Incorporating smart computer vision and in-drilling information into rock quality evaluation via incomplete data-driven Bayesian networks

Abstract

Tunnelling is a challenging task due to a lack of full understanding of the surrounding rock quality. This study proposes a solution driven by a refined computer vision (CV) method, complemented by rock mass drilling tests and Bayesian networks, to address this issue through a multi-source heterogeneous data approach. Initially, improvements are made to the popular Swin Transformer to improve the recognition and segmentation of intricate rock features. Notably, refined smart CV, owing to its U-shaped architecture and smart window self-attention computation, exhibits segmentation performance superior to that of conventional CV methods such as Swin Transformer, Deeplab V3+, and UNet. Building upon the segmentation outcomes of the refined CV, a parameter set comprising apparent rock parameters is established. Then, two datasets encompassing rock internal drilling parameters and mechanics, as well as design parameters, are curated. The combination of the aforementioned parameter sets is referred to as the rock quality comprehensive evaluation dataset. However, analysis reveals data incompleteness issues within these datasets. To mitigate this problem, a novel tree-augmented Bayesian network is designed, and a prediction accuracy of 91% is realized, surpassing popular decision trees, ensemble learning, and deep learning methods. Furthermore, evaluation services are provided in mountain and submarine tunnels, suggesting that drilling parameters significantly enhance the evaluation performance. Moreover, employing two sensitivity analysis metrics underscores the prominent influence of rotating pressure and drilling speed parameters. This study endeavor presents diverse solutions for achieving precise and expeditious predictions of rock quality through various parameter sets, tailored to cater to diverse requirements of tunnels.

Keywords

Rock mass quality / Smart computer vision / Drilling parameters / Incomplete datasets / Tree augmented naive Bayesian

Data availability

The data employed to drive the TAN BN can be obtained from the following link: https://github.com/BluesOctopus/Data-driven-Bayesian-networks-Data-V1-.git. And the code of the applied visual method is as follows: https://github.com/suofer/Smart-Swin-Transforme.

CRediT authorship contribution statement

Chen Wu: Writing - review & editing, Writing - original draft, Formal analysis, Methodology, Software. Minglun Tan: Funding acquisition, Project administration, Data curation. Yue Tong: Funding acquisition, Data curation. Hongwei Huang: Writing - review & editing, Project administration, Formal analysis, Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The work described in this paper is supported by the National Natural Science Foundation of China (Grant No. 52279107), Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co., Ltd., Academician and Expert Workstation of Yunnan Province (Grant No. 202205AF150015), and the Science and Technology Innovation Project of YCIC Group Co., Ltd. (Grant No. YCIC-YF-2022-15)

References

[1] Baghbani, A., Choudhury, T., Costa, S., & Reiner, J. (2022). Application of artificial intelligence in geotechnical engineering : A state-of-the-art review. Earth-Science Reviews, 228, 103991.

[2] Barnard, C., Kallu Raj, R., Warren, S., & Thareja, R. (2016). Inflatable rock bolt bond strength versus rock mass rating (RMR): A comparative analysis of pull-out testing data from underground mines in Nevada. International Journal of Mining Science and Technology, 26 (1), 19-22.

[3] Barton, N., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock Mechanics and Rock Engineering, 6 (4), 189-236.

[4] Borsuk, M. E., Stow, C. A., & Reckhow, K. H. (2004). A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling, 173 (2/3), 219-239.

[5] Chen, C. S., & Liu, Y. C. (2007). A methodology for evaluation and classification of rock mass quality on tunnel engineering. Tunnelling and Underground Space Technology, 22 (4), 377-387.

[6] Chen, J., & Yue, Z. Q. (2016). Weak zone characterization using full drilling analysis of rotary-percussive instrumented drilling. International Journal of Rock Mechanics and Mining Sciences, 89, 227-234.

[7] Chen, J. Q., Li, X. J., Zhu, H. H., & Rubin, Y. (2017). Geostatistical method for inferring RMR ahead of tunnel face excavation using dynamically exposed geological information. Engineering Geology, 228, 214-223.

[8] Chen, J. Y., Huang, H. W., Cohn, A. G., Zhang, D. M., & Zhou, M. L. (2022). Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning. International Journal of Mining Science and Technology, 32 (2), 309-322.

[9] Chen, J. Y., Zhang, D. M., Huang, H. W., Shadabfar, M., Zhou, M. L., & Yang, T. J. (2020). Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning. Automation in Construction, 120, 103371.

[10] Chen, J. Y., Zhou, M. L., Huang, H. W., Zhang, D. M., & Peng, Z. C. (2021a). Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning. International Journal of Rock Mechanics and Mining Sciences, 142, 104745.

[11] Chen, J.N., Lu, Y.Y, & Yu, Q. H. (2021b). TransUNet: transformers make strong encoders for medical image segmentation. https://arxiv.org/abs/2102.04306v1.

[12] Dempster, A. P. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39 (1), 1-22.

[13] Feng, S. X., Chen, Z. Y., Luo, H., Wang, S. Y., Zhao, Y. F., Liu, L. P., Ling, D. S., & Jing, L. J. (2021). Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning. Tunnelling and Underground Space Technology, 110, 103636.

[14] Feng, X. D., & Jimenez, R. (2015). Predicting tunnel squeezing with incomplete data using Bayesian networks. Engineering Geology, 195, 214-224.

[15] Fu, L. Y., Chen, Y. Z., Ji, W., & Yang, F. (2024). SSTrans-Net: Smart swin transformer network for medical image segmentation. Biomedical Signal Processing and Control, 91, 106071.

[16] Gong, F. Q., Li, X. B., & Zhang, W. (2008). Over-excavation forecast of underground opening by using Bayes discriminant analysis method. Journal of Central South University of Technology, 15 (4), 498-502.

[17] Guo, P., Bu, M., Zhang, P., Li, Q., & He, M. (2023). Review on catastrophe mechanism and disaster countermeasure of high geotemperature tunnels. Chinese Journal of Rock Mechanics and Engineering, 42, 1561-1581 (in Chinese).

[18] He, X., Zhou, Y., & Zhao, J. (2022). Swin transformer embedding UNet for remote sensing image semantic segmentation. IEEE Transactions on Geoscience and Remote Sensing, 60, 4408715.

[19] Hou, S., Liu, Y., & Yang, Q. (2022). Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning. Journal of Rock Mechanics and Geotechnical Engineering, 14 (1), 123-143.

[20] Huang, H., Wu, C., Zhou, M., Chen, J., Han, T., & Zhang, L. (2024). Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network. International Journal of Mining Science and Technology, 34 (3), 323-337.

[21] Huang, M. Q., Ninic, J., & Zhang, Q. B. (2021). BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives. Tunnelling and Underground Space Technology, 108, 103677.

[22] Huang, Z., Liao, M. X., Zhang, H. L., Zhang, J. B., Ma, S. K., & Zhu, Q. X. (2022). Predicting tunnel squeezing using the SVM-BP combination model. Geotechnical and Geological Engineering, 40, 1387-1405.

[23] Ismail, M. A. M., Majid, T. A., Goh, C. O., Lim, S. P., & Tan, C. G. (2019). Geological assessment for tunnel excavation under river with shallow overburden using surface site investigation data and electrical resistivity tomography. Measurement, 144, 260-274.

[24] Kalantari, S., Baghbanan, A., & Hashemalhosseini, H. (2019). An analytical model for estimating rock strength parameters from small-scale drilling data. Journal of Rock Mechanics and Geotechnical Engineering, 11 (1), 135-145.

[25] Kalantari, S., Hashemolhosseini, H., & Baghbanan, A. (2018). Estimating rock strength parameters using drilling data. International Journal of Rock Mechanics and Mining Sciences, 104, 45-52.

[26] Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society: Series B (Methodological), 50 (2), 157-194.

[27] Li, N., Feng, X., & Jimenez, R. (2017a). Predicting rock burst hazard with incomplete data using Bayesian networks. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, 61, 61-70.

[28] Li, N., Jimenez, R., & Feng, X. D. (2017b). The influence of bayesian networks structure on rock burst hazard prediction with incomplete data. In ISRM European Rock Mechanics Symposium (EUROCK) (pp. 206-214). Ostrava: The Czech Republic.

[29] Li, S. C., Liu, B., Xu, X. J., Nie, L. C., Liu, Z. Y., Song, J., Sun, H. F., Chen, L., & Fan, K. R. (2017c). An overview of ahead geological prospecting in tunneling. Tunnelling and Underground Space Technology, 63, 69-94.

[30] Lin, A. L., Chen, B. Z., Xu, J. Y., Zhang, Z., Lu, G. M., & Zhang, D. (2022). DS-TransUNet: Dual swin transformer U-net for medical image segmentation. IEEE Transactions on Instrumentation and Measurement, 71, 4005615.

[31] Liu, W. L., Shao, Y. X., Li, C., Li, C. Q., & Jiang, Z. H. (2023). Development of a non-Gaussian copula Bayesian network for safety assessment of metro tunnel maintenance. Reliability Engineering & System Safety, 238, 109423.

[32] Liu, Z., Lin, Y. T., Cao, Y., Hu, H., Wei, Y. X., Zhang, Z., Lin, S., & Guo, B. N. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the 18th IEEE/CVF International Conference on Computer Vision (ICCV), IEEE Network (pp. 9992-10002).

[33] Loozen, G., Ozcelik, O., Boon, N., De Mol, A., Schoen, C., Quirynen, M., & Teughels, W. (2013). Inter-bacterial correlations in subgingival biofilms: A large-scale survey. Journal of Clinical Periodontology, 41 (1), 1-10.

[34] Mohammadi, S. D., Naseri, F., & Alipoor, S. (2015). Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran. Bulletin of Engineering Geology and the Environment, 74 (3), 827-843.

[35] Ou, X. D., Wu, Y. F., Wu, B., Jiang, J., & Qiu, W. X. (2022). Dynamic Bayesian network for predicting tunnel-collapse risk in the case of incomplete data. Journal of Performance of Constructed Facilities, 36 (4), 04022034.

[36] Palmstrom, A. (1996). Characterizing rock masses by the RMi for use in practical rock engineering: Part 1: The development of the Rock Mass index (RMi). Tunnelling and Underground Space Technology, 11 (2), 175-188.

[37] Patel, A. K., & Chatterjee, S. (2016). Computer vision-based limestone rock-type classification using probabilistic neural network. Geoscience Frontiers, 7 (1), 53-60.

[38] Qin, S. J., Qi, T. Y., Deng, T., & Huang, X. D. (2024). Image segmentation using Vision Transformer for tunnel defect assessment. Computer-Aided Civil and Infrastructure Engineering, 39 (21), 3243-3268.

[39] Špačková O., & Straub, D. (2013). Dynamic Bayesian network for probabilistic modeling of tunnel excavation processes. Computer-Aided Civil and Infrastructure Engineering, 28 (1), 1-21.

[40] Sugawara, J., Yue, Z. Q., Tham, L. G., Law, K. T., & Lee, C. F. (2003). Weathered rock characterization using drilling parameters. Canadian Geotechnical Journal, 40 (3), 661-668.

[41] Tu, W. F., Li, L. P., Li, S. C., Shi, S. S., Zhou, Z. Q., & Chen, D. Y. (2019). Research on the application of dynamic weighting on the rock mass quality rating. Arabian Journal of Geosciences, 12 (3), 87.

[42] Wang, Q., Gao, H. K., Jiang, B., Li, S. C., He, M. C., & Qin, Q. (2021). In-situ test and bolt-grouting design evaluation method of underground engineering based on digital drilling. International Journal of Rock Mechanics and Mining Sciences, 138, 104575.

[43] Wu, C., Huang, H. W., Zhang, L., Chen, J. Y., Tong, Y., & Zhou, M. L. (2023). Towards automated 3D evaluation of water leakage on a tunnel face via improved GAN and self-attention DL model. Tunnelling and Underground Space Technology, 142, 105423.

[44] Yue, Z. Q., Lee, C. F., Law, K. T., & Tham, L. G. (2004). Automatic monitoring of rotary-percussive drilling for ground characterization—illustrated by a case example in Hong Kong. International Journal of Rock Mechanics and Mining Sciences, 41 (4), 573-612.

[45] Zhou, M., Chen, J., Huang, H., Zhang, D., Zhao, S., & Shadabfar, M. (2021). Multi-source data driven method for assessing the rock mass quality of a NATM tunnel face via hybrid ensemble learning models. International Journal of Rock Mechanics and Mining Sciences, 147, 104914.

[46] Zhou, Z., Zhang, J. J., & Gong, C. J. (2023). Hybrid semantic segmentation for tunnel lining cracks based on Swin Transformer and convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 38 (17), 2491-2510.