Multivariate formulation to predict the frictional strength of fiber reinforced soils using gene expression programming

Abstract

Modeling and prediction approaches have attracted much interest in recent years to evaluate the efficiency of fiber reinforcement in improving the strength characteristics of loose soils. Several analytical and artificial intelligence (AI) based models have been presented to estimate friction angle of fiber reinforced soils (FRS). However, due to limited input characteristics, these analytical models lack accuracy, whereas AI-based models are developed on advanced programming methods and need substantial programming expertise, resulting in a lack of agreement in mechanism and application. Herein, we provided a multivariate formulation that uses gene expression programming (GEP) to predict the frictional strength of FRS using fifteen influencing factors, which, to the best of our knowledge, has never been reported before. In this regard, two mathematical models are presented, one of which incorporates both qualitative and quantitative input factors, while the other just considers quantitative variables. Laboratory experimental data on FRS from various sources was assessed and properly analysed within the proposed framework while taking into account various affecting factors. The results illustrate the efficiency of GEP in estimating FRS frictional strength and emphasis the need of considering several parameters for prediction. Sensitivity and parametric studies were also performed to identify the relevance of each influencing factor and to determine whether the prediction models meet physical processes or are purely empirical relations. Finally, a user-friendly computer program was developed based on the GEP derived mathematical formulations to anticipate the FRS friction angle, which offers practitioners great field applicability.