Road performance and prediction model for carbonaceous mudstone soil-rock mixtures under wet-dry cycles

The abandoned carbonaceous mudstone has caused severe environmental problems such as land occupation and landslides. For the consideration of economic and ecological factors, carbonaceous mudstone soil-rock mixture (CMSRM) is used as an embankment material assessed by California bearing ratio (CBR) and unconfined compression strength (UCS). A series of experiments were conducted to measure the CBR and UCS of the CMSRM with different wet-dry cycles (0, 2, 4, 6 and 8) and different rock contents (0, 20, 40, 60 and 80%). The experimental results were predicted and analysed by a convolutional neural network (CNN). The experiment results show that the CBR and UCS of CMSRM increased at first and then decreased with the increase of rock content and were negatively correlated with wet-dry cycles. The CNN predicted values were highly correlated with the measured values. The CNN model enables variable parameter analysis of the experiment results via deep learning, which provides a new method to the CMSRM embankment road performance prediction.