Exploring relationships between mechanical properties of marl core samples by a coupling of mutual information and predictive ensemble model

Inappropriate evaluation of uniaxial compression indexes (E and UCS) of rocks in high seismic intensity areas such as dam regions can lead to underestimation of the load, and possible settlement of the structure. Indirect assessments of these rock mechanical indexes based on non-destructive experiments and by using intelligent models is a well-accepted method to overcome associated limitations with laboratory tests of E and UCS. This study introduces the mutual information (MI) method as a unique system for variable importance measurement (VIM) and feature selection. Conducting MI-VIM assessments between various analyses of marl core samples (depth, density, ultrasonic tests (nu(d), V-p and V-s), Brazilian test (sigma t), triaxial compression test (C and and phi) and point load test (I-s(50)) indicated that V-s and sigma t had the highest importance for E and UCS prediction. adaptive boosting-neural network ensemble (Adaboost-NNE) was used for the prediction of E and UCS. Testing of the generated Adaboost-NNE indicated that this model could accurately predict UCS and E with correlations of determinations 0.98 and 0.92, respectively. These results showed that VIM of MI coupled with Adaboost-NNE could develop a robust model that can be used for the prediction and modeling of other indexes of rocks.