Investigating Correlation of Physico-Mechanical Parameters and P-Wave Velocity of Rocks: a Comparative Intelligent Study

The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, achieving a high-accurate method to estimate PWV is a very important task. This work investigates the use of different intelligent models such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), group method of data handling (GMDH), and gene expression programming (GEP) for the prediction of PWV. The proposed models are then evaluated using several error statistics, i.e. squared correlation coefficient (R۲) and root mean squared error (RMSE). The values of R۲ obtained from the CART, MARS, GMDH, and GEP models are ۰.۹۸۳, ۰.۹۹۹, ۰.۹۹۵, and ۰.۹۹۸, respectively. Furthermore, the CART, MARS, GMDH, and GEP models predict PWV with the RMSE values of ۰.۰۳۷, ۰.۰۰۷, ۰.۰۲۳, and ۰.۰۲۰, respectively. According to the aforementioned amounts, the models presented in this work predict PWV with a good performance. Nevertheless, the results obtained reveal that the MARS model yields a better prediction in comparison to the GEP, GMDH, and CART models. Accordingly, MARS can be offered as an accurate model for predicting the aims in other rock mechanics and geotechnical fields.   

کلیدواژه ها:

P-wave velocity ، Artificial intelligence ، Prediction models ، Multivariate adaptive regression splines