Urban Tunneling Risk Management: Ground Settlement Assessment through Proportional Hazards Modeling

Kazempour Osalou, Ali; Moosazadeh, Sayfoddin; Nouri Qarahasanlou, Ali; Baghban Golpasand, Mohammad-Reza

doi:10.22044/jme.2024.13913.2590

Document Type : Original Research Paper

Authors

¹ Department of Mining Engineering, Faculty of Engineering, Urmia University, urmia, Iran

² Faculty of Science and Technology, UiT, the Arctic University of Norway, Tromsø, Norway

³ Department of Civil Engineering, Seraj Institute of Higher Education, Tabriz, Iran

https://doi.org/10.22044/jme.2024.13913.2590

Abstract

Nowadays, tunnel excavation plays a major role in the development of countries. Due to the complex and challenging ground conditions, a comprehensive study and analysis must be done before, during, and after the excavation of tunnels. Hence, the importance of study and evaluation of ground settlement is dramatically increased since many tunnel projects are performed in urban areas, where there are plenty of constructions, buildings, and facilities. For this reason, the control and prediction of ground settlement is one of the complicated topics in the field of risk engineering. Therefore, in this paper, the proportional hazard model (PHM) is used to analyze and study the ground settlement induced by Tabriz Metro Line 2 (TML2) tunneling. The PHM method is a semi-parametric regression method that can enter environmental conditions or factors affecting settlement probability. These influential factors are used as risk factors in the analysis. After establishing a database for a case study and using a proportional hazard model for surface settlement analysis, and then by evaluating the effect of environmental conditions on the ground surface settlement, it has been found that the risk factors of grouting pressure behind the segment, the ratio of tunnel depth to groundwater level, and drained cohesion strength at a significant level of 5% have a direct effect on the probability of settlement. The results also showed that the effect of grout injection pressure on ground subsidence is more than other parameters, and with increasing injection pressure, the probability of exceeding safe subsidence values decreases. In addition, it has been found that increasing the risk factor for the ratio of tunnel depth to groundwater level reduces the probability of exceeding the safe ground settlement. Finally, increasing the number of risk factors for drained cohesion strength increases the probability of exceeding safe settlement.

Keywords

Main Subjects

Mechanized Excavation

References

[1]. Qods, M., Parhizkar miandehi, A., & Bangian, A. (2016). Prediction of maximum ground surface settlement in metro line 2 in Mashhad, Iran by using PLAXIS software. Journal of mining engineering Amirkabir university of Technology. 37, 9.

[2]. Dindarloo, S. R., & Siami-Irdemoosa, E. (2015). Maximum surface settlement based classification of shallow tunnels in soft ground. Tunnelling and Underground Space Technology, 49, 320-327.

[3]. Fang, Q., Tai, Q., Zhang, D., & Wong, L. N. Y. (2016). Ground surface settlements due to construction of closely-spaced twin tunnels with different geometric arrangements. Tunnelling and Underground Space Technology, 51, 144-151.

[4]. Rey, S. J. (2015). Mathematical models in geography. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition (pp. 785-790). Elsevier Inc.

[5]. Renard, P., Alcolea, A., & Ginsbourger, D. (2013). Stochastic versus deterministic approaches. Environmental modelling: Finding simplicity in complexity, 133-149.

[6]. Mollon, G., Dias, D., & Soubra, A. H. (2013). Probabilistic analyses of tunneling-induced ground movements. Acta Geotechnica, 8, 181-199.

[7]. Grasmick, J. G., & Mooney, M. A. (2017). A probabilistic approach for predicting settlement due to tunneling in spatially varying glacial till. In Geo-Risk 2017 (pp. 300-309).

[8]. Gong, W., Luo, Z., Juang, C. H., Huang, H., Zhang, J., & Wang, L. (2014). Optimization of site exploration program for improved prediction of tunneling-induced ground settlement in clays. Computers and Geotechnics, 56, 69-79.

[9]. Miro, S., König, M., Hartmann, D., & Schanz, T. (2015). A probabilistic analysis of subsoil parameters uncertainty impacts on tunnel-induced ground movements with a back-analysis study. Computers and Geotechnics, 68, 38-53.

[10]. Michael, P. (2017). Structural Reliability Analysis of Tunneling-Induced Ground Settlement and Damage to Adjacent Buildings: A Case Study using Moment Methods and FLAC2D. https://digitalcommons.mtech.edu/grad_rsch/128

[11]. Cheng, H., Chen, J., & Chen, G. (2019). Analysis of ground surface settlement induced by a large EPB shield tunnelling: a case study in Beijing, China. Environmental Earth Sciences, 78, 1-18.

[12]. Wei, Y. & Yang, C. (2018). Predictive Modeling of Mining Induced Ground Subsidence with Survival Analysis and Online Sequential Extreme Learning Machine. Geotechnical and Geological Engineering. 36: 3573-3581.

[13]. Yang, X.L., Zhou, T., & Li, W.T. (2017). Reliability analysis of tunnel roof in layered Hoek-Brown rock masses. Computers and Geotechnics. 104: 8.

[14]. Zhang, S., Wang, Y., Gao, Q., Ma, X., Zhou, H., & Wang, Z. (2024). Probabilistic analysis of ground settlement induced by tunnel excavation in multilayered soil considering spatial variability. Computers and Geotechnics. 165: 105951.

[15]. Franco, V.H., Gitirana, G.d.F.N., & de Assis, A.P. (2019). Probabilistic assessment of tunneling-induced building damage. Computers and Geotechnics. 113: 103097.

[16]. Tian, Z. & Liao, H. (2011). Condition based maintenance optimization for multi-component systems using proportional hazards model. Reliability Engineering & System Safety. 96: 581-589.

[17]. Chen, H., Miao, Q., Liu, L., Wang, D., & Cong, L. (2012). The Study of Gearbox Condition Maintenance Policy Based on Proportional Hazards Model. Engineering Asset Management and Infrastructure Sustainability. 113-119.

[18]. Guo, X., Wang, Z., Geng, P., Chen, C., & Zhang, J. (2021). Ground surface settlement response to subway station construction activities using pile–beam–arch method. Tunnelling and Underground Space Technology. 108: 103729

[19]. Jallow, A., Ou, C.-Y., & Lim, A. (2019). Three-dimensional numerical study of long-term settlement induced in shield tunneling. Tunnelling and Underground Space Technology. 88: 221-236.

[20]. Jia, P., Zhao, W., Khoshghalb, A., Ni, P., Jiang, B., Chen, Y., & Li, S. (2020). A new model to predict ground surface settlement induced by jacked pipes with flanges. Tunnelling and Underground Space Technology. 98: 103330.

[21]. Ahmadi, S., Moosazadeh, S., Hajihassani, M., Moomivand, H., & Rajaei, M. M. (2019). Reliability, availability and maintainability analysis of the conveyor system in mechanized tunneling. Measurement, 145, 756-764.

[22]. Luxhoj, J.T. & Shyur, H.-J. (1997). Comparison of proportional hazards models and neural networks for reliability estimation. Journal of Intelligent Manufacturing. 8: 227-234.

[23]. Xu, D., Xiao, X., & Haibo, Y. (2020). Reliability evaluation of smart meters under degradation-shock loads based on phase-type distributions. IEEE Access, 8, 39734-39746.

[24]. Birolini, A., & Birolini, A. (2017). Reliability & availability of repairable systems. Reliability Engineering: Theory and Practice, 169-310.

[25]. Stapelberg, R. F. (2009). Availability and maintainability in engineering design (pp. 295-527). Springer London.

[26]. Wessels, W. (2010). Practical reliability engineering and analysis for system design and life-cycle sustainment. CRC Press.

[27]. Moosazadeh, S., Hoseinie, S. H., & Ghodrati, B. (2023). Effects of mixed-ground condition on tool life and cutterhead maintenance of tunnel boring machines. International Journal of System Assurance Engineering and Management, 14(6), 2586-2594.

[28]. Qarahasanlou, A. N. (2017). Production Assurance of Mining Fleet Based on Dependability and Risk Factor (Case Study: Sungun Copper Mine). Doctoral dissertation, PhD Thesis in Mineral Exploita, Shahrood University of Technology Faculty of Mining, Petroleum & Geophysics, Iran, Shahrood.

[29]. Aramesh, M., Shaban, Y., Yacout, S., Attia, M. H., Kishawy, H. A., & Balazinski, M. (2016). Survival life analysis applied to tool life estimation with variable cutting conditions when machining titanium metal matrix composites (Ti-MMCs). Machining Science and Technology, 20(1), 132-147.

[30]. Seetharaman, P. B., & Chintagunta, P. K. (2003). The proportional hazard model for purchase timing: A comparison of alternative specifications. Journal of Business & Economic Statistics, 21(3), 368-382.

[31]. Sankaran, P. G., & Sreeja, V. N. (2007). Proportional hazards model for multivariate failure time data. Communications in Statistics—Theory and Methods, 36(8), 1627-1641.

[32]. Wei, L.J. (1984). Testing Goodness of Fit for Proportional Hazards Model with Censored Observations. Journal of the American Statistical Association. 79: 649-652

[33]. Kleinbaum, D. G., & Klein, M. (1996). Survival analysis a self-learning text. Springer.

[34]. Ranger, J., & Ortner, T. M. (2013). Response time modeling based on the proportional hazards model. Multivariate Behavioral Research, 48(4), 503-533.

[35]. Barabadi, A., Tobias Gudmestad, O. & Barabady, J. (2015). RAMS data collection under Arctic conditions. Reliability Engineering & System Safety. 135: 92-99

[36]. Nouri Qarahasanlou, A., Ataei, M., Khalokakaie, R., Fatoorachi, S., & Barabady, R. (2019). Operating Environment Based Reliability Analysis of Mining Equipment Case Study: Molybdenum-Copper Mine (Sungun Copper Mine). Journal of Analytical and Numerical Methods in Mining Engineering, 9(18), 129-141.

[37]. Koohsari, A., Kalatehjari, R., Moosazadeh, S., Hajihassani, M., & Van, B. (2022). A Critical Investigation on the Reliability, Availability, and Maintainability of EPB Machines: A Case Study. Applied Sciences, 12(21), 11245.

[38]. Barabadi, R., Ataei, M., Khalokakaie, R., Nouri Gharahasanlou, A., & Aliyari, M. (2019). Reliability-based Spare Parts Provision (Case Study: Jajarm Bauxite Mine). National Conference of Risk and Resilience Management in Mining Industries.

[39]. Prentice, R. L., & Zhao, S. (2021). Regression models and multivariate life tables. Journal of the American Statistical Association, 116(535), 1330-1345.

[40]. Akbari, S., Zare, S., & Mirzaei Nasirabad, H. (2014). Evaluation of Interaction between Line 1 Twin Tunnels of Tabriz Metro Using 3D Numerical Modelling. Journal of Analytical and Numerical Methods in Mining Engineering, 4(8), 39-53.

Journal of Mining and Environment

Urban Tunneling Risk Management: Ground Settlement Assessment through Proportional Hazards Modeling

References

References

Volume 15, Issue 3
May 2024
Pages 1161-1175

Urban Tunneling Risk Management: Ground Settlement Assessment through Proportional Hazards Modeling

References

References

Volume 15, Issue 3May 2024Pages 1161-1175

Volume 15, Issue 3
May 2024
Pages 1161-1175