Research on Fine Identification Method of Tunnel Geological Digital Drilling Based on Artificial Neural Network

There are a large number of tunnel projects in the construction of national infrastructure. The water and mud inrush disaster of the tunnel seriously limits the construction process. Due to the unclear geological body in front of the tunnel face and the fact that the survey data can not completely and accurately reflect the geological situation in front of the construction, it brings great blindness to the on-site construction. Therefore, unpredictable geological disasters often occur, including sudden collapse, water and mud inrush Rock burst and harmful gas, the engineering disasters caused by tunnel excavation are non selective, diversified, difficult to treat and sudden. In view of the above problems, this paper puts forward the fine identification method of tunnel geological digital drilling, explores the relevant skillful indicators between dip parameters and formation information as the main line, takes the drillability index, drilling energy and drilling parameter data as the core, and based on the setting up of parameter acquisition system and data conversion, so as to realize the identification of formation interface, identification of adverse geological bodies and classification of surrounding rock, It has been f tested and applied in engineering practice, and a series of innovative research results have been obtained. This paper studies how to strengthen the application basis of artificial neural network technology and improve its practicability in system modeling, calculation and prediction, especially in tunnel geological calculation and prediction.