Prediction of Sandstone Dilatancy Point in Different Water Contents Using Infrared Radiation Characteristic: Experimental and Machine Learning Approaches

In rock mechanics, the dilatancy point is always occurring before rock failure during loading process. Water content plays a significant role in the rock physiomechanical properties, which also impact the rock dilatancy point under loading process. This dilatancy point significantly plays a warning role in the rock engineering structures stability. Therefore, it is essential to predict the rock dilatancy point under different water contents to get an early warning for effective monitoring of engineering projects. This study investigates the water contents effects on sandstone dilatancy point under loading in the presence of infrared radiation (IR). Furthermore, this IR was used for the first time as an input parameter for different artificial intelligence (AI) techniques to predict the dilatancy point in the stress-strain curve. The experimental findings show that the stress range in stress-strain curve stages (crack closure and unstable crack propagation) increases with water content. However, this range for deformation and stable crack propagation stages decreases with water content. The dilatancy stress, crack initiation stress, and elastic modulus are negatively linearly correlated, while peak stress and stress level are negatively quadraticaly correlated with a high (R-2). The absolute strain energy rate, which gives a sudden increase at the point of dilatancy, is used as the dilatancy point index. The stress level is 0.86 sigma(max) at the dilatancy point for dry rock and decreases with water content. This index is predicted from IR data using three computing techniques: artificial neural network (ANN), random forest regression (RFR), and k-nearest neighbor (KNN). The performance of all techniques was evaluated using R-2 and root-means-square error (RMSE). The results of the predicted models show satisfactory performances for all, but KNN is remarkable. The research findings will be helpful and provide guidelines about underground engineering project stability evaluation in water environments.

Automated summary

In rock mechanics, the dilatancy point, which occurs before rock failure, is influenced by water content and plays a crucial role in assessing the stability of rock engineering structures. This study examines the impact of water content on the dilatancy point in sandstone under loading, utilizing infrared radiation as an input parameter for artificial intelligence techniques. The experimental findings indicate that water content affects the stress-strain curve stages, and the dilatancy point can be predicted using computational techniques such as artificial neural network, random forest regression, and k-nearest neighbor.