A brief introduction to disaster rock mass mechanics

Rock mass mechanics can be classified into engineering rock mass mechanics and disaster rock mass mechanics based on science and application.Their conception,object,scientific essence and application were elaborated.The connotation,studying method and theoretical framework of disaster rock mass mechanics were described.Disaster rock mass mechanics is a strongly nonlinear discipline which is a strong tool to study natural and artificially-induced disasters.The rock mass system where disasters happen exhibits extremely spatial-temporal nonlinearity in the critically unstable state.Hence,the potentially effective prediction and forecasting of disasters depends on statistical analysis of highly probable events.The direction of efforts for predicting and forecasting disasters could be to find the quantitative or semi-quantitative relationship between physical and biological information and instability of rock mass system.

Debris flow susceptibility mapping in mountainous area based on multi-source data fusion and CNN model-taking Nujiang Prefecture,China as an example

Efforts to evaluate the susceptibility of debris flows in large areas,especially in mountainous regions,are often hampered by the alpine and canyon terrain.This paper proposes a convolution neural network(CNN)model named dense residual shuffle net(DRSNet).It is successfully applied to Nujiang Prefecture in Yunnan Province of China,a typical alpine area with frequent debris flows.DRSNet uses digital elevation model,remote sensing,lithology,soil type and precipitation data as input.First,dense connection and residual structure were used to extract the shallow features of various data.Next,channel shuffle,fuse block and fully connection were applied to strengthen the correlation between different shallow features and give inner danger scores.Finally,precipitation as the activation factor was introduced giving the valleys susceptibility.To verify the feasibility of DRSNet,comparative tests were conducted on 7 CNN models and 3 other machine learning(ML)methods.Experimental results show that DRSNet can achieve 78.6%accuracy in debris flow valley classification,which is at least 7.4%higher than common CNN models and 15.2%higher than other ML methods.This article provides new ideas for debris flow susceptibility evaluation.