Hard-rock tunnel lithology identification using multi-scale dilated convolutional attention network based on tunnel face images

Abstract

For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for in situ rock lithology classification utilizing geological images of the tunnel face.

Graphical abstract

Keywords

hard-rock tunnel face / intelligent lithology identification / multi-scale dilated convolutional attention network / image classification / deep learning

References

[1] Xu Z H, Liu F M, Lin P, Shao R Q, Shi X S. Non-destructive, in-situ, fast identification of adverse geology in tunnels based on anomalies analysis of element content. Tunnelling and Underground Space Technology, 2021, 118: 104146

[2] Xu Z H, Ma W, Lin P, Hua Y L. Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 14(4): 1140–1152

[3] Liu Z B, Li L, Fang X L, Qi W B, Shen J M, Zhou H Y, Zhang Y L. Hard-rock tunnel lithology prediction with TBM construction Big Data using a global-attention-mechanism-based LSTM network. Automation in Construction, 2021, 125: 103647

[4] Xu Z H, Shi H, Lin P, Liu T H. Integrated lithology identification based on images and elemental data from rocks. Journal of Petroleum Science Engineering, 2021, 205: 108853

[5] Xu Z H, Wang W Y, Lin P, Nie L C, Wu J, Li Z M. Hard-rock TBM jamming subject to adverse geological conditions: Influencing factor, hazard mode and a case study of Gaoligongshan Tunnel. Tunnelling and Underground Space Technology, 2021, 108: 103683

[6] Ren D J, Shen S L, Arulrajah A, Cheng W C. Prediction model of TBM disc cutter wear during tunnelling in heterogeneous ground. Rock Mechanics and Rock Engineering, 2018, 51(11): 3599–3611

[7] Kanik M. Evaluation of the limitations of RMR89 system for preliminary support selection in weak rock class. Computers and Geotechnics, 2019, 115: 103159

[8] Peng R, Meng X R, Zhao G M, Ouyang Z H, Li Y M. Multi-echelon support method to limit asymmetry instability in different lithology roadways under high ground stress. Tunnelling and Underground Space Technology, 2021, 108: 103681

[9] Ayawah P E A, Sebbeh-Newton S, Azure J W A, Kaba A G A, Anani A, Bansah S, Zabidi H. A review and case study of artificial intelligence and machine learning methods used for ground condition prediction ahead of tunnel boring machines. Tunnelling and Underground Space Technology, 2022, 125: 104497

[10] de Miguel-García E, Gómez-González J F. A new methodology to estimate the powder factor of explosives considering the different lithologies of volcanic lands: A case study from the island of Tenerife, Spain. Tunnelling and Underground Space Technology, 2019, 91: 103023

[11] Bi L, Ren B Y, Zhong D H, Hu L X. Real-time construction schedule analysis of long-distance diversion tunnels based on lithological predictions using a Markov process. Journal of Construction Engineering and Management, 2015, 141(2): 04014076

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

[13] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507

[14] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84–90

[15] SzegedyCLiu WJiaY QSermanetPReedS AnguelovDErhan DVanhouckeVRabinovichA. Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA: IEEE, 2015, 1–9

[16] SimonyanKZisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR). San Diego, CA: ICLR, 2015

[17] HeK MZhang X YRenS QSunJ. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE, 2016, 770–778

[18] Lei M F, Liu L H, Shi C H, Tan Y, Lin Y X, Wang W D. A novel tunnel-lining crack recognition system based on digital image technology. Tunnelling and Underground Space Technology, 2021, 108: 103724

[19] SunX HShi C HLiuL HLeiM F. Concrete crack image recognition system based on improved seed filling algorithm. Journal of South China University of Technology (Natural Science Edition), 2022, 50(5): 127–136, 146 (in Chinese)

[20] Tinoco J, Gomes Correia A, Cortez P. Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Computers and Geotechnics, 2014, 55: 132–140

[21] Makasis N, Narsilio G A, Bidarmaghz A. A machine learning approach to energy pile design. Computers and Geotechnics, 2018, 97: 189–203

[22] Han X L, Jiang N J, Yang Y F, Choi J, Singh D N, Beta P, Du Y J, Wang Y J. Deep learning based approach for the instance segmentation of clayey soil desiccation cracks. Computers and Geotechnics, 2022, 146: 104733

[23] Zhang W G, Li H R, Li Y Q, Liu H L, Chen Y M, Ding X M. Application of deep learning algorithms in geotechnical engineering: A short critical review. Artificial Intelligence Review, 2021, 54(8): 5633–5673

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

[25] Bai X D, Cheng W C, Sheil B B, Li G. Pipejacking clogging detection in soft alluvial deposits using machine learning algorithms. Tunnelling and Underground Space Technology, 2021, 113: 103908

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

[27] Wang Z F, Cheng W C. Predicting jet-grout column diameter to mitigate the environmental impact using an artificial intelligence algorithm. Underground Space, 2021, 6(3): 267–280

[28] Hu A F, Li T, Chen Y, Ge H B, Li Y J. Deep learning for preprocessing of measured settlement data. Journal of Hunan University (Natural Sciences), 2021, 48(9): 43–51

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

[30] Xu Z H, Ma W, Lin P, Shi H, Pan D D, Liu T H. Deep learning of rock images for intelligent lithology identification. Computers & Geosciences, 2021, 154: 104799

[31] Cai Y Y, Xu D G, Shi H. Rapid identification of ore minerals using multi-scale dilated convolutional attention network associated with portable Raman spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, 267: 120607

[32] Cao Z M, Yang C, Han J, Mu H W, Wan C, Gao P. Lithology identification method based on integrated K-means clustering and meta-object representation. Arabian Journal of Geosciences, 2022, 15(17): 1462

[33] Fu D, Su C, Wang W J, Yuan R Y. Deep learning based lithology classification of drill core images. PLoS One, 2022, 17(7): e0270826

[34] Li N, Hao H Z, Gu Q, Wang D R, Hu X M. A transfer learning method for automatic identification of sandstone microscopic images. Computers & Geosciences, 2017, 103: 111–121

[35] Polat Ö, Polat A, Ekici T. Automatic classification of volcanic rocks from thin section images using transfer learning networks. Neural Computing & Applications, 2021, 33(18): 11531–11540

[36] Seo W, Kim Y, Sim H, Song Y, Yun T S. Classification of igneous rocks from petrographic thin section images using convolutional neural network. Earth Science Informatics, 2022, 15(2): 1297–1307

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

[38] Chen J Y, Chen Y F, Cohn A G, Huang H W, Man J H, Wei L J. A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 14(4): 1077–1088

[39] Xue Y D, Cao Y P, Zhou M L, Zhang F, Shen K, Jia F. Rock mass fracture maps prediction based on spatiotemporal image sequence modeling. Computer-Aided Civil and Infrastructure Engineering, 2023, 38(4): 470–488

[40] Chen J Y, Yang T J, Zhang D M, Huang H W, Tian Y. Deep learning based classification of rock structure of tunnel face. Geoscience Frontiers, 2021, 12(1): 395–404

[41] Qiao W D, Zhao Y F, Xu Y, Lei Y M, Wang Y J, Yu S, Li H. Deep learning-based pixel-level rock fragment recognition during tunnel excavation using instance segmentation model. Tunnelling and Underground Space Technology, 2021, 115: 104072

[42] Cheng W C, Bai X D, Sheil B B, Li G, Wang F. Identifying characteristics of pipejacking parameters to assess geological conditions using optimisation algorithm-based support vector machines. Tunnelling and Underground Space Technology, 2020, 106: 103592

[43] Chen J Y, Zhou M L, Zhang D M, Huang H W, Zhang F S. Quantification of water inflow in rock tunnel faces via convolutional neural network approach. Automation in Construction, 2021, 123: 103526

[44] Chen J Y, Huang H W, Cohn A G, Zhou M L, Zhang D M, Man J H. A hierarchical DCNN-based approach for classifying imbalanced water inflow in rock tunnel faces. Tunnelling and Underground Space Technology, 2022, 122: 104399

[45] Jalalifar H, Mojedifar S, Sahebi A A, Nezamabadi-pour H. Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Computers and Geotechnics, 2011, 38(6): 783–790

[46] Wang M N, Zhao S G, Tong J J, Wang Z L, Yao M, Li J W, Yi W H. Intelligent classification model of surrounding rock of tunnel using drilling and blasting method. Underground Space, 2021, 6(5): 539–550

[47] Zhao S G, Wang M N, Yi W H, Yang D, Tong J J. Intelligent classification of surrounding rock of tunnel based on 10 machine learning algorithms. Applied Sciences, 2022, 12(5): 2656

[48] Hou S K, Liu Y R, Yang Q. 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, 2022, 14(1): 123–143

[49] Qiu D H, Fu K, Xue Y G, Tao Y F, Kong F M, Bai C H. TBM tunnel surrounding rock classification method and real-time identification model based on tunneling performance. International Journal of Geomechanics, 2022, 22(6): 04022070

[50] Hu J H, Zhou T, Ma S W, Yang D J, Guo M M, Huang P L. Rock mass classification prediction model using heuristic algorithms and support vector machines: A case study of Chambishi copper mine. Scientific Reports, 2022, 12(1): 928

[51] Xu J J, Zhang H, Tang C S, Cheng Q, Tian B G, Liu B, Shi B. Automatic soil crack recognition under uneven illumination condition with the application of artificial intelligence. Engineering Geology, 2022, 296: 106495

[52] HeK MZhang X YRenS QSunJ. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, CA: IEEE, 2015, 1026–1034

[53] XieS NGirshick RDollárPTuZ WHeK M. Aggregated residual transformations for deep neural networks. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition (CVPR). Honolulu, HI: IEEE, 2017, 1492–1500

[54] HowardA GZhu M LChenBKalenichenkoDWangW J WeyandTAndreetto MAdamH. Mobilenets: Efficient convolutional neural networks for mobile vision applications. 2017, arXiv: 1704.04861

[55] WangP QChen P FYuanYLiuDHuangZ H HouX DCottrell G. Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Tahoe, NV: IEEE, 2018, 1451–1460

[56] YuFKoltunV. Multi-scale context aggregation by dilated convolutions. In: Proceedings of the 4th International Conference on Learning Representations (ICLR). San Juan, UT: ICLR, 2016

[57] Vo D M, Lee S W. Semantic image segmentation using fully convolutional neural networks with multi-scale images and multi-scale dilated convolutions. Multimedia Tools and Applications, 2018, 77(14): 18689–18707

[58] MnihVHeess NGravesAKavukcuogluK. Recurrent models of visual attention. In: Ghahramani Z, Welling M, Cortes C, Lawrence N D, Weinberger K Q, eds. Advances in Neural Information Processing Systems 27 (NIPS 2014). Montreal, QC: NIPS, 2014

[59] VaswaniAShazeer NParmarNUszkoreitJJonesL GomezA NKaiser LPolosukhinI. Attention is all you need. In: Guyon I, Luxburg U V, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems 30 (NIPS 2017). Long Beach, CA: NIPS, 2017

[60] HuJShenL SunG. Squeeze-and-excitation networks. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT: IEEE, 2018, 7132–7141

[61] KingmaD PBa J. Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR). San Diego, CA: ICLR, 2015

[62] Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278–2324

[63] LiuZMaoH WuC YFeichtenhofer CDarrellTXieS. A ConvNet for the 2020s. In: Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA: IEEE, 2022

[64] SelvarajuR RCogswellMDasA VedantamRParikh DBatraD. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017, 618–626

RIGHTS & PERMISSIONS

Higher Education Press