Parametric analysis of mixshield tunnelling in mixed ground containing mudstone and protection of adjacent buildings: case study in Nanning metro

References

• Abpeykar, S., & Ghatee, M. (2014). Supervised and unsupervised learning DSS for incident management in intelligent tunnel: A case study in Tehran Niayesh tunnel. Tunnelling and Underground Space Technology, 42, 293–306. doi:10.1016/j.tust.2014.03.008
   Web of Science ®Google Scholar
• Adoko, A. C., Jiao, Y. Y., Wu, L., Wang, H., & Wang, Z. H. (2013). Predicting tunnel convergence using multivariate adaptive regression spline and artificial neural network. Tunnelling and Underground Space Technology, 38, 368–376. doi:10.1016/j.tust.2013.07.023
   Web of Science ®Google Scholar
• Arthur, D., & Vassilvitskii, S. (2007). K-Means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 8, 1027–1025. doi:10.1145/1283383.1283494
   Google Scholar
• Avunduk, E., Tumac, D., & Atalay, A. K. (2014). Prediction of roadheader performance by artificial neural network. Tunnelling and Underground Space Technology, 44, 3–9. doi:10.1016/j.tust.2014.07.003
   Web of Science ®Google Scholar
• Bai, Y., Yang, Z., & Jiang, Z. (2014). Key protection techniques adopted and analysis of influence on adjacent buildings due to the Bund Tunnel construction. Tunnelling and Underground Space Technology, 41, 24–34. doi:10.1016/j.tust.2013.11.005
   Web of Science ®Google Scholar
• Barzegari, G., Uromeihy, A., & Zhao, J. (2013). EPB–TBM tunnelling issues on mixed faced ground at Tabriz Metro line 1, Iran. In G. Anagnostou and H. Ehrbar (Eds.), Underground. The way to the future (pp. 2062–2069). Geneva, Switzerland: CRC Press. doi:10.1201/b14769-282
   Google Scholar
• Benardos, A. G., & Kaliampakos, D. C. (2004). Modelling TBM performance with artificial neural networks. Tunnelling and Underground Space Technology, 19, 597–605. doi:10.1016/j.tust.2004.02.128
   Web of Science ®Google Scholar
• Bilgin, N., Copur, H., & Balci, C. (2012). Effect of replacing disc cutters with chisel tools on performance of a TBM in difficult ground conditions. Tunnelling and Underground Space Technology, 27, 41–51. doi:10.1016/j.tust.2011.06.006
   Web of Science ®Google Scholar
• Cheng, M. Y., & Hoang, N. D. (2014). A novel groutability estimation model for ground improvement projects in sandy silt soil based on Bayesian framework. Tunnelling and Underground Space Technology, 43, 453–458. doi:10.1016/j.tust.2014.07.001
   Web of Science ®Google Scholar
• Cui, Q.-L., Wu, H.-N., Shen, S.-L., Yin, Z.-Y., & Horpibulsuk, S. (2016). Protection of neighbour buildings due to construction of shield tunnel in mixed ground with sand over weathered granite. Environmental Earth Sciences, 75, 249. doi:10.1007/s12665-016-5300-7
   Web of Science ®Google Scholar
• Diez, A., Khoa, N. L. D., Makki Alamdari, M., Wang, Y., Chen, F., & Runcie, P. (2016). A clustering approach for structural health monitoring on bridges. Journal of Civil Structural Health Monitoring, 6, 429–445. doi:10.1007/s13349-016-0160-0
   Web of Science ®Google Scholar
• Ding, L., Ma, L., Luo, H., Yu, M., & Wu, X. (2011). Wavelet analysis for tunneling-induced ground settlement based on a stochastic model. Tunnelling and Underground Space Technology, 26, 619–628. doi:10.1016/j.tust.2011.03.005
   Web of Science ®Google Scholar
• Farrou, I., Kolokotroni, M., & Santamouris, M. (2012). A method for energy classification of hotels: A case-study of Greece. Energy and Buildings, 55, 553–562. doi:10.1016/j.enbuild.2012.08.010
   Web of Science ®Google Scholar
• Feinendegen, M., Ziegler, M., Spagnoli, G., Fernandez-Steeger, T., & Stanjek, H. (2010). A new laboratory test to evaluate the problem of clogging in mechanical tunnel driving with EPB-shields. In J. Zhao, V. Labiouse, & J.-F. Dudt (Eds.), ISRM International Symposium-EUROCK 2010 (pp. 429–432). Lausanne, Switzerland: Taylor & Francis Group. Retrieved from https://www.academia.edu/download/43386603/A_New_Laboratory_Test_to_Evaluate_the_Pr20160305-30686-1ouknha.pdf
   Google Scholar
• Geng, Q., Wei, Z., Meng, H., & Macias, F. J. (2016). Mechanical performance of TBM cutterhead in mixed rock ground conditions. Tunnelling and Underground Space Technology, 57, 76–84. doi:10.1016/j.tust.2016.02.012
   Web of Science ®Google Scholar
• Goel, R. K. (2016). Experiences and lessons from the use of TBM in the Himalaya – A review. Tunnelling and Underground Space Technology, 57, 277–283. doi:10.1016/j.tust.2016.02.015
   Web of Science ®Google Scholar
• Guo, J., Ding, L., Luo, H., Zhou, C., & Ma, L. (2014). Wavelet prediction method for ground deformation induced by tunneling. Tunnelling and Underground Space Technology, 41, 137–151. doi:10.1016/j.tust.2013.12.009
   Web of Science ®Google Scholar
• Heuser, M., Spagnoli, G., Leroy, P., Klitzsch, N., & Stanjek, H. (2012). Electro-osmotic flow in clays and its potential for reducing clogging in mechanical tunnel driving. Bulletin of Engineering Geology and the Environment, 71, 721–733. doi:10.1007/s10064-012-0431-x
   Web of Science ®Google Scholar
• Hollmann, F. S., & Thewes, M. (2013). Assessment method for clay clogging and disintegration of fines in mechanised tunnelling. Tunnelling and Underground Space Technology, 37, 96–106. doi:10.1016/j.tust.2013.03.010
   Web of Science ®Google Scholar
• Iphar, M. (2012). ANN and ANFIS performance prediction models for hydraulic impact hammers. Tunnelling and Underground Space Technology, 27, 23–29. doi:10.1016/j.tust.2011.06.004
   Web of Science ®Google Scholar
• Lau, S. C., Lu, M., & Ariaratnam, S. T. (2010). Applying radial basis function neural networks to estimate next-cycle production rates in tunnelling construction. Tunnelling and Underground Space Technology, 25, 357–365. doi:10.1016/j.tust.2010.01.010
   Web of Science ®Google Scholar
• Leu, S.-S., Chen, C.-N., & Chang, S.-L. (2001). Data mining for tunnel support stability: Neural network approach. Automation in Construction, 10, 429–441. doi:10.1016/S0926-5805(00)00078-9
   Web of Science ®Google Scholar
• Li, X. G., & Yuan, D. J. (2012). Response of a double-decked metro tunnel to shield driving of twin closely under-crossing tunnels. Tunnelling and Underground Space Technology, 28, 18–30. doi:10.1016/j.tust.2011.08.005
   Web of Science ®Google Scholar
• Liao, S. M., Shi, Z. H., Jiao, Q. Z., & Liu, H. K. (2012). Clogging induced Problems and Countermeasures of Large Slurry. In ITA-AITES World Tunnel Congress. Bangkok.
   Google Scholar
• Lin, C., Zhang, Z., Wu, S., & Yu, F. (2013). Key techniques and important issues for slurry shield under-passing embankments: A case study of Hangzhou Qiantang River Tunnel. Tunnelling and Underground Space Technology, 38, 306–325. doi:10.1016/j.tust.2013.07.004
   Web of Science ®Google Scholar
• Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28, 129–137. doi:10.1109/TIT.1982.1056489
   Web of Science ®Google Scholar
• Lu, H. H., Zhuo, F. C., Bai, Y., & Li, Q. S. (2014). Research on key techniques of shield construction in mixed ground. Applied Mechanics and Materials, 513–517, 3482–3488. doi:10.4028/www.scientific.net/AMM.513-517.3482
   Google Scholar
• Ma, H. S., Yin, L. J., Gong, Q. M., & Wang, J. (2015). TBM tunneling in mixed-face ground: Problems and solutions. International Journal of Mining Science and Technology, 25, 641–647. doi:10.1016/j.ijmst.2015.05.019
   Web of Science ®Google Scholar
• Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297). Berkeley, CA: University of California Press. doi:citeulike-article-id:6083430
   Google Scholar
• Mahdevari, S., Shirzad Haghighat, H., & Torabi, S. R. (2013). A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunnelling and Underground Space Technology, 38, 59–68. doi:10.1016/j.tust.2013.05.002
   Web of Science ®Google Scholar
• Mahdevari, S., & Torabi, S. R. (2012). Prediction of tunnel convergence using artificial neural networks. Tunnelling and Underground Space Technology, 28, 218–228. doi:10.1016/j.tust.2011.11.002
   Web of Science ®Google Scholar
• MathWorks. (n.d.). MATLAB R2016b. Natick, MA. Retrieved from https://www.mathworks.com/
   Google Scholar
• Mohamad, I. Bin, & Usman, D. (2013). Standardization and its effects on K -means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology, 6, 3299–3303.
   Google Scholar
• N. Lam, N. S., Reams, M., Li, K., Li, C., & Mata, L. P. (2016). Measuring community resilience to coastal hazards along the northern gulf of Mexico. Natural Hazards Review, 17, 04015013. doi:10.1061/(ASCE)NH.1527-6996.0000193
   PubMed Web of Science ®Google Scholar
• Namli, M., & Bilgin, N. (2017). A model to predict daily advance rates of EPB-TBMs in a complex geology in Istanbul. Tunnelling and Underground Space Technology, 62, 43–52. doi:10.1016/j.tust.2016.11.008
   Web of Science ®Google Scholar
• Ninić, J., & Meschke, G. (2015). Model update and real-time steering of tunnel boring machines using simulation-based meta models. Tunnelling and Underground Space Technology, 45, 138–152. doi:10.1016/j.tust.2014.09.013
   Web of Science ®Google Scholar
• Ratrout, N. (2011). Subtractive clustering-based K-means technique for determining optimum time-of-day breakpoints. Journal of Computing in Civil Engineering, 25, 380–387. doi:10.1061/(asce)cp.1943-5487.0000099
   Web of Science ®Google Scholar
• Roca-Pardiñas, J., Argüelles-Fraga, R., de Asís López, F., & Ordóñez, C. (2014). Analysis of the influence of range and angle of incidence of terrestrial laser scanning measurements on tunnel inspection. Tunnelling and Underground Space Technology, 43, 133–139. doi:10.1016/j.tust.2014.04.011
   Web of Science ®Google Scholar
• Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. doi:10.1016/0377-0427(87)90125-7
   Web of Science ®Google Scholar
• Sass, I., & Burbaum, U. (2009). A method for assessing adhesion of clays to tunneling machines. Bulletin of Engineering Geology and the Environment, 68, 27–34. doi:10.1007/s10064-008-0178-6
   Web of Science ®Google Scholar
• Schoesser, A. B., & Bochum, R. (2015). Marsh Funnel testing for rheology analysis of bentonite slurries for Slurry Shields. In ITA WTC 2015 Congress and 41st General Assembly. Dubrovnik.
   Google Scholar
• Schubert, W., & Moritz, B. (2015). The state of the art in monitoring and geotechnical safety management for shallow and deep tunnels. Geomechanics and Tunnelling, 8, 409–413. doi:10.1002/geot.201500019
   Google Scholar
• Spagnoli, G., Feinendegen, M., & Fernandez-Steeger, T. (2011). Influence of salt solutions on the undrained shear strength and the clogging of smectite-quartz mixtures. Environmental and Engineering Geoscience, 17, 293–305. doi:10.2113/gseegeosci.17.3.293
   Web of Science ®Google Scholar
• Spagnoli, G., & Fernandez-steeger, T. M. (2011). Evaluation of the clogging potential in mechanical tunnel driving with Evaluation of the clogging potential in mechanical tunnel driving with EPB-shields. In C. Anagnostopoulos, A. Pachakis, & M. Tsatsanifos (Ed.), Proceedings of the 15th European Conference on Soil Mechanics and Geotechnical Engineering. Athens: IOS PRESS. doi:10.3233/978-1-60750-801-4-1633
   Google Scholar
• 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, 133–150. doi:10.1016/j.tust.2005.06.007
   Web of Science ®Google Scholar
• Thewes, M., Schoesser, B., & Zizka, Z. (2016). Transient face support in slurry shield tunneling due to different time scales for excavation sequence of cutting tools and penetration time of support fluid. ITA-AITES World Tunnel Congress 2016, WTC 2016 (pp. 2465–2474). San Francisco, CA, United states: Society for Mining, Metallurgy and Exploration.
   Google Scholar
• Tóth, Á., Gong, Q., & Zhao, J. (2013). Case studies of TBM tunneling performance in rock–soil interface mixed ground. Tunnelling and Underground Space Technology, 38, 140–150. doi:10.1016/j.tust.2013.06.001
   Web of Science ®Google Scholar
• Vashani, H., Sullivan, J., & El Asmar, M. (2016). DB 2020: Analyzing and forecasting design-build market trends. Journal of Construction Engineering and Management, 142(6), 1–12. doi:10.1061/(ASCE)CO.1943-7862
   Web of Science ®Google Scholar
• Wang, Z., Li, X., Peng, K., & Xie, J. (2015). Impact of blasting parameters on vibration signal spectrum: Determination and statistical evidence. Tunnelling and Underground Space Technology, 48, 94–100. doi:10.1016/j.tust.2015.02.004
   Web of Science ®Google Scholar
• Xie, X., Wang, Q., Liu, H., Li, J., & Qi, Y. (2016). Settlement control study of shield tunnelling crossing railway station in round gravel strata. Chinese Journal of Rock Mechanics and Engineering, 35, 3960–3970. doi:10.13722/j.cnki.jrme.2016.0805
   Google Scholar
• Xie, X., Yang, Y., & Ji, M. (2016). Analysis of ground surface settlement induced by the construction of a large-diameter shield-driven tunnel in Shanghai, China. Tunnelling and Underground Space Technology, 51, 120–132. doi:10.1016/j.tust.2015.10.008
   Web of Science ®Google Scholar
• Zhang, K., Yu, H., Liu, Z., & Lai, X. (2010). Dynamic characteristic analysis of TBM tunnelling in mixed-face conditions. Simulation Modelling Practice and Theory, 18, 1019–1031. doi:10.1016/j.simpat.2010.03.005
   Web of Science ®Google Scholar
• Zhao, H., Ru, Z., Chang, X., Yin, S., & Li, S. (2014). Reliability analysis of tunnel using least square support vector machine. Tunnelling and Underground Space Technology, 41, 14–23. doi:10.1016/j.tust.2013.11.004
   Web of Science ®Google Scholar
• Zhao, J., Gong, Q. M., & Eisensten, Z. (2007). Tunnelling through a frequently changing and mixed ground: A case history in Singapore. Tunnelling and Underground Space Technology, 22, 388–400. doi:10.1016/j.tust.2006.10.002
   Web of Science ®Google Scholar
• Zhu, J.-J., Segovia, J., & Anderson, P. R. (2015). Defining influent scenarios: Application of cluster analysis to a water reclamation plant. Journal of Environmental Engineering, 141, 04015005. doi:10.1061/(ASCE)EE.1943-7870.0000934
   Web of Science ®Google Scholar
• Zumsteg, R., Plötze, M., & Puzrin, A. M. (2013). Reduction of the clogging potential of clays: New chemical applications and novel quantification approaches. Géotechnique, 63, 276–286. doi:10.1680/geot.SIP13.P.005
   Web of Science ®Google Scholar
• Zumsteg, R., & Puzrin, A. M. (2012). Stickiness and adhesion of conditioned clay pastes. Tunnelling and Underground Space Technology, 31, 86–96. doi:10.1016/j.tust.2012.04.010
   Web of Science ®Google Scholar
• Zumsteg, R., Puzrin, A. M., & Anagnostou, G. (2016). Effects of slurry on stickiness of excavated clays and clogging of equipment in fluid supported excavations. Tunnelling and Underground Space Technology, 58, 197–208. doi:10.1016/j.tust.2016.05.006
   Web of Science ®Google Scholar