Prediction of longitudinal surface settlement in composite formation using large-diameter shield machine based on machine learning techniques

Abstract

The employment of large-diameter shield machines has increased the likelihood of encountering composite formations, posing engineering challenges associated with excessive surface settlement. To tackle this issue, this study introduces a hybrid model which integrates the extreme learning machine (ELM) with the sparrow search algorithm (SSA) to predict longitudinal surface settlement. Based on on-site measurements. this study analyzed longitudinal surface settlement patterns across both homogeneous and composite formations. Tunneling parameters, geological parameters, and geometrical parameters were considered as input parameters. Furthermore, this study conducted a comparative analysis of the predictive performance among SSA-ELM, ELM, and SSA-back propagation (BP), with respect to coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), and training time. Last, in anticipation of potential risks, a feasible optimization approach is provided. SSA-ELM outperforms both ELM and SSA-BP in terms of R², MAE, and RMSE, with values of 0.8822, 0.3357, and 0.4072, respectively. Regarding training time, SSA-ELM requires 0.2346 s, prior to SSA-BP with a value of 1.8427. Although it is not as fast as ELM, the discrepancy between SSA-ELM and ELM is only 0.1187 s. Overall, SSA-ELM demonstrates higher performance and serves as an effective tool to guide the construction process.

Graphical abstract

Keywords

large-diameter shield machine / composite formation / extreme learning machine / sparrow search algorithm / longitudinal surface settlement

References

[1] Jong S C, Ong D E L, Oh E. State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction. Tunnelling and Underground Space Technology, 2021, 113: 103946

[2] Zhou C, Gao Y Y, Chen E J, Ding L Y, Qin W B. Deep learning technologies for shield tunneling: Challenges and opportunities. Automation in Construction, 2023, 154: 104982

[3] Wen Z, Guo L M, Meng S P, Rong X L, Shi Y H. IoT-assisted feature learning for surface settlement prediction caused by shield tunnelling. Computer Communications, 2023, 203: 276–288

[4] Xu D M, Wang Y S, Huang J Q, Liu S J, Xu S J, Zhou K. Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms. Tunnelling and Underground Space Technology, 2023, 131: 104852

[5] Ling X Z, Kong X X, Tang L, Zhao Y Z, Tang W C, Zhang Y F. Predicting earth pressure balance (EPB) shield tunneling-induced ground settlement in compound strata using random forest. Transportation Geotechnics, 2022, 35: 100771

[6] PeakR B. Deep excavation and tunneling in soft ground. In: Proceedings of 7th International Conference on Soil Mechanic and Foundation Engineering. Mexico: International Society for Soil Mechanics and Geotechnical Engineering, 1969, 225–290

[7] Mair R J, Taylor R N, Bracegirdle A. Subsurface settlement profiles above tunnels in clays. Geotechnique, 1993, 43(2): 315–320

[8] Loganathan N, Poulos H G. Analytical prediction for tunneling-induced ground movements in clays. Journal of Geotechnical and Geoenvironmental Engineering, 1998, 124(9): 846–856

[9] Bobet A. Analytical solutions for shallow tunnels in saturated ground. Journal of Engineering Mechanics, 2001, 127(12): 1258–1266

[10] Zhang J, Gao Y F, Feng T G, Yang J S, Yang F. Upper-bound finite-element analysis of axisymmetric problems using a mesh adaptive strategy. Computers and Geotechnics, 2018, 102: 148–154

[11] Zhang J, Feng T G, Yang J S, Yang F, Gao Y F. Upper-bound finite-element analysis of characteristics of critical settlement induced by tunneling in undrained clay. International Journal of Geomechanics, 2018, 18(9): 04018110

[12] Huang H W, Gong W P, Khoshnevisan S, Juang C H, Zhang D M, Wang L. Simplified procedure for finite element analysis of the longitudinal performance of shield tunnels considering spatial soil variability in longitudinal direction. Computers and Geotechnics, 2015, 64: 132–145

[13] Paternesi A, Schweiger H F, Scarpelli G. Numerical analyses of stability and deformation behavior of reinforced and unreinforced tunnel faces. Computers and Geotechnics, 2017, 88: 256–266

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

[15] WuS CWangRZhangJ M. Research on CPTu-based soil classification model using random forest algorithm and its application in different regions. Journal of Central South University, 2023, 54(11): 4391–4402 (in Chinese)

[16] CuiFWangH FShuZ L. Prediction of aerodynamic pressure amplitude in tunnel based on PSO-BP neural network. Journal of Central South University, 2023, 54(9): 3752–3761 (in Chinese)

[17] Neaupane K M, Adhikari N R. Prediction of tunneling-induced ground movement with the multi-layer perceptron. Tunnelling and Underground Space Technology, 2006, 21(2): 151–159

[18] Ocak I, Seker S E. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environmental Earth Sciences, 2013, 70(3): 1263–1276

[19] ZhouC ZYangJ SMouY TWangS Y. BP neural network prediction method for ground surface settlement caused by shield tunneling in soft layer down laid on hard layer in Nanchang. Journal of Disaster Prevention and Mitigation Engineering, 2015, 35(4): 556–562 (in Chinese)

[20] Chen R P, Zhang P, Kang X, Zhong Z Q, Liu Y, Wu H N. Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods. Soil and Foundation, 2019, 59(2): 284–295

[21] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1–3): 489–501

[22] Liu S G, Sun C Z, Zhou H, Wang Y H. Intelligent prediction model (IPM) of foundation pit displacement based on extreme learning machine (ELM) and its application. Processes, 2022, 10(5): 896

[23] SunHJiaL HWeiX LLinF LMengX B. Research on fault diagnosis method of atmospheric cutter head shield machine hob based on machine learning. Tunnel Construction, 2023, 43(Sup 1): 550–557 (in Chinese)

[24] ShaoC JLiX LSuH Y. Performance prediction of hard rock TBM based on extreme learning machine. In: Lee J, Lee M C, Liu H H, et al., eds. International Conference on Intelligent Robotics and Applications (ICIRA). Lect Notes in Computer Science, Vol 8103. Berlin: Springer, 2013, 409–41

[25] LinS JDongCChenM ZZhangFChenJ H. Summary of new group intelligent optimization algorithms. Computer Engineering and Applications, 2018, 54(12): 1–9 (in Chinese)

[26] Li Y M, Peng T, Hua L, Ji C L, Ma H X, Nazir M S, Zhang C. Research and application of an evolutionary deep learning model based on improved grey wolf optimization algorithm and DBN-ELM for AQI prediction. Sustainable Cities and Society, 2022, 87: 104209

[27] DorigoM. Optimization, learning and natural algorithms. Dissertation for the Doctoral Degree. Milan: Polytechnic University of Milan, 1992 (in Italian)

[28] KennedyJEberhartR. Particle swarm optimization. In: Proceedings of ICNN’95—International Conference on Neural Networks, Vol 4. Perth: IEEE, 1995, 1942–1948

[29] Xue J K, Shen B. A novel swarm intelligence optimization approach: Sparrow search algorithm. Systems Science & Control Engineering, 2020, 8(1): 22–34

[30] Yan S Q, Liu W D, Li X Q, Yang P, Wu F X, Yan Z. Comparative study and improvement analysis of sparrow search algorithm. Wireless Communications and Mobile Computing, 2022, 2022: 4882521

[31] Adnan R M, Mostafa R R, Kisi O, Yaseen Z M, Shahid S, Zounemat-Kermani M. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowledge-Based Systems, 2021, 230: 107379

[32] Fan C C, Zheng Y X, Wen Y C, Sun M. Classification and prediction of deformed steel and concrete bond-slip failure modes based on SSA-ELM model. Structures, 2023, 57: 105131

[33] Yue Y G, Cao L, Lu D W, Hu Z Y, Xu M H, Wang S X, Li B, Ding H H. Review and empirical analysis of sparrow search algorithm. Artificial Intelligence Review, 2023, 56(10): 10867–10919

[34] Li J H, Li P X, Guo D, Li X, Chen Z Y. Advanced prediction of tunnel boring machine performance based on big data. Geoscience Frontiers, 2021, 12(1): 331–338

[35] YinQZhouYRaoJ Y. A deep learning-based method for predicting surface settlement induced by shield tunnel construction. Journal of Central South University, 2024, 55(2): 607–617 (in Chinese)

[36] Feng T G, Wang C R, Zhang J, Zhou K, Qiao G X. Prediction of stratum deformation during the excavation of a foundation pit in composite formation based on the artificial bee colony–back-propagation model. Engineering Optimization, 2022, 54(7): 1217–1235

Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (Grant Nos. 52178386 and 52378336). The authors are grateful to these institutions for their support.

Competing interests

The authors declare that they have no competing interests.

RIGHTS & PERMISSIONS

2024 Higher Education Press