Implementation of artificial neural network to predict the permeability and solubility models of gypseous soil
2021
Obead, Imad Habeeb | Omran, Hassan Ali | Fattah, Mohammed Yousif
The objective of the present study is to make a database that describes the leaching permeability behavior of collapsible gypseous soil. The data will be implemented to develop ANN prediction models for predicting the saturated coefficient of permeability and percentage of solubility by weight. The complex soil behavior and tedious and time consume in soil testing have driven researchers to use Artificial Neural Network (ANN) as tool for prediction. The objectives of the study were to investigate leaching-permeability behavior of collapsible gypseous soils and to develop ANN models for estimating the saturated coefficient of permeability and solubility of the soils. The MATLAB R2015a software was used to predict the saturated coefficient of permeability and the solubility percentage by weight of gypseous soils. The dataset used in this work included (513) records of experimental measurements extracted from leaching-permeability tests conducted on gypseous soil samples taken from Baher Al-Najaf in Iraq. Four input variables were investigated to have the most important influence on the permeability and solubility percentage by weight. According to the achieved statistical analysis, the ANNs model have a reliable capability to find out the predictions with a high-level of accuracy. The gypseous soils exhibited a high rate of dissolution of soluble minerals content, which caused increase in the coefficient of permeability as the soil samples reach the state of long-term full saturation.
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