Investigation of ANN architecture for predicting residual strength of clay soil

• Published in: Neural computing & applications 2022-11, Vol.34 (21), p.19253-19268
• Main Authors: Tran, Van Quan, Dang, Viet Quoc, Do, Hai Quan, Ho, Lanh Si
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Clay minerals; Clay soils; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Correlation coefficients; Data Mining and Knowledge Discovery; Estimation; Friction; Image Processing and Computer Vision; Kaolinite; Liquid limits; Mathematical models; Mica; Original Article; Parameters; Probability and Statistics in Computer Science; Residual strength; Sensitivity analysis; Smectites; Soil strength; Soils; Two dimensional analysis
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-07547-0

This paper introduces a developed method of an artificial neural networks (ANN) architecture for estimating the residual strength of clay soil. To implement this purpose, a database of input soil parameters is built, including liquid limit, plasticity index, A-line value, clay fraction, massive minerals, mica, kaolinite, and smectite, in which the output is the residual friction angle. The ANN model was developed by extensively analyzing a number of hidden layers and number of neurons in every layer, incorporating a statistical investigation of the model performance. The obtained results indicate that the ANN model is an outperformed and promising method based on various well-known indicators such as correlation coefficient, mean absolute error, and root mean square error. The achieved ANN model also gives higher estimation accuracy than those results in the literature. Finally, partial dependence plot 2-D was used for sensitivity analysis within the ANN algorithm to investigate the effect of coupled input variables on the estimated residual friction angle of the soil. It was found that A-line value, clay fraction, and massive minerals are the most important input parameters influencing the residual friction angle.