Passive earth pressure coefficients prediction using deep learning techniques

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Predicting passive earth pressure coefficients is crucial for the design of retaining structures, as these coefficients, dependent on soil properties and wall geometry, they impact wall stability. In a practical context, the experimental and analytical approaches are often inaccurate and complex. In this paper, we have developed and tested three Recurrent Neural Networks: RNN, LSTM, and GRU models to predict passive earth pressure coefficients 〖(K〗_pγ, K_pq and K_(pc )) using datasets with 1147 observations, including key parameters such as the ratio of wall width to height (b⁄(h)), the soil-wall interface friction angle to internal friction angle ratio (δ⁄(ϕ)) and the soil friction angle (ϕ). The results demonstrate that our deep neural network models perform better in terms of accuracy and reliability when compared to previously models. As a result, the LSTM model outperformed the RNN, and GRU models in terms of prediction accuracy, exhibiting the best MSE performance (K_pγ = 0.0014, K_pq = 0.0020, K_pc = 0.0020).