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| Real-time intelligent evaluation and advanced prediction of surrounding rock integrity during mechanized drill-and-blast tunneling based on drilling parameters |
| CHEN Ziquan1, 2, WEI Fangming1, 2, HE Chuan1, 2*, LIN Nengduo1, 2, WANG Bo1, 2, YAO Renjie1, 2 |
(1. State Key Laboratory of Intelligent Geotechnics and Tunnelling, Southwest Jiaotong University, Chengdu, Sichuan 610031, China;
2. Key Laboratory of Transportation Tunnel Engineering of Ministry of Education, Southwest Jiaotong University,
Chengdu, Sichuan 610031, China) |
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Abstract To address the challenges associated with accurately identifying the integrity of tunnel surrounding rock in complex geological environments—specifically issues of high difficulty, insufficient refinement, poor timeliness, and low accuracy of advanced predictions—a three-dimensional refined intelligent identification and dynamic advanced prediction method for assessing the fragmentation degree of tunnel surrounding rock in mechanized drilling and blasting construction has been developed. This method integrates drilling parameters collected by the rock drilling trolley with a deep learning algorithm model, focusing on the construction process：(1) The research findings indicate a significant correlation between drilling parameters and rock mass integrity. Utilizing this relationship, the proposed method for extracting the fluctuation features of drilling parameter data can effectively characterize the development of joints and fissures within the rock mass. Unlike traditional surface observation methods at the tunnel face, this approach employs a spatial interpolation technique for discrete borehole data, enabling a non-uniform, high-density characterization of the surrounding rock’s integrity within the three-dimensional spatial range of the excavation face. (2) Taking into account the spatial distribution characteristics and temporal variation features of the drilling parameters, an intelligent recognition method for the integrity of tunnel surrounding rock using the drilling and blasting technique was constructed based on an attention spatial-temporal fusion network model, achieving an accuracy rate of 91.38%. This represents an improvement of 19.58% compared to the LSTM model, transforming the recognition of rock mass integrity at the tunnel face from a superficial understanding to a “three-dimensional refined dynamic intelligent evaluation.” (3) Based on the spatial continuity and gradual change characteristics of the lithology through which long tunnels pass, an advanced prediction model for the integrity of surrounding rock is proposed, utilizing a bidirectional long short-term memory neural network (Bi-LSTM). This model dynamically predicts the integrity of surrounding rock in the next unexcavated cycle based on the variation patterns of surrounding rock information from the excavated section, achieving an accuracy rate of 88.75%. It provides effective support for evaluating surrounding rock quality, identifying stability, and making dynamic decisions regarding excavation and support in mechanized tunnel construction using the drilling and blasting method.
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2026, Vol. 45 
