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Temporal–spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network

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Journal of Civil Structural Health Monitoring Aims and scope Submit manuscript

Abstract

Predicting the mechanical behaviors of tunnel and subsurface facilities is an effective way to prevent accidental disasters. However, some drawbacks exist in many traditional prediction models, such as inadequate consideration of impacting factors, low predictive accuracy, and high computational cost. To this end, a coupled model based on deep attention-based temporal convolutional network (DATCN) is proposed for multiple prediction of structural mechanical behavior, where temporal convolutional network and self-attention mechanism are applied to learn temporal dependencies and spatial dependencies respectively. Subsequently, the DATCN model is formalized on a long-term dataset collected using a Structural Health Monitoring System in the Wuhan Yangtze River tunnel. Using three evaluation indicators, a series of data experiments are conducted to obtain the most appropriate parameters involved in the model and the superiority of DATCN over other commonly used models including LSTM, RNN, GRU, LR, and SVR is discussed. Experimental results indicate that future structural behavior shows a strong correlation between spatial dependencies and historical performance, especially that in the last 16 days. Moreover, the predictive capability of DATCN is the best compared to other commonly used models, whose predictive accuracy for the next 10 days is better than 88% and improved by 1.726% at least. Finally, the DATCN model is adopted to predict the structural behavior of the tunnel under extreme conditions as a field application, and the results suggest that the DATCN model is robust and accurate.

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References

  1. Shahrour I, Bian H, Xie X, Zhang Z (2020) Smart technology applications for the optimal management of underground facilities. Undergr Space 6(5):551-559

    Article  Google Scholar 

  2. Kruachottikul P, Cooharojananone N, Phanomchoeng G et al (2021) Deep learning-based visual defect-inspection system for reinforced concrete bridge substructure: a case of Thailand’s department of highways. J Civil Struct Health Monit 11:949–965

    Article  Google Scholar 

  3. Tan X, Chen W, Wu G, Wang L, Yang J (2020) A structural health monitoring system for data analysis of segment joint opening in an underwater shield tunnel. Struct Health Monit 19(4):1032–1050

    Article  Google Scholar 

  4. Yang JP, Chen WZ, Li M, Tan XJ, Yu J (2018) Structural health monitoring and analysis of an underwater TBM tunnel. Tunnelling Undergr Space Technol 82:235–247

    Article  Google Scholar 

  5. Wang Y, Ni Y (2020) Bayesian dynamic forecasting of structural strain response using structural health monitoring data. Struct Control Health Monit 27:e2575

    Google Scholar 

  6. Tan X, Chen W, Wang L, Tan X, Yang J (2020) Integrated approach for structural stability evaluation using real-time monitoring and statistical analysis: underwater shield tunnel case study. J Perform Constr Facil 34:04019118

    Article  Google Scholar 

  7. Spencer BF Jr, Ruiz-Sandoval ME, Kurata N (2004) Smart sensing technology: opportunities and challenges. Struct Control Health Monit 11:349–368

    Article  Google Scholar 

  8. Mahmoodzadeh A, Mohammadi M, Ibrahim HH, Rashid TA, Aldalwie AHM, Ali HFH, Daraei A (2021) Tunnel geomechanical parameters prediction using gaussian process regression. Mach Learn Appl 3:100020

    Google Scholar 

  9. Fahimifar A, Tehrani FM, Hedayat A, Vakilzadeh A (2010) Analytical solution for the excavation of circular tunnels in a visco-elastic burger’s material under hydrostatic stress field. Tunn Undergr Space Technol 25(4):297–304

    Article  Google Scholar 

  10. Sharifzadeh M, Tarifard A, Moridi MA (2013) Time-dependent behavior of tunnel lining in weak rock mass based on displacement back analysis method. Tunn Undergr Space Technol 38:348–356

    Article  Google Scholar 

  11. Cai Y, Esaki T, Jiang Y (2004) An analytical model to predict axial load in grouted rock bolt for soft rock tunnelling. Tunne Undergr Space Technol 19(6):607–618

    Article  Google Scholar 

  12. Gong WP, Huang CH, Huang HW, Zhang J, Luo Z (2015) Improved analytical model for circumferential behavior of jointed shield tunnels considering the longitudinal differential settlement. Tunn Undergr Space Technol 45:153–165

    Article  Google Scholar 

  13. Nadimi S, Shahriar K, Sharifzadeh M, Moarefvand P (2011) Triaxial creep tests and back analysis of time-dependent behavior of Siah Bisheh cavern by 3-dimensional distinct element method. Tunn Under Space Technol 26:155–162

    Article  Google Scholar 

  14. Debernardi D, Barla G (2009) New viscoplastic model for design analysis of tunnels in squeezing conditions. Rock Mech Rock Eng 42:259–288

    Article  Google Scholar 

  15. Wang LY, Chen WZ, Tan XY, Tan XJ, Yang JP, Yang DS, Zhang X (2019) Numerical investigation on the stability of deforming fractured rocks using discrete fracture networks: a case study of underground excavation. Bull Eng Geol Environ 79(6):1–19

    Google Scholar 

  16. Jimenez R, Recio D (2011) A linear classifier for probabilistic predictionof squeezing conditions in Himalayan tunnels. Eng Geol 121:101–109

    Article  Google Scholar 

  17. Wang LY, Chen WZ, Tan XY, Tan XJ, Yuan JQ, Liu Q (2019) Evaluation of mountain slope stability considering the impact of geological interfaces using discrete fractures model. J Mountain Sci 16(9):2184-2202

    Article  Google Scholar 

  18. Yun HB, Park SH, Mehdawi N, Mokhtari S, Chopra M, Reddi LN, Park KT (2014) Monitoring for close proximity tunneling effects on an existing tunnel using principal component analysis technique with limited sensor data. Tunn Undergr Space Technol 43:398–412

    Article  Google Scholar 

  19. Zhang J, Shi R, Shi S, Alzo’ubi A K, Roco-Videla A, Hussein M M A, Khan A (2021) Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP). Eng comput

  20. Mei L, Mita A, Zhou J (2016) An improved substructural damage detection approach of shear structure based on armax model residual. Struct Control Health Monit 23:218–236

    Article  Google Scholar 

  21. Farahani RV, Penumadu D (2016) Full-scale bridge damage identification using time series analysis of a dense array of geophones excited by drop weight. Struct Control Health Monit 23:982–997

    Article  Google Scholar 

  22. Thanh CL, Trong N N, Samir K, Phuoc T N, Seyedali M, Khuong D N (2021) An efcient approach for damage identifcation based on improved machine learning using PSO-SVM. Eng comput

  23. Goulet JA (2017) Bayesian dynamic linear models for structural health monitoring. Struct Control Health Monit 24:e203

    Article  Google Scholar 

  24. Goulet JA, Koo K (2018) Empirical validation of bayesian dynamic linear models in the context of structural health monitoring. J Bridge Eng 23:05017017

    Article  Google Scholar 

  25. Zhu H, Wang X, Chen X, Zhang L (2020) Similarity search and performance prediction of shield tunnels in operation through time series data mining. Autom Constr 114:103178

    Article  Google Scholar 

  26. Mahdevari S, Torabi SR (2012) Prediction of tunnel convergence using artificial neural networks. T Tunn Undergr Space Technol 28:218–228

    Article  Google Scholar 

  27. Deng LZ, Smith A, Dixon N, Yuan HY (2021) Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements. Eng Geol 293:106315

    Article  Google Scholar 

  28. Li N, Nguyen H, Rostami J, Zhang WG, Bui XN, Pradhan B (2022) Predicting rock displacement in underground mines using improved machine learning-based models. Meas 188:110552

    Article  Google Scholar 

  29. Hou SK, Liu YR, Yang Q (2021) Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning. J Rock Mech Geotech Eng 14(1):123-143

    Article  Google Scholar 

  30. Xu C, Liu XL, Wang EZ, Wang SJ (2021) Prediction of tunnel boring machine operating parameters using various machine learning algorithms. Tunn Undergr Space Technol 109:103699

    Article  Google Scholar 

  31. Mahmoodzadeh A, Mohammadi M, Ibrahim HH, Abdulhamid SN, Ali HFH, Hasan AM, Khishe M, Mahmud H (2021) Machine learning forecasting models of disc cutters life of tunnel boring machine. Autom Constr 128:103779

    Article  Google Scholar 

  32. Tan XY, Sun XX, Chen WZ, Du BW, Ye JC, Sun LL (2021) Investigation on the data augmentation using machine learning algorithms in structural health monitoring information. Struct Health Monit 20(4):2054-2068

    Article  Google Scholar 

  33. Krishna RS, Ankit KP, Korra SB, Santos KD (2021) Multi-output TCN autoencoder for long-term pollution forecasting for multiple sites. Urban Clim 39:100943

    Article  Google Scholar 

  34. Tang XL, Chen HX, Xiang WH, Yang JM, Zou M (2022) Short-term load forecasting using channel and temporal attention based temporal convolutional network. Electr Power Syst Res 205:107761

    Article  Google Scholar 

  35. Syed M A, Indumathi C P, Ganesh S W, Babu G (2021) A neural network based machine learning model in digital health care for wait-time prediction. Mater Today

  36. Krishna Rani Samal K, Korra SB, Santos KD (2021) Temporal convolutional denoising autoencoder network for air pollution prediction with missing values. Urban Clim 38:100872

    Article  Google Scholar 

  37. Ashish V, Noam S, Parmar N K (2017) Attention is all you need. Google

  38. Shang YM, Huang HY, Sun X, Wei W, Mao XL (2022) A pattern-aware self-attention network for distant supervised relation extraction. Inf Sci 584:269–279

    Article  Google Scholar 

  39. Xiao DY, Qin CJ, Ge JW, Xia PC, Huang YX, Liu CL (2022) Self-attention-based adaptive remaining useful life prediction for IGBT with Monte Carlo dropout. Knowl-Based Syst 239:107902

    Article  Google Scholar 

  40. Du BW, Li WT, Tan XY, Ye JC, Chen WZ, Sun LL (2021) Development of load-temporal model to predict the further mechanical behaviors of tunnel structure under various boundary conditions. Tunnelling Underground Space Technol 116:104077

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant nos. U1806226, 51991392), Key Research Program of Chinese Academy of Sciences (Grant no. ZDRW-ZS-2021-3-3).

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Authors and Affiliations

  1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China

    Xuyan Tan, Weizhong Chen, Jianping Yang & Xianjun Tan

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xuyan Tan, Weizhong Chen, Jianping Yang & Xianjun Tan

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  1. Xuyan Tan
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  2. Weizhong Chen
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  4. Xianjun Tan
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Correspondence to Xuyan Tan or Weizhong Chen.

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Tan, X., Chen, W., Yang, J. et al. Temporal–spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network. J Civil Struct Health Monit 12, 675–687 (2022). https://doi.org/10.1007/s13349-022-00574-4

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  • DOI: https://doi.org/10.1007/s13349-022-00574-4

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