Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Feature Selection Model

Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index (BI). In addition, the bootstrap aggregate (Bagged) regression tree (BRT) and two efficient lazy machine learning approaches, namely local weighted linear regression (LWLR) and KStar approach, were examined to validate the LGP model. To the best of our knowledge, this is the first attempt to estimate the BI through the LGP model. A tunneling project in Pahang state, Malaysia, was investigated, and the requirement datasets were measured to construct the proposed models. According to the results from the testing phase, the LGP model yielded the best statistical indicators (R = 0.9529, RMSE = 0.4838, and I-A = 0.9744) for modeling BI, followed by LWLR (R = 0.9490, RMSE = 0.6607, and I-A = 0.9400), BRT (R = 0.9433, RMSE = 0.6875, and I-A = 0.9324), and KStar (R = 0.9310, RMSE = 0.7933, and I-A = 0.9095), respectively. In addition, the sensitivity analysis demonstrated that the dry density factor demonstrated the most effective prediction of BI.

Automated summary

This study proposes a linear genetic programming (LGP) model for estimating brittleness index (BI) in deep underground projects and validates it using local weighted linear regression (LWLR) and KStar approaches. The results show that the LGP model outperforms other methods in estimating BI.