Peak Shear Strength of Discrete Fiber-Reinforced Soils Computed by Machine Learning and Metaensemble Methods

AbstractThe accuracy of prior theoretical and empirical models for predicting the shear strength of fiber-reinforced soil (FRS) is questionable because of the difficulty of using these simplified models to describe the complex mechanism of soil-fiber interaction. This study compiled a large database of available high quality triaxial and direct shear tests on FRS documented in the literature from 1983 to 2015. The database includes information on the properties of sand, fibers, soil-fiber interface, and stress parameters. After data preprocessing, data mining technologies were employed to identify factors influencing shear strength and to predict the peak friction angle of FRS. The analysis techniques included (1) classification and regression methods, i.e., linear regression (REG) analysis, classification and regression tree (CART) analysis, a generalized linear (GENLIN) model, and chi-squared automatic interaction detection (CHAID); (2) machine learners, i.e., artificial neural network (ANN) and support...

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