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

AbstractThe 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.KEYWORDS: Carbonaceous mudstonewet-dry cyclesCalifornia bearing ratiounconfined compressive strengthconvolution neural networkprediction model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe authors gratefully acknowledge the financial support offered by the National Natural Science Foundation of China (52078066, 52004036, 42207204, 52378440), the Postgraduate Scientific Research Innovation Project of Hunan Province (CX20210738), the Changsha City Outstanding Innovative Youth Training Program (kq1905043), the Hunan young scientific and technological innovation talents (2020RC306), the Natural Science Foundation of Hunan Province Outstanding Youth Fund Project (2023JJ10045), the ‘Double First-class’ International Cooperation project of Changsha University of Science and Technology (2019IC04), the 2021 Bridge Engineering Safety Control Key Laboratory of Ministry of Education Open Fund Project (21KB12), the National Natural Science Foundation of Hunan Province Projects (2021JJ40572), the Open Fund of Key Laboratory of Bridge Engineering Safety Control by Department of Education (Changsha University of Science & Technology) (15KB01) and the Research Foundation of Education Bureau of Hunan Province Project (20B040).