Survival Analysis Using Cox Proportional Hazards Regression for Pile Bridge Piles Under Wet Service Conditions

Source:
By:Naiyi Li, Kuang-Yuan Hou, Yunchao Ye, Chung C. Fu

DOI: https://doi.org/10.30564/jaeser.v6i2.5690

Abstract:

This paper studies the deterioration of bridge substructures utilizing the Long-Term Bridge Performance (LTBP) Program InfoBridge^TM and develops a survival model using Cox proportional hazards regression. The survival analysis is based on the National Bridge Inventory (NBI) dataset. The study calculates the survival rate of reinforced and prestressed concrete piles on bridges under marine conditions over a 29-year span (from 1992 to 2020). The state of Maryland is the primary focus of this study, with data from three neighboring regions, the District of Columbia, Virginia, and Delaware to expand the sample size. The data obtained from the National Bridge Inventory are condensed and filtered to acquire the most relevant information for model development. The Cox proportional hazards regression is applied to the condensed NBI data with six parameters: Age, ADT, ADTT, number of spans, span length, and structural length. Two survival models are generated for the bridge substructures: Reinforced and prestressed concrete piles in Maryland and reinforced and prestressed concrete piles in wet service conditions in the District of Columbia, Maryland, Delaware, and Virginia. Results from the Cox proportional hazards regression are used to construct Markov chains to demonstrate the sequence of the deterioration of bridge substructures. The Markov chains can be used as a tool to assist in the prediction and decision-making for repair, rehabilitation, and replacement of bridge piles. Based on the numerical model, the Pile Assessment Matrix Program (PAM) is developed to facilitate the assessment and maintenance of current bridge structures. The program integrates the NBI database with the inspection and research reports from various states’ department of transportation, to serve as a tool for condition state simulation based on maintenance or rehabilitation strategies.

References:

[1] An-jie, W., Wan-li, Y., 2020. Numerical study of pile group effect on the hydrodynamic force on a pile of sea-crossing bridges during earthquakes. Ocean Engineering. 199, 106999. DOI: https://doi.org/10.1016/j.oceaneng.2020.106999 [2] Tu, W., Gu, X., Chen, H.P., et al., 2021. Time domain nonlinear kinematic seismic response of composite caisson-piles foundation for bridge in deep water. Ocean Engineering. 235, 109398. DOI: https://doi.org/10.1016/j.oceaneng.2021.109398 [3] Ding, Y., Ma, R., Shi, Y.D., et al., 2018. Underwater shaking table tests on bridge pier under combined earthquake and wave-current action. Marine Structures. 58, 301-320. DOI: https://doi.org/10.1016/j.marstruc.2017.12.004 [4] Walsh, M.T., Sagüés, A.A., 2016. Steel corrosion in submerged concrete structures—Part 1: Field observations and corrosion distribution modeling. Corrosion. 72(4), 518-533. DOI: https://doi.org/10.5006/1945 [5] Goyal, R., 2015. Development of a survival based framework for bridge deterioration modeling with large-scale application to the North Carolina bridge management system [PhD thesis]. Charlotte: The University of North Carolina. [6] Cox, D.R., 1972. Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological). 34(2), 187-202. DOI: https://doi.org/10.1111/j.2517-6161.1972.tb00899.x [7] Lemeshow, S., May, S., Hosmer Jr, D.W., 2011. Applied survival analysis: Regression modeling of time-to-event data. John Wiley & Sons: New York. [8] Greene, W.H., 1997. Econometric analysis. Prentice Hall, Inc.: Upper Saddle River. [9] Butt, A., Shahin, M.Y., Feighan, K.J., et al., 1987. Pavement performance prediction model using the Markov process. Transportation Research Record 1123: Pavement Management and Weigh in Motion. Transportation Research Board: Washington, DC. [10] Jiang, Y., Saito, M., Sinha, K.C., 1988. Bridge performance prediction model using the Markov chain. Transportation Research Record. 1180, 25-32. [11] Madanat, S., Mishalani, R., Ibrahim, W.H.W., 1995. Estimation of infrastructure transition probabilities from condition rating data. Journal of Infrastructure Systems. 1(2), 120-125. DOI: https://doi.org/10.1061/(asce)1076-0342(1995)1:2(120) [12] Rens, K.L., Nogueira, C.L., Neiman, Y.M., et al., 1999. Bridge management system for the city and county of Denver. Practice Periodical on Structural Design and Construction. 4(4), 131-136. [13] LTBP InfoBridge™ [Internet]. U.S. Department of Transportation Federal Highway Administration. Available from: https://infobridge.fhwa.dot.gov/ [14] National Bridge Inventory [Internet]. U.S. Department of Transportation Federal Highway Administration. Available from: https://www.fhwa.dot.gov/bridge/inspection/ [15] Cavalline, T.L., Whelan, M.J., Tempest, B.Q., et al., 2015. Determination of Bridge Deterioration Models and Bridge User Costs for the NCDOT Bridge Management System [Internet]. Available from: https://trid.trb.org/view/1405296 [16] Moser, R., Holland, B., Kahn, L., et al., 2011. Durability of Precast Prestressed Concrete Piles in Marine Environment: Reinforcement Corrosion and Mitigation [Internet]. Available from: https://rosap.ntl.bts.gov/view/dot/20616 [17] Federal Highway Administration, 1995. Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation's Bridges [Internet]. Available from: https://www.fhwa.dot.gov/bridge/mtguide.pdf