seepage prediction in nonhomogeneous earthen dams

Fatehi-Nobarian,

Fard Moradinia

2024

Water Practice &Amp; Technology

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“…Because of the materials heterogeneity, water variations content and density in the implementation took place. This leads to significant permeability variations with the ratio of 10 to 100 inside the same layer [3]. The best solution to overcome the earth dam heterogeneity is certainly the vertical drain arranged within the dam body which is inspired from the very 1 st arrangement done by Terzaghi for the Vigario dam in 1947 in Brazil.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Estimation of Seepage through Homogeneous Earth Dams Using Neural Network and Empirical Equation

Chouireb

Noureddine²,

Djehiche

et al. 2023

AEF

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In this investigation, the quantity of seepage through a homogeneous earth dam with a vertical drain on a permeable foundation was studied using the SEEP/W program. Moreover, the effects of the geometrical and geotechnical parameters (upstream slope, dam height, top width, free board, and permeability ratio) on leakage flow were analyzed with SPSS 20. To begin with, leakage flow values through an earth dam were estimated using SEEP/W by taking three values of each parameter, and then statistical analysis was performed with the help of dimensional analysis. In addition, an empirical equation and a neural network model dubbed q_ANN were developed to calculate leakage flow through an earth dam with a vertical drain. The validation of these models (empirical equation and q_ANN) is made by their applications to real case studies such as El-Haimeur and Boubrik dams located in Ghardaia City. In conclusion, the values of these models can prove that the performance of the models is high and accurate to predict leakage flow through a homogeneous earth dam with a vertical drain on a permeable base.

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Seepage Analysis of an Earthen Dam: A Case Study of Tamta Dam

Gupta,

Yadu

2024

Lecture Notes in Civil Engineering

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Seepage Through Earth Dams with Internal Cut Off

Cited by 12 publications

References 5 publications

Wavelet–ANN hybrid model evaluation in seepage prediction in nonhomogeneous earthen dams

Wavelet–ANN hybrid model evaluation in seepage prediction in nonhomogeneous earthen dams

Analysis and Estimation of Seepage through Homogeneous Earth Dams Using Neural Network and Empirical Equation

Seepage Analysis of an Earthen Dam: A Case Study of Tamta Dam

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