Prediction of shear strength parameters of hydrocarbon contaminated sand based on machine learning methods

Abstract

ABSTRACT The objective of this paper is to predict the effect of hydrocarbon contamination on the shear strength parameters of sand by using various machine learning platforms. Multilayer perceptron, support vector machine, random forest, gradient boosting method, and multi-output support vector machine were methods used to predict the hydrocarbon contamination impacts on the internal friction angle and cohesion of contaminated sand. Random forest exhibited the best results for cohesion, whereas, for the friction angle, the gradient boosting method outperformed other approaches. Moreover, the multi-output support vector machine yielded better results than those pertaining to a single support vector machine. Based on the sensitivity analyses, the most influencing parameter for cohesion prediction was the percentage of contamination for all the methods except for random forest where initial cohesion proved to be the primary governing factor, while the least influencing parameter was specific gravity for all methods. For the internal friction angle, the most influencing parameter was the initial friction angle for all methods except for gradient boosting where the percentage of contamination proved to be most controlling, whereas the least influencing factor was also specific gravity for all methods except for random forest where the initial cohesion was the least influencing parameter.