Application of automatic interpretation technology of tunnel rock mass integrity based on digital drilling and multi-scale model fusion

LIANG Ming; PENG Hao; XIE Weiwei; HAN Yu; SONG Guanxian; ZHU Menglong; HUANG Nenghao; ZHOU Banghong; LU Zhenlong

doi:10.11779/CJGE20221141

Volume 46 Issue 2

Turn off MathJax

Article Contents

Abstract

References

Supplements

Chinese Journal of Geotechnical Engineering > 2024 > 46(2): 396-405. > DOI: 10.11779/CJGE20221141

LIANG Ming, PENG Hao, XIE Weiwei, HAN Yu, SONG Guanxian, ZHU Menglong, HUANG Nenghao, ZHOU Banghong, LU Zhenlong. Application of automatic interpretation technology of tunnel rock mass integrity based on digital drilling and multi-scale model fusion[J]. Chinese Journal of Geotechnical Engineering, 2024, 46(2): 396-405. DOI: 10.11779/CJGE20221141

Citation:

PDF (3946 KB)

Application of automatic interpretation technology of tunnel rock mass integrity based on digital drilling and multi-scale model fusion

1.
Guangxi Road and Bridge Engineering Group Co., Ltd., Nanning 530000, China
2.
College of Civil Engineering and Architecture, Guangxi University, Nanning 530000, China

More Information

Received Date: September 18, 2022
Available Online: February 05, 2024

Graphical Abstract

Abstract

Abstract

By collecting the multi-lithology and multi-index drilling data, an automatic interpretation technology of tunnel rock mass integrity based on integrated algorithm and multi-scale model fusion is proposed considering comprehensive interpretation accuracy and practical effect. First, the pre-processing such as noise reduction and equidistant segmentations (0.5, 1 and 2 m) is carried out of the raw data to form a multi-scale, high-quality machine learning dataset. Then the operations such as automatic parameter optimization, training, evaluation and interpretability of model are performed to verify the accuracy and reliability. Finally, the weighted average method is used to fuse the multi-scale interpretation results to enhance the engineering practical effect. In addition, in order to facilitate practical engineering applications, a lightweight automatic interpretation platform is developed. The application results of several limestone and sandstone tunnels show that compared with the conventional interpretation, the multi-scale model fusion interpretation has the overall excellent performance in interpretation efficiency and prediction effect. It can provide reliable rock mass integrity information for the excavation and support of tunnel construction.
- tunnel engineering,
- advanced drilling forecast,
- rock mass quality evaluation,
- machine learning,
- model interpretability

FullText(HTML)

References (21)

References

[1]	李术才, 刘斌, 孙怀凤, 等. 隧道施工超前地质预报研究现状及发展趋势[J]. 岩石力学与工程学报, 2014, 33(6): 1090-1113. LI Shucai, LIU Bin, SUN Huaifeng, et al. State of art and trends of advanced geological prediction in tunnel construction[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(6): 1090-1113. (in Chinese)
[2]	晏军. 岩溶隧道超前地质预报几种主要物探方法的选择与实践[J]. 隧道建设(中英文), 2020, 40(增刊1): 327-336. YAN Jun. Selection and application of several main geophysical methods for advanced geological prediction of Karst tunnels[J]. Tunnel Construction, 2020, 40(S1): 327-336. (in Chinese)
[3]	周轮, 李术才, 许振浩, 等. 隧道综合超前地质预报技术及其工程应用[J]. 山东大学学报(工学版), 2017, 47(2): 55-62. ZHOU Lun, LI Shucai, XU Zhenhao, et al. Integrated advanced geological prediction technology of tunnel and its engineering application[J]. Journal of Shandong University (Engineering Science), 2017, 47(2): 55-62. (in Chinese)
[4]	岳中琦. 钻孔过程监测(DPM)对工程岩体质量评价方法的完善与提升[J]. 岩石力学与工程学报, 2014, 33(10): 1977-1996. YUE Zhongqi Drilling process monitoring for refining and upgrading rock mass quality classification methods[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33(10): 1977-1996. (in Chinese)
[5]	PFISTER P. Recording drilling parameters in ground engineering[J]. Ground Engineering, 1985, 18(3): 16-21.
[6]	GUI M, SOGA K, Bolton, et al. Instrumented borehole drilling using ENPASOL system Field Measurements of Geomechanics, 1999.
[7]	WANG Q, GAO H K, JIANG B, et al. In-situ test and bolt-grouting design evaluation method of underground engineering based on digital drilling[J]. International Journal of Rock Mechanics and Mining Sciences, 2021, 138: 104575. doi: 10.1016/j.ijrmms.2020.104575
[8]	梁栋才, 汤华, 吴振君, 等. 基于多钻进参数和概率分类方法的地层识别研究[J]. 岩土力学, 2022, 43(4): 1123-1134. LIANG Dongcai, TANG Hua, WU Zhenjun, et al. Stratum identification based on multiple drilling parameters and probability classification[J]. Rock and Soil Mechanics, 2022, 43(4): 1123-1134. (in Chinese)
[9]	SCHUNNESSON H, FALKSUND H, GUSTAFSON A, et al. Assessment of rock mass quality using drill monitoring technique for hydraulic ITH drills[J]. International Journal of Mining and Mineral Engineering, 2017, 8(3): 169. doi: 10.1504/IJMME.2017.085830
[10]	ELDERT J, SCHUNNESSON H, JOHANSSON D, et al. Application of measurement while drilling technology to predict rock mass quality and rock support for tunnelling[J]. Rock Mechanics and Rock Engineering, 2020, 53(3): 1349-1358. doi: 10.1007/s00603-019-01979-2
[11]	WU Z J, WEI R L, CHU Z F, et al. Real-time rock mass condition prediction with TBM tunneling big data using a novel rock-machine mutual feedback perception method[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2021, 13(6): 1311-1325. doi: 10.1016/j.jrmge.2021.07.012
[12]	房昱纬, 吴振君, 盛谦, 等. 基于超前钻探测试的隧道地层智能识别方法[J]. 岩土力学, 2020, 41(7): 2494-2503. FANG Yuwei, WU Zhenjun, SHENG Qian, et al. Intelligent recognition of tunnel stratum based on advanced drilling tests[J]. Rock and Soil Mechanics, 2020, 41(7): 2494-2503. (in Chinese)
[13]	WANG M N, ZHAO S G, TONG J J, et al. Intelligent classification model of surrounding rock of tunnel using drilling and blasting method[J]. Underground Space, 2021, 6(5): 539-550. doi: 10.1016/j.undsp.2020.10.001
[14]	王志坚. 郑万高铁隧道智能化建造技术研究及展望[J]. 隧道建设(中英文), 2021, 41(11): 1877-1890. WANG Zhijian. Status and prospect of intelligent construction technology of tunnel of Zhengzhou-wanzhou high-speed railway[J]. Tunnel Construction, 2021, 41(11): 1877-1890. (in Chinese)
[15]	LI S C, LIU B, XU X J, et al. An overview of ahead geological prospecting in tunneling[J]. Tunnelling and Underground Space Technology, 2017, 63: 69-94. doi: 10.1016/j.tust.2016.12.011
[16]	CHEN T Q, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, 2016.
[17]	吴贤国, 陈彬, 刘琼, 等. 基于LSSVM和NSGA-Ⅱ混凝土耐久性多目标配合比优化[J]. 隧道建设(中英文), 2020, 40(12): 1691-1699. WU Xianguo, CHEN Bin, LIU Qiong, et al. Optimization of multi-objective mix ratio for concrete durability based on LSSVM and NSGA-Ⅱ[J]. Tunnel Construction, 2020, 40(12): 1691-1699. (in Chinese)
[18]	LUNDBERG S, LEE S I. A unified approach to interpreting model predictions[EB/OL]. 2017: arXiv: 1705.07874. https://arxiv.org/abs/1705.07874
[19]	公路隧道设计规范: JTG 3370.1—2018[S]. 北京: 人民交通出版社, 2018. Specifications for Design of Highway Tunnels: JTG 3370.1—2018[S]. Beijing: China Communications Press, 2018. (in Chinese)
[20]	梁铭, 彭浩, 解威威, 等. 基于超前钻探及优化集成算法的隧道围岩双层质量评价[J]. 隧道建设(中英文), 2022, 42(8): 1443-1452. LIANG Ming, PENG Hao, XIE Weiwei, et al. Double-layer quality evaluation of surrounding rock of a tunnel based on advance drilling and optimized integration algorithm[J]. Tunnel Construction, 2022, 42(8): 1443-1452. (in Chinese)
[21]	陈振宇, 刘金波, 李晨, 等. 基于LSTM与XGBoost组合模型的超短期电力负荷预测[J]. 电网技术, 2020, 44(2): 614-620. CHEN Zhenyu, LIU Jinbo, LI Chen, et al. Ultra short-term power load forecasting based on combined LSTM-XGBoost model[J]. Power System Technology, 2020, 44(2): 614-620. (in Chinese)