Cohesion and internal friction angle are critical parameters for evaluating the suitability of stone. To build a reliable model to predict the cohesion and internal friction angle of rock, dataset containing 597 rock samples were collected and their petrological characteristics were investigated. In this study, artificial neural network (ANN) and particle swarm optimisation (PSO) algorithm are hybridised to establish a new hybrid machine learning (ML) model for predicting cohesion and internal friction angle based on petrological features. By comparing other five ML models, the efficiency of t...
