Volume Expansion Rate Index Reveals the Damage Process of Surrounding Rock: A Machine Learning-Based Effectiveness Evaluation

Jiaqi Wen (), Lei Tang (), Chang Deng, Qibing Zhan and Yukun Wang
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Jiaqi Wen: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210029, China
Lei Tang: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210029, China
Chang Deng: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210029, China
Qibing Zhan: Department of Materials and Structures, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Yukun Wang: Department of Materials and Structures, Nanjing Hydraulic Research Institute, Nanjing 210029, China

Sustainability, 2023, vol. 15, issue 17, 1-25

Abstract: Energy sustainability and the establishment of the ‘national water network’ are all inseparable from the construction of underground engineering. Monitoring indices for the surrounding rock are vital for the safety management of underground engineering construction, which determines the actual state of the surrounding rock. The complexity of deep underground engineering construction leads to many situations that cannot be predicted and explained by existing experience. Therefore, it is necessary to identify which monitoring index best represents the surrounding rock damage. Currently, there are no advanced and convenient effectiveness evaluation schemes for surrounding rock monitoring information. To fill the technical gap, this study introduces the volume expansion rate (VER) index for surrounding rock and proposes a machine learning (ML)-based evaluation scheme for the effectiveness of monitoring indices. First, six conditions with different in situ stresses are designed, and tunnel excavation monitoring tests are conducted. Second, the surrounding rock damage determination experiments using the ML classification algorithm are performed, and the accuracy matrix and index significance scores are obtained. The evaluation results show that: (1) The multi-class logistic regression algorithm is more suitable for determining surrounding rock damage with high accuracy and more appropriate significance evaluation outcomes. (2) Under the higher in situ stress condition, the tangential stress is more sensitive to the surrounding rock damage. (3) As the in situ stress increases, the significant monitoring indices demonstrate a transition ‘from shallow to deep, from regional damage to point failure’, describing the instability of the surrounding rock and inspiring a new instability criterion for surrounding rock.

Keywords: surrounding rock damage determination; surrounding rock monitoring; volume expansion rate; machine learning; effectiveness evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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