Modelling of cast-in-place concrete tunnel liners condition

References

• Ahmed, A., Saeed, T. U., & Labi, S. (2016). Estimation of rest periods for newly constructed/reconstructed pavements. Transport, 31(2), 183–191. doi:10.3846/16484142.2016.1193050
   Web of Science ®Google Scholar
• Ai, Q., Yuan, Y., Mahadevan, S., & Jiang, X. (2016). Probabilistic degradation modelling of circular tunnels assembled from segmental linings. Structural Concrete, 17(2), 257–273.
   Web of Science ®Google Scholar
• Al-Barqawi, H., & Zayed, T. (2006). Condition rating model for underground infrastructure sustainable water mains. Journal of Performance of Constructed Facilities, 20(2), 126–135.
   Web of Science ®Google Scholar
• Azari, H., Nazarian, S., & Yuan, D. (2014). Case study on tunnel lining assessment using stress wave-based nondestructive testing methods. In Geo-Congress 2014: Geo-Characterization and Modeling for Sustainability, Geo-Congress 2014, Atlanta, Georgia. pp. 439–452. doi:10.1061/9780784413272.042
   Google Scholar
• Baik, H., Jeong, H., & Abraham, D. (2006). Estimating transition probabilities in Marko Chain – Based deterioration models for management of wastewater systems. Journal of Water Resources Planning and Management, 132(1), 15–24. doi:10.1061/(ASCE)0733-9496(2006)132:1(15)
   Web of Science ®Google Scholar
• Bergeson, W., & Ernst, S. (2015). Tunnel operations, maintenance, inspection, and evaluation (TOMIE) manual. Report No. FHWA-HIF-15-005. Washington DC: Federal Highway Administration, Office of Bridges and Structures.
   Google Scholar
• Boes, S., & Winkelmann, R. (2006). Ordered response models. In Modern econometric analysis (pp. 167–181). Berlin, Heidelberg: Springer.
   Google Scholar
• Chapman, D. N., Metje, N., & Stark, A. (2017). Introduction to tunnel construction. Boca Raton, FL: CRC Press.
   Google Scholar
• Chen, D., & Mastin, N. (2016). Sigmoidal models for predicting pavement performance conditions. Journal of Performance of Constructed Facilities, 30(4), 04015078. doi:10.1061/(ASCE)CF.1943-5509.0000833
   Web of Science ®Google Scholar
• Cividini, A., Contini, A., Locatelli, L., & Gioda, G. (2012). Investigation on the cause of damages of a deep tunnel. International Journal of Geomechanics, 12(6), 722–731. doi:10.1061/(ASCE)GM.1943-5622.0000108
   Web of Science ®Google Scholar
• Elwakil, E., Eweda, A., & Zayed, T. (2014). Modelling the effect of various factors on the condition of pavement marking. Structure and Infrastructure Engineering, 10(1), 93–105. doi:10.1080/15732479.2012.701650
   Web of Science ®Google Scholar
• Fares, H., Shahata, K., Elwakil, E., Eweda, A., Zayed, T., Abdelrahman, M., & Basha, I. (2012). Modelling the performance of pavement marking in cold weather conditions. Structure and Infrastructure Engineering, 8(11), 1067–1079. doi:10.1080/15732479.2010.504212
   Web of Science ®Google Scholar
• FHWA (Federal Highway Administration). (2019). Download NTI Data, 2018 Data. Retrieved from https://www.fhwa.dot.gov/bridge/inspection/tunnel/inventory/download.cfm
   Google Scholar
• FHWA (Federal Highway Administration). (2015). Specifications for the National Tunnel Inventory. Report No. FHWA-HIF-15-006. Washington, DC: Office of Infrastructure, Office of Bridges and Structures.
   Google Scholar
• Frangopol, D. M. (2011). Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges 1. Structure and Infrastructure Engineering, 7(6), 389–413. doi:10.1080/15732471003594427
   Web of Science ®Google Scholar
• Greene, W. H. (2008). NLOGIT Version 4.0, student reference guide, Econometric Software.
   Google Scholar
• Hasan, M. S., Setunge, S., Law, D. W., & Koay, Y. C. (2015). Forecasting deterioration of bridge components from visual inspection data. International Journal of Engineering and Technology, 7(1), 40–44. doi:10.7763/IJET.2015.V7.763
   Google Scholar
• Idris, J., Verdel, T., & Al-Heib, M. (2008). Numerical modelling and mechanical behaviour analysis of ancient tunnel masonry structures. Tunnelling and Underground Space Technology, 23(3), 251–263. doi:10.1016/j.tust.2007.04.006
   Web of Science ®Google Scholar
• Japan Road Bureau (JRB). (2014). Road tunnel routine inspection manual. Tokyo: Japan Road Bureau of Ministry of Land, Infrastructure, Transport and Tourism.
   Google Scholar
• Johnston, C., McDonald, J., & Quist, K. (2019). A generalized ordered Probit model. Communications in Statistics – Theory and Methods, 1–18. doi:10.1080/03610926.2019.1565780
   Web of Science ®Google Scholar
• Kamel, T., Limam, A., & Silvani, C. (2016). Residual strength of underground structures in service. Canadian Geotechnical Journal, 53(6), 988–999. doi:10.1139/cgj-2014-0554
   Web of Science ®Google Scholar
• Kamel, T., Silvani, C., & Breul, P. (2017). Numerical modelling of the deterioration of the Parisian metro infrastructures based on a new auscultation method. Structure and Infrastructure Engineering, 13(10), 1284–1293. doi:10.1080/15732479.2016.1263672
   Web of Science ®Google Scholar
• Kırbaş, U., & Karaşahin, M. (2016). Performance models for hot mix asphalt pavements in urban roads. Construction and Building Materials, 116, 281–288. doi:10.1016/j.conbuildmat.2016.04.118
   Web of Science ®Google Scholar
• Kuesel, T. R., King, E. H., & Bickel, J. O. (1996). Tunnel engineering handbook. New York: Springer. doi:10.1007/978-1-4613-0449-4
   Google Scholar
• Larsson Ivanov, O., Honfi, D., Santandrea, F., & Stripple, H. (2019). Consideration of uncertainties in LCA for infrastructure using probabilistic methods. Structure and Infrastructure Engineering, 15(6), 711–724. doi:10.1080/15732479.2019.1572200
   Web of Science ®Google Scholar
• Luo, Z. (2013). Pavement performance modelling with an auto-regression approach. International Journal of Pavement Engineering, 14(1), 85–94. doi:10.1080/10298436.2011.617442
   Web of Science ®Google Scholar
• Mannering, F. L., Shankar, V., & Bhat, C. R. (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research, 11, 1–16. doi:10.1016/j.amar.2016.04.001
   Web of Science ®Google Scholar
• Meguid, M. A., & Dang, H. K. (2009). The effect of erosion voids on existing tunnel linings. Tunnelling and Underground Space Technology, 24(3), 278–286. doi:10.1016/j.tust.2008.09.002
   Web of Science ®Google Scholar
• Osorio-Lird, A., Chamorro, A., Videla, C., Tighe, S., & Torres-Machi, C. (2018). Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 14(9), 1169–1181. doi:10.1080/15732479.2017.1402064
   Web of Science ®Google Scholar
• Pandey, M. D., Yuan, X. X., & Van Noortwijk, J. M. (2009). The influence of temporal uncertainty of deterioration on life-cycle management of structures. Structure and Infrastructure Engineering, 5(2), 145–156. doi:10.1080/15732470601012154
   Web of Science ®Google Scholar
• Paul, S. L., Hendron, A. J., Cording, E. J., Sgouros, G. E., & Saha, P. K. (1983). Design recommendations for concrete tunnel linings. Volume II-Summary of research and proposed recommendations. Washington, D.C.: U.S. Department of Transportation, Urban Mass Transportation Administration.
   Google Scholar
• Puz, G., & Radic, J. (2011). Life-cycle performance model based on homogeneous Markov processes. Structure and Infrastructure Engineering, 7(4), 285–296. doi:10.1080/15732470802532943
   Web of Science ®Google Scholar
• Saeed, T. U., Qiao, Y., Chen, S., Gkritza, K., & Labi, S. (2017). Methodology for probabilistic modeling of highway bridge infrastructure condition: Accounting for improvement effectiveness and incorporating random effects. Journal of Infrastructure Systems, 23(4), 04017030. doi:10.1061/(ASCE)IS.1943-555X.0000389
   Web of Science ®Google Scholar
• Sobanjo, J. O. (2011). State transition probabilities in bridge deterioration based on Weibull sojourn times. Structure and Infrastructure Engineering, 7(10), 747–764. doi:10.1080/15732470902917028
   Web of Science ®Google Scholar
• Volovski, M., Murillo-Hoyos, J., Saeed, T. U., & Labi, S. (2017). Estimation of routine maintenance expenditures for highway pavement segments: Accounting for heterogeneity using random-effects models. Journal of Transportation Engineering, Part A: Systems, 143(5), 04017006. doi:10.1061/JTEPBS.0000041
   Web of Science ®Google Scholar
• Wang, J. H. (2018). Lifecycle cost and performance analysis for repair of concrete tunnels. In Eco-efficient repair and rehabilitation of concrete infrastructures (pp. 637–672). Duxford, England: Woodhead. doi:10.1016/B978-0-08-102181-1.00023-X
   Google Scholar
• Wang, H., Huang, H., Feng, Y., & Zhang, D. (2018). Characterization of crack and leakage defects of concrete linings of road tunnels in China. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(4), 04018041. doi:10.1061/AJRUA6.0000989
   Google Scholar
• Wang, J., Huang, H., Xu, P., Xie, S., & Wong, S. C. (2019). Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings. Journal of Transportation Safety & Security, 1–20. doi:10.1080/19439962.2018.1551257
   Google Scholar
• Washington, S. P., Karlaftis, M. G., & Mannering, F. (2011). Statistical and econometric methods for transportation data analysis (2nd Ed.). Boca Raton, FL: Chapman and Hall/CRC.
   Google Scholar
• Wirahadikusumah, R., Abraham, D., & Iseley, T. (2001). Challenging issues in modeling deterioration of combined sewers. Journal of Infrastructure Systems, 7(2), 77–84. doi:10.1061/(ASCE)1076-0342(2001)7:2(77)
   Google Scholar
• Yamany, M. S., Saeed, T. U., & Hall, T. (2019). Probabilistic estimation of pavement condition across local and arterial rural roads. 105th Purdue Road School Transportation Conference & Expo, March 4–7, 2019, Purdue University, West Lafayette, IN, USA.
   Google Scholar
• Yamany, M. S., Saeed, T. U., & Volovski, M. (2019). Performance prediction of interstate flexible pavement across the Midwestern United States: Random-parameter regression vs artificial neural network. Transportation Research Board 98th Annual Meeting, January 13–17, 2019. Washington, DC, USA.
   Google Scholar
• Yu, T., Zhu, A., & Chen, Y. (2017). Efficient crack detection method for tunnel lining surface cracks based on infrared images. Journal of Computing in Civil Engineering, 31(3, 04016067. doi:10.1061/(ASCE)CP.1943-5487.0000645
   Web of Science ®Google Scholar
• Yuan, C., Li, S., & Li, S. (2011). Study on the defects characteristics of water leaking of old tunnels in cold regions. In Tunnel management, emerging technologies, and innovation, GeoHunan International Conference 2011. Hunan, China.pp. 187–193. doi:10.1061/47632(411)24
   Google Scholar
• Yuan, Y., Bai, Y., & Liu, J. (2012). Assessment service state of tunnel structure. Tunnelling and Underground Space Technology, 27(1), 72–85. doi:10.1016/j.tust.2011.07.002
   Web of Science ®Google Scholar