Investigation of ANN architecture for predicting residual strength of clay soil

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 mine...

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Bibliographic Details
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
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07547-0