Prediction of elastic modulus for fibre-reinforced soil-cement mixtures: a machine learning approach

Abstract

Soil-cement mixtures reinforced with fibres are an alternative method of chemical soil stabilisation in which the inherent disadvantage of low or no tensile or flexural strength is overcome by incorporating fibres. These mixtures require a significant amount of time and resources for comprehensive laboratory characterisation, because a considerable number of parameters are involved. Therefore, the implementation of a Machine Learning (ML) approach provides an alternative way to predict the mechanical properties of soil-cement mixtures reinforced with fibres. In this study, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF), and Multiple Regression (MR) algorithms were trained for predicting the elastic modulus of soil-cement mixtures reinforced with fibres. For ML algorithms training, a dataset of 121 records was used, comprising 16 properties of the composite material (soil, binder, and fibres). ANN and RF showed a promising determination coefficient (R² ≥ 0.93) on elastic modulus prediction. Moreover, the results of the proposed models are consistent with the findings that the fibre and binder content have a significant effect on the elastic modulus.