Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/43024
Title: Using artificial neural network (ANN) for prediction of soil coefficient of consolidation
Authors: Pham, Thai Binh
Singh, Sushant K.
Ly, Hai Bang
Keywords: Artificial Neural Networks
Compression coefficient
Machine learning
Vietnam
Issue Date: 2020
Series/Report no.: Vietnam Journal of Earth Sciences;Vol. 42, No. 04 .- P.311-319
Abstract: Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/43024
ISSN: 0866-7187
Appears in Collections:Vietnam journal of Earth sciences

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