Prediction of UCS and BTS under freeze-thaw conditions in the NW himalayan rock mass using petrographic analysis and laboratory testing

Abstract

Repeated freeze-thaw (F&T) cycles substantially harm the durability of rocks, heightening the potential for landslides, rockslides, and avalanches. The current work investigates the effect of the F&T cycle on rock mass (biotite schist) samples. For this purpose, 32 rock samples were prepared and gathered from eight distinct locations in the northwest Himalayan region. For each sample, petrographical analysis and laboratory testing such as uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) are investigated at repeated (0th, 10th, 20th, and 30th) F&T cycles. Additionally, machine learning (ML) sequential models such as recurrent neural networks (RNN), gated recurrent units (GRU), and bi-directional long short-term memory (Bi-LSTM) are constructed to estimate the UCS and BTS under F&T conditions. Petrographical results show no change in the mineral indices, while there is a noticeable increase in aspect ratio but a significant decline in mean grain size with each successive 10th cycle, suggesting sample damage. The study also provides a comprehensive assessment of the ML models' performance, highlighting the Bi-LSTM model's superior accuracy among all models in terms of R2 (0.9850) and RMSLE (0.0100) during the TR stage and R2 (0.9020) and RMSLE (0.0170) during the TS stage for UCS prediction. Similarly, BTS prediction also shows superior precision, recording an R2 (0.7543) and RMSLE (0.0345) during TR and R2 (0.7404) and RMSLE (0.0213) during TS stages. The present study also explores the heatmap, line diagram, regression analysis, 2D kernel density plot, Taylor diagram, and DDR criterion for evaluating the model performance more clearly.