Intelligent Identification Methods for Stratigraphic Structure and Rock Mass Shear Strength Parameters in Mining Rock Slopes Using Drilling Data
Year: 2024
Type: article
Abstract: Objective Accurately identifying stratigraphic structures and shear strength parameters in rock is crucial for ensuring the safety and stability of rock slopes, especially within the context of open-pit mining operations. China, known for its extensive mineral resources and diverse mining activities, places high importance on the safe production of its numerous open-pit mines. The extraction process in these mines generates large volumes of high-steep rock slopes, whose stability is essential for mining safety. Analysis of slope stability requires an understanding of the slope’s structure, the distribution of structural planes, and groundwater distribution along with the precise determination of the shear strength parameters of rock. However, the inherent complexity of natural rock makes it challenging to determine these parameters, a persistent issue in rock mechanics. Additionally, the dynamic nature of open-pit mining means that slope structures continually evolve, and controlling factors alter accordingly. Therefore, dynamically and accurately obtaining the shear strength parameters of rock at various slope locations is paramount. The measurement while drilling (MWD) technique, which installs multiple sensors on a drilling rig to record in-situ drilling parameters in real time, facilitates the inversion of lithology and shear strength parameters of strata. Daily drilling of numerous blasting holes for blasting and extraction produces extensive drilling data in mining operations. Utilizing this data to reflect the shear strength parameters of rock in real time and accurately fulfills the dynamic and in-situ requirements for these parameters, ensuring timely optimization of rock slope bench design and regionalized fine monitoring of slopes. This approach aids in developing more automated, digitized, and intelligent safe mines. However, current research on geological lithology and shear strength parameters inversion based on MWD heavily relies on manual interpretation of depth sequence downhole data from existing geological information or core drilling results, which is difficult to apply widely due to significant human influence on geological information. This reliance poses a risk of missing some weak layers and failing to meet the intelligent sensing and prediction requirements. In addition, monitoring downhole parameters such as thrust and torque relies on indirect hydraulic pressure monitoring, resulting in data that cannot be directly utilized to construct a downhole identification model for rock shear strength parameters in standard units.Methods This study establishes a model for identifying shear strength parameters during drilling using a non-dimensional drillability index. This model allows for the direct recognition of rock shear strength parameters without the need for calibration or conversion of MWD test results. The study uses unsupervised learning and an autonomously designed decision module to conduct intelligent identification and automatic smoothing of depth sequence drilling data. It intelligently identifies geological structures and quantitatively describes each rock layer’s drilling and shear strength parameters. The blasting drill holes in the Heqing Beiya open-pit mine limestone area and the F6-1 fault junction area in Yunnan were monitored employing a self-developed high-precision on-site MWD test apparatus. The comprehensive monitoring results from two representative boreholes demonstrate the application of the intelligent drilling identification method throughout the entire drilling process.Results and Discussions The intelligent drilling identification method accurately identified various rock layers with differing shear strength parameters, cohesion (C), and internal friction angle (ϕ) in the ZK1 and ZK2 boreholes. This included fractured weak rock layers, clayey limestone, and dolomitic limestone layers. Specifically, the initial geological profile of the ZK1 borehole consisted of clayey limestone (ϕ: 38.20°, C: 205.25 kPa) and dolomitic limestone (ϕ: 38.96°, C: 224.83 kPa). The intelligent drilling identification method further pinpointed fractured weak rock layers (ϕ: 35.27°～38.02°, C: 139.82～199.95 kPa), additional clayey limestone layers (ϕ: 37.78°～38.52°, C: 194.39～213.61 kPa), dolomitic limestone layers (ϕ: 38.77°～39.01°, C: 219.81～226.12 kPa), and other rock layers (ϕ: 38.99°～40.13°, C: 227.20～259.44 kPa). Similarly, the original geological profile of the ZK2 borehole included clayey limestone (ϕ: 38.67°, C: 220.84 kPa) and dolomitic limestone (ϕ: 39.22°, C: 232.80 kPa). The method also detected fractured weak rock layers (ϕ: 37.64°, C: 191.85 kPa), clayey limestone layers (ϕ: 38.67°, C: 220.00 kPa), and dolomitic limestone layers (ϕ: 39.38°, C: 236.61 kPa). This method confirmed the presence of layers noted in the original geological profiles and revealed additional rock layers with varying shear strength parameters that were not initially identified. Borehole imaging validated these findings, showing the method’s high accuracy and significant practical value in identifying critical layers absent from original geological profiles. This alignment with large-scale geological profile results provides solid validation for identifying fractured weak rock layers and their strength parameters, enhancing the accuracy of geological model construction. In addition, comparing the shear strength parameters C and ϕ from the initial geological profiles with those identified through this method shows significant variations in the ZK1 borehole, with maximum differences of 65.43 kPa for C and 2.93° for ϕ. In the ZK2 borehole, the maximum differences were 28.99 kPa for C and 1.03° for the internal friction angle. These discrepancies underscore the potential risks associated with inaccuracies in assessing rock mechanics parameters, which can undermine the stability of rock slopes.Conclusions The development and application of the intelligent drilling identification method represent a significant advancement in rock and soil engineering. This method enhances the ability to maintain slope stability in open-pit mines, augmenting the safety and sustainability of mining operations by providing a mechanism to accurately and dynamically evaluate the shear strength parameters of rock layers. Its practical deployment demonstrates the viability of this approach and underscores its potential to transform traditional practices in geological modeling and in-situ testing of shear strength parameters.
Authors: Xu CHENG, Hua TANG, Zhenjun WU, Yonghui ZHANG, Hui QIN, Teng WANG, Zeqing JIA, Yuwei FANG
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7001183670
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