طبقه‌بندی فازی ریسک نشست ناشی از حفاری مکانیزه با ماشین TBM-EPB با استفاده از سیستم استنتاج فازی – عصبی تطبیقی (ANFIS)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری؛ گروه مدیریت ساخت و آب، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران

2 استادیار؛ گروه مدیریت ساخت و آب، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران

3 استاد؛ دانشکده‌ی مهندسی عمران، دانشگاه سمنان

4 استادیار؛ دانشکده مهندسی راه آهن، دانشگاه علم و صنعت

چکیده

پیش بینی و ارزیابی ریسک ناشی از نشست سطح زمین در اثر حفاری مکانیزه با استفاده از سپر EPB از مهمترین بخش های مدیریت ریسک در مدیریت پروژه های تونل سازی می باشد. از اینرو، در این مقاله سعی می گردد طی دو بخش مستقل به این مهم پرداخته شود. در بخش ابتدایی، رقم نشست سطحی زمین ناشی از حفاری مکانیزه با استفاده از دستگاه TBM-EPB بر اساس روش سیستم استنتاج فازی – عصبی تطبیقی (ANFIS) با در نظر گرفتن ده متغیر جامع ورودی شبکه، تخمین زده می شود. سیستم استنتاج فازی – عصبی تطبیقی با در نظر گرفتن هفت تابع عضویت گوسی برای هر یک از ده متغیر ورودی در لایه اول شبکه و همچنین تعریف هفت قانون فازی برای استنتاج خود در لایه دوم شبکه، قادر بوده است متغیر خروجی سیستم که حداکثر نشست سطحی می باشد را با دقت مطلوب و رقم 01322/0 ریشه میانگین مربعات خطا (RMSE) پیش بینی نماید. در بخش دوم مقاله، ریسک ناشی از نشست سطحی در حفاری مکانیزه با استفاده از توابع عضویت گوسی در پنج کلاس مختلف ریسک تحلیل و طبقه بندی فازی می شود. در انتها، طبقه بندی قطعی رده های مختلف ریسک نشست در پژوهش های پیشین و طبقه بندی فازی سطوح مختلف ریسک انجام شده در این تحقیق، قیاس می شوند. طبق نتایج، با تلفیق قضاوت مهندسی فازی منحصر به هر پروژه تونل سازی مشابه و همچنین تحلیل و روش ارائه شده در این مقاله، می توان به تصمیم بهینه کاربردی در مدل ارزیابی ریسک دست یافت.

کلیدواژه‌ها


Abdellah, W. R., Ali, M., & Yang, H. (2018). Studying the Effect of Some Parameters on the Stability of Shallow Tunnels.  Journal of Sustainable Mining, 17(1), 20–33.
Addenbrooke, T. I., Potts, D., & Puzrin, A. (1997). The Influence of Pre-Failure Soil Stiffness on the Numerical Analysis of Tunnel Construction. Geotechnique, 47(3), 693–712.
Ahangari, K., Moeinossadat, S. R., & Behnia, D. (2015). Estimation of Tunnelling-Induced Settlement by Modern Intelligent Methods. Soils and Foundations, 55(4), 737–48.
Ahmadi, M., Naderpour, H., & Kheyroddin, A. (2014). Utilization of Artificial Neural Networks to Prediction of the Capacity of CCFT Short Columns Subject to Short Term Axial Load. Archives of Civil and Mechanical Engineering, 14(3), 510–17.
Assadoulahi, M., & Moomivand, H. (2021). A Critical Analysis of the Effect of Earth Pressure Balance (EPB) on Displacements of Tunnel Face by Numerical Modeling for the Geomechanical Conditions of Abuzar Water Transitional Tunnel.  Journal of Civil and Environmental Engineering, University of Tabriz, 50(4), 83–93.
Attewell, P. B., & Hurrell, M. (1985). Settlement Development Caused By Tunnelling in Soil. Ground Engineering, 18(8), 17–20.
Baziar, M. H., Moghadam, M. R., Choo, Y. W., & Kim, D. S. (2016). Tunnel Flexibility Effect on the Ground Surface Acceleration Response. Journal of Earthquake Engineering and Engineering Vibration, 15(3), 457–76.
Behro Comprehensive Consulting Engineers Company (Behro.co). (2021). The employer's consultant in civil, procurement, equipment, rail transportation, & the operation of Tehran Metro -  Line 6.
Bouayad, D., & Emeriault, F. (2017). Modeling the Relationship between Ground Surface Settlements Induced by Shield Tunneling and the Operational and Geological Parameters Based on the Hybrid PCA/ANFIS Method. Tunnelling and Underground Space Technology, 68, 142–152.
Bouayad, D., Emeriault, F., & Maza, M. (2015). Assessment of Ground Surface Displacements Induced by an Earth Pressure Balance Shield Tunneling Using Partial Least Squares Regression. Environmental Earth Sciences, 73(11), 7603–16.
Chen, R., Meng, F., Li, Z., Ye, Y., & Ye, J. (2016). Investigation of Response of Metro Tunnels Due to Adjacent Large Excavation and Protective Measures in Soft Soils. Tunnelling and Underground Space Technology, 58, 224-235.
Chen, R., Zhang, P., Wu, H., Wang, Z., & Zhong, Z. (2019). Prediction of Shield Tunneling-Induced Ground Settlement Using Machine Learning Techniques. Frontiers of Structural and Civil Engineering, 13(6), 1363–78.
Chou, J., & Lin, C. (2013). Predicting Disputes in Public-Private Partnership Projects: Classification and Ensemble Models. Journal of Computing in Civil Engineering, 27(1), 51–60.
Dai, H., & Cao, Z. (2017). A Wavelet Support Vector Machine-Based Neural Network Metamodel for Structural Reliability Assessment. Computer-Aided Civil and Infrastructure Engineering, 32(4), 344–57.
Ding, L., Wang, F., Luo, H., Yu, M., & Wu, X. (2013). Feedforward Analysis for Shield-Ground System. Journal of Computing in Civil Engineering, 27(3), 231–42.
Hamdia, K. M., Lahmer, T., Nguyen-Thoi, T., & Rabczuk, T. (2015). Predicting the Fracture Toughness of PNCs: A Stochastic Approach Based on ANN and ANFIS. Computational Materials Science, 102, 304–13.
Huang, H., Gong, V., Khoshnevisan, S., Juang, C. H., Zhang, D., & Wang, L. (2015). Simplified Procedure for Finite Element Analysis of the Longitudinal Performance of Shield Tunnels Considering Spatial Soil Variability in Longitudinal Direction. Computers and Geotechnics, 64, 132–45.
Idinger, G., Aklik, P., Wu, W., & Borja, R. I. (2011). Centrifuge Model Test on the Face Stability of Shallow Tunnel. Acta Geotechnica, 6(2), 105–17.
Karakus, M. (2007). Appraising the Methods Accounting for 3D Tunnelling Effects in 2D Plane Strain FE Analysis. Tunnelling and Underground Space Technology, 22(1), 47–56.
Karakus, M., & Fowell, R. (2005). Back Analysis for Tunnelling Induced Ground Movements and Stress Redistribution. Tunnelling and Underground Space Technology, 20(6), 514–24.
Kim, C. Y., Bae, G., Hong, S., Park, C., Moon, H., & Shin, H. (2001). Neural Network Based Prediction of Ground Surface Settelements Due to Tunnelling. Computers and Geotechnics, 28(6–7), 517–47.
Kirsch, A. (2010). Experimental Investigation of the Face Stability of Shallow Tunnels in Sand. Acta Geotechnica, 5(1), 43–62.
Kohestani, V. R., Bazargan-Lari, M. R., & Asgari-Marnani, J. (2017). Prediction of Maximum Surface Settlement Caused by Earth Pressure Balance Shield Tunneling Using Random Forest. Journal of AI and Data Mining, 5(1), 127–35.
Liu, W., Zhai, S., & Liu, W. (2019). Predictive Analysis of Settlement Risk in Tunnel Construction: A Bow-Tie-Bayesian Network Approach. Advances in Civil Engineering, vol. 2019, Article ID, 2045125, 19 pages.
Guglielmetti. V., Grasso, P., Mahtab, A., & Xu, S. (2008). Mechanized Tunnelling in Urban Areas, Design methodology and construction control. CRC Press, eBook ISBN, 9780203938515, (pp. 129-39).
Naderpour, H., Kheyroddin, A., & Ghodrati Amiri, G. (2010). Prediction of FRP-Confined Compressive Strength of Concrete Using Artificial Neural Networks. Composite Structures, 92(12), 2817–29.
Naderpour, H., & Mirrashid, M. (2020). Soft Computing in Civil Engineering. Semnan University Press, (pp. 20-24 & 248-250).
Ng, C. W.W., Hong, Y., & Soomro, M. A. (2015). Effects of Piggyback Twin Tunnelling on a Pile Group: 3D Centrifuge Tests and Numerical Modelling. Geotechnique, 65(1), 38–51.
O’Reilly, M. P., & New, B. M. (1982). Settlements above Tunnels in the United Kingdom - Their Magnitude and Prediction. Tunnelling ’82. Papers presented at the 3rd international symposium, 173–81.
Ocak, I., & Seker, S. E. (2013). Calculation of Surface Settlements Caused by EPBM Tunneling Using Artificial Neural Network, SVM, and Gaussian Processes. Environmental Earth Sciences, 70(3), 1263–76.
Pakbaz, M. S., Imanzadeh, S., & Bagherinia, K. H. (2013). Characteristics of Diaphragm Wall Lateral Deformations and Ground Surface Settlements: Case Study in Iran-Ahwaz Metro. Tunnelling and Underground Space Technology, 35, 109–21.
Paternesi, A., Schweiger, H. F., & Scarpelli, G. (2017). Numerical Analyses of Stability and Deformation Behavior of Reinforced and Unreinforced Tunnel Faces. Computers and Geotechnics, 88, 256–66.
Peck, R. B. (1969). Deep Excavations and Tunneling in Soft Ground. 7th International Conference on Soil Mechanics and Foundation Engineering, 225–90.
Pourtaghi, A., & Lotfollahi-Yaghin, M. A. (2012). Wavenet Ability Assessment in Comparison to ANN for Predicting the Maximum Surface Settlement Caused by Tunneling. Tunnelling and Underground Space Technology, 28(1), 257–71.
Chapman, D., Rogers, C., & Hunt, D. (2004). Predicting the Settlements above Twin Tunnels Constructed in Soft Ground. Tunnelling and Underground Space Technology, 19(4–5), 378.
Qi, C., & Tang, X. (2018). Slope Stability Prediction Using Integrated Metaheuristic and Machine Learning Approaches: A Comparative Study. Computers and Industrial Engineering, 118, 112–22.
Sagaseta, C. (1987). Analysis of Undrained Soil Deformation Due to Ground Loss. Geotechnique, 37(3), 301–20.
Samui, P., & Sitharam, T. G. (2008). Least-Square Support Vector Machine Applied to Settlement of Shallow Foundations on Cohesionless Soils. International Journal for Numerical and Analytical Methods in Geomechanics, 32(17), 2033–43.
Santos, O. J., & Celestino, T. B. (2008). Artificial Neural Networks Analysis of São Paulo Subway Tunnel Settlement Data. Tunnelling and Underground Space Technology, 23(5), 481–91.
Shahin, M. A., Maier, H. R., & Jaksa, M. B. (2005). Investigation into the Robustness of Artificial Neural Networks for a Case Study in Civil Engineering. In International Congress on Modeling and Simulation, MODSIM 2005, , 79–83.
Shi, H., Yang, H., Gong, G., & Wang, L. (2011). Determination of the Cutterhead Torque for EPB Shield Tunneling Machine.  Automation in Construction, 20(8), 1087–95.
Shi, J., Ortigao, J. A. R., & Bai, J. (1998). Modular Neural Networks for Predicting Settlements during Tunneling. Journal of Geotechnical and Geoenvironmental Engineering, 124(5), 389–95.
Sun, W., Shi, M., Zhang, C., Zhao, J., & Song, X. (2018). Dynamic Load Prediction of Tunnel Boring Machine (TBM) Based on Heterogeneous in-Situ Data. Automation in Construction, 92, 23–34.
Suwansawat, S., & Einstein, H. H. (2006). Artificial Neural Networks for Predicting the Maximum Surface Settlement Caused by EPB Shield Tunneling. Tunnelling and Underground Space Technology, 21(2), 133–50.
Verruijt, A., & Booker, J. R. (1998). Surface Settlements Due to Deformation of a Tunnel in an Elastic Half Plane. Géotechnique, 48(5), 709–13.
Vorster, T. E., Klar, A., Soga, K., & Mair, R. J. (2005). Estimating the Effects of Tunneling on Existing Pipelines. Journal of Geotechnical and Geoenvironmental Engineering, 131(11), 1399–1410.
Zhang, L., Wu, X., Ji, W., & AbouRizk, S. M. (2017). Intelligent Approach to Estimation of Tunnel-Induced Ground Settlement Using Wavelet Packet and Support Vector Machines. Journal of Computing in Civil Engineering, 31(2).
Zhang, L., Wu, X., Zhu, H., & AbouRizk, S. M. (2017). Performing Global Uncertainty and Sensitivity Analysis from Given Data in Tunnel Construction. Journal of Computing in Civil Engineering, 31(6).
Zhang, W. G., Li, H. R., Wu, C. Z., Li, Y. Q., Liu, Z. Q., & Liu, H. L. (2021). Soft Computing Approach for Prediction of Surface Settlement Induced by Earth Pressure Balance Shield Tunneling. Underground Space (China), 6(4), 353–63.
Zhang, Z., & Huang, M. (2014). Geotechnical Influence on Existing Subway Tunnels Induced by Multiline Tunneling in Shanghai Soft Soil. Computers and Geotechnics, 56, 121–132.
Zoveidavianpoor, M. (2014). A Comparative Study of Artificial Neural Network and Adaptive Neurofuzzy Inference System for Prediction of Compressional Wave Velocity. Neural Computing and Applications, 25(5), 1169–76.