Abstract: Aiming at the problem that the development of fissures in the rock mass of grotto temple is slow and the influencing factors are diverse, it is difficult to predict the development of fissures. A new prediction network for the development of fissures in rock mass based on deep learning is proposed. It is a hybrid network with parallel self-attention mechanism. It models temporal correlations through local convolution modules and global recurrent modules to capture temporal patterns at different time scales accurately. Self-attention mechanism is introduced to model the complex dependencies between different sequences in multivariate time series data. To further improve the robustness of the model, traditional autoregressive processing is followed. We constructed the first dataset in this field based on the monitoring data of fissure development-related factors in Cave No. 32 of North Grotto Temple in Q City. Comparative experiments on this dataset show that the proposed model has a better performance in fracture development prediction of grotto rock mass.

Key words: rock fissure development,  , multivariate time series data forecast,  , self-attention mechanism,  , rock mass fissure dataset