Feature Visualization Using a Deep Learning Technique with Attention-Based Mechanism for Pipe Jacking through “Soft Rocks”
Publication: Geo-Risk 2023
ABSTRACT
In pipe jacking operations, the prediction of jacking forces has been the subject of several studies. Many established jacking force models are based on the strength properties of the negotiated soils or rocks during jacking. The application of these models is useful during the planning for pipe jacking construction. However, such models have limited use during the construction stage when decisions are made based on pipe jacking operation parameters, that is, jacking speed, lubricant, slurry pressure, etc. The influence of pipe jacking operation parameters on jacking forces is not well quantified. This gap could present adverse risks to construction progress, particularly in the highly weathered “soft rocks” encountered in the Tuang Formation underlying the central business district of Kuching, Malaysia. To assist in modelling pipe jacking operation, deep learning approaches have been widely deployed due to its powerful capability of feature learning. Therefore, an interesting question is raised: are we able to absorb the knowledge gained from neural networks to help reduce the decision-making gap in pipe jacking operations between the construction and planning phases? To this end, this paper proposes the use of gated recurrent units (GRUs) with an attention-based mechanism for providing insight into the decisions made on operation parameters during pipe jacking. A case study will be presented to demonstrate the visualization of attention utilized by the deep learning model towards the operation parameters. The findings showed that the input features (i.e., operation parameters) shifted throughout the drive. At the initial sections of the drive, more attention was directed towards operation parameters related to the micro-tunnel boring machine (mTBM) face. Subsequently, the attention was shifted to the cumulative operation parameters with progressive pipe jacking. The findings from this paper have the potential to help pipe jacking specialists identify and quantify the key operation parameters and assess their risks throughout a pipe jacking drive.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
ATV-A (German Association for Water Environment). (1990). ATV-A 161, Structural calculation of driven pipes. German Association for the Water Environment.
Chen, C. W., Tseng, S. P., Kuan, T. W., and Wang, J. F. (2020). Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital Information 11, no. 2: 106. https://doi.org/10.3390/info11020106.
Chen, H., Xiao, C., Yao, Z., Jiang, H., Zhang, T., and Guan, Y. (2019). Prediction of TBM Tunneling Parameters through an LSTM Neural Network. IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 702–707, doi: https://doi.org/10.1109/ROBIO49542.2019.8961809.
Chen, J. Y., Yang, T. J., Zhang, D. M., Huang, H. W., and Tian, Y. (2021). Deep learning based classification of rock structure of tunnel face. Geoscience Frontiers, 12, 395–404, https://doi.org/10.1016/j.gsf.2020.04.003.
Cheng, W.-C., Bai, X.-D., Sheil, B. B., Li, G., and Wang, F. (2020). Identifying characteristics of pipejacking parameters to assess geological conditions using optimisation algorithm-based support vector machines. Tunnelling and Underground Space Technology, 106. https://doi.org/10.1016/j.tust.2020.103592.
Cheng, W.-C., Ni, J. C., Shen, J. S.-L., and Huang, H.-W. (2017). Investigation into factors affecting jacking force: a case study. Proceedings of the Institution of Civil Engineers – Geotechnical Engineering, 170(4), 322–334. https://doi.org/10.1680/jgeen.16.00117.
Choo, C. S., and Ong, D. E. L. (2015). Evaluation of pipe-jacking forces based on direct shear testing of reconstituted tunneling rock spoils. Journal of Geotechnical & Geoenvironmental Engineering, 141. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001348.
Choo, C. S., and Ong, D. E. L. (2012). Back-analysis of Frictional Jacking Forces Based on Shear Box Testing of Excavated Spoils, Second International Conference on Geotechnique, Construction Materials and Environment, Kuala Lumpur, Malaysia, ISBN: 978-4-9905958-1-4 C3051.
Choo, C. S., and Ong, D. E. L. (2017). Impact of highly weathered geology on pipe-jacking forces. Geotechnical Research, 4(2), 94–106. https://doi.org/10.1680/jgere.16.00022.
Choo, C. S., and Ong, D. E. L. (2020). Assessment of non-linear rock strength parameters for the estimation of pipe-jacking forces. Part 2. Numerical modeling. Engineering Geology, 265. https://doi.org/10.1016/j.enggeo.2019.105405.
Gao, B., Wang, R., Lin, C., Guo, X., Liu, B., and Zhang, W. (2020). TBM penetration rate prediction based on the long short-term memory neural network. Underground Space. https://doi.org/10.1016/j.undsp.2020.01.003.
Hadri, M. S. A. M., and Mohammad, H. (2020). Case Study of sewerage pipe installation using Pipe Jacking and Micro-tunnelling Boring Machine (MTBM) in Ipoh. IOP Conference Series: Materials Science and Engineering, 932. https://doi.org/10.1088/1757-899X/932/1/012047.
Hu, X. T., Huang, Y. A., Yin, Z. P., and Xiong, Y. L. (2012). Driving force planning in shield tunneling based on Markov decision processes. Sci China Tech Sci, 55:1022–1030. doi: https://doi.org/10.1007/s11431-011-4723-3.
Ji, X., Zhao, W., Ni, P., Barla, M., Han, J., Jia, P., Chen, Y., and Zhang, C. (2019). A method to estimate the jacking force for pipe jacking in sandy soils. Tunnelling and Underground Space Technology, 90, 119–130. https://doi.org/10.1016/j.tust.2019.04.002.
Jong, S. C., Ong, D. E. L., and Oh, E. (2021). State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction. Tunnelling and Underground Space Technology, 113. https://doi.org/10.1016/j.tust.2021.103946.
Lee, S. H., Chee, S. C., and Paolo, R. (2018). Multi-organ plant classification based on convolutional and recurrent neural networks. IEEE Transactions on Image Processing 27(9), 4287–4301.
Marcher, T., Erharter, G. H., and Winkler, M. (2020). Machine Learning in tunnelling – Capabilities and challenges. Geomechanics and Tunnelling, 13(2), 191–198. https://doi.org/10.1002/geot.202000001.
Nagrecha, K., Fisher, L., Mooney, M., Rodriguez-Nikl, T., Mazari, M., and Pourhomayoun, M. (2020). As-Encountered Prediction of Tunnel Boring Machine Performance Parameters using Recurrent Neural Networks. Transportation Research Record: Journal of the Transportation Research Board, 2674(10), 241–249. https://doi.org/10.1177/0361198120934796.
Ong, D. E. L., and Choo, C. S. (2016). Back-analysis and finite element modeling of jacking forces in weathered rocks. Tunnelling and Underground Space Technology, 51, 1–10. https://doi.org/10.1016/j.tust.2015.10.014.
Ong, D. E. L., and Choo, C. S. (2018). Assessment of non-linear rock strength parameters for the estimation of pipe-jacking forces. Part 1. Direct shear testing and backanalysis. Engineering Geology, 244, 159–172. https://doi.org/10.1016/j.enggeo.2018.07.013.
Ong, D. E. L., Barla, M., Cheng, J. W.-C., Choo, C. S., Sun, M., and Peerun, M. I. (2022). Sustainable Pipe Jacking Technology in the Urban Environment - Recent Advances and Innovations, Springer Singapore ISBN 978-981-16-9371-7, https://doi.org/10.1007/978-981-16-9372-4.
Osumi, T. (2000). Calculating Jacking Forces for Pipe Jacking Methods. No-Dig International Research, 40–42.
Peerun, M. I., Ong, D. E. L., Choo, C. S., and Cheng, W. C. (2020). Effect of interparticle behavior on the development of soil arching in soil-structure interaction. Tunnelling and Underground Space Technology, 106. https://doi.org/10.1016/j.tust.2020.103610.
Pellet-Beaucour, A.-L., and Kastner, R. (2002). Experimental and analytical study of friction forces during microtunneling operations. Tunnelling and Underground Space Technology, 17(1), 83–97. https://doi.org/10.1016/S0886-7798(01)00044-X.
Shao, B., Ma, B., and Shi, L. (2009). A Sewer Pipeline Installation Using Pipe-jacking in Lang Fang. American Society Civil Engineering, 1413–1424. https://doi.org/10.1061/41073(361)148.
Shou, K., Yen, J., and Liu, M. (2010). On the frictional property of lubricants and its impact on jacking force and soil–pipe interaction of pipe-jacking. Tunnelling and Underground Space Technology, 25(4), 469–477. https://doi.org/10.1016/j.tust.2010.02.009.
Staheli, K. (2006). Jacking force prediction: an interface friction approach based on pipe surface roughness. PhD Dissertation, Georgia Institute of Technology.
Sun, S. L. (2022). Shield Tunneling Parameters Matching Based on Support Vector Machine and Improved Particle Swarm Optimization. Scientific Programming, vol. 2022. https://doi.org/10.1155/2022/6782947.
Luong, T., Pham, H., and Manning, C. D. (2015). Effective Approaches to Attention-based Neural Machine Translation. EMNLP. 1412–1421.
Verburg, N. (2006). An analysis of friction by microtunnelling. Master thesis, Technische Universiteit Delft.
Wei, X.-J., Wang, X., Wei, G., Zhu, C.-W., and Shi, Y. (2021). Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models. Journal of Marine Science and Engineering, 9(1). https://doi.org/10.3390/jmse9010071.
Xu, K., et al. (2015). Show, attend and tell: Neural image caption generation with visual attention. International conference on machine learning. PMLR.
Zhang, N., Zhou, A., Pan, Y., and Shen, S.-L. (2021). Measurement and prediction of tunnelling induced ground settlement in karst region by using expanding deep learning method. Measurement, 183. https://doi.org/10.1016/j.measurement.2021.109700.
Information & Authors
Information
Published In
Geo-Risk 2023
Pages: 42 - 54
History
Published online: Jul 20, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.