Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine (CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering. This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters. The uniaxial compressive strength (UCS), rock integrity factor (Kv), basic quality index ([BQ]), rock quality index RQD, brazilian tensile strength (BTS) and brittleness index (BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3, and then established the performance prediction model of cantilever boring machine. Then the deep belief network (DBN) was introduced into the performance prediction model, and the reliability of performance prediction model was verified by combining with engineering data. The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS > RQD > K v > BI. The performance prediction model shows that the instantaneous cutting rate (ICR) has a good correlation with the surrounding rock parameters, and the predicting model accuracy is related to the reliability of construction data. The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable. The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.