Enhancing Bridge Resilience and Sustainability by Assessing Material Durability and Preservation in Wet-Freeze Zones

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Owolabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7661874/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzes the historical and future deterioration of concrete, steel, and prestressed concrete bridges in wet-freeze zones using National Bridge Inventory (NBI) data from 1993–2024 and predictive survival models through 2070. Results show that prestressed concrete bridges are the most durable. Survival modeling under limited (PHDM-L) and maximum (MPM) preservation strategies highlights the impact of maintenance. Without adequate preservation, all bridge types decline significantly, while maximum preservation extends lifespan and maintains structural integrity. The findings emphasize the need for proactive maintenance and material selection. Prioritizing prestressed concrete for new construction can enhance sustainability and reduce long-term costs. Bridge Resilience Wet-Freeze Zones Material Durability Bridge Materials Survival Analysis Long-Term Bridge Performance (LTBP) Structural Deterioration Predictive Modeling Condition Ratings Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Bridges are a vital component of the public road system, enhancing regional connectivity and supporting economic growth (Mirzaei and Adey, 2015). Bridge management organizations throughout the world have spent years creating and improving Bridge Management Systems (BMS) to guarantee their durability and functionality (Calvert et al., 2020; Medina & Gonzalez, 2022). This research focused on material durability and preservation strategies to ensure the long-term sustainability and resilience of bridge infrastructure in wet-freeze zones. The findings aim to enhance performance monitoring and support the development of optimal preservation schedules for different bridge types. Ensuring a safe and efficient bridge transportation system is a complex challenge for transportation agencies. Factors such as material aging, structural deterioration, climatic factors, increasing traffic loads, the need for upgrades, modernization efforts, efficiency, and tight budget constraints all contribute to this challenge (Furuta et al., 2004; Kong & Frangopol, 2003; Stewart, 2003). The wet-freeze climatic zone can cause bridge damage like cracking, spalling, frost heave, and material breakdown. To handle heavy rain and freezing winters, careful choices in design, materials, and maintenance are essential, otherwise, these factors can lead to poor bridge performance over time (Chen et al., 2019). To facilitate informed decision-making by agencies operating under financial constraints, the implementation of asset management strategies is of paramount importance. The National Bridge Inventory (NBI) constitutes the most extensive and comprehensive database encompassing deck condition data from 1993 to date, thus providing significant opportunities for the development of deterioration models (Fleischhacker, 2020). Madanat et al. (1995) were among the first to use NBI data to model bridge deck deterioration. They focused on estimating the likelihood of a deck transitioning between condition ratings. At the time, most models followed a Markovian approach, meaning they did not account for how long a deck remained in a given condition before deteriorating further. Their study addressed this limitation by introducing an incremental model that factored in gradual deterioration over time, a more realistic approach for bridge decks. By refining the classification of bridge deck settings, Morcous et al. (2003) endeavored to enhance Markovian deterioration models by employing genetic algorithms to ascertain optimal categorizations of environmental conditions based on variables such as highway classification, average daily traffic (ADT), geographical region, and the average daily truck traffic (ADTT) on bridge data from Ministère des Transports du Québec, Canada. The findings led to the development of improved Markovian deterioration models. Markovian models exhibit simplicity but possess inherent limitations. They inadequately account for a bridge's historical condition in deterioration predictions, and estimating transition probabilities, particularly regarding maintenance, poses difficulties. Due to these drawbacks, alternative methods, Madanat et al. (1995) introduced the ordered probit model to establish a more robust correlation between deterioration and critical variables. Time-based models forecast the duration for changes in bridge component conditions. Their notable advantage lies in the capability to process both censored and uncensored datasets (Greene, 1997). Censored data involves partially known condition ratings, often resulting from missing inspections, while uncensored data is completely observed. Mauch & Madanat (2001) conducted this analysis by establishing stochastic duration models using the Cox proportional hazards strategy for Indiana bridge decks. Their framework incorporated variables such as span, deck width, wearing surface type, climate, traffic volume, bridge age, and functional classification. Sobanjo et al. (2010) examined Florida's NBI bridge components to formulate a reliability model, utilizing survival and hazard functions to evaluate deterioration rates. Their findings revealed expedited deterioration in bridge categories influenced by age and roadway type. Similarly, Tabatabai et al. (2011) developed a reliability model for Wisconsin's NBI bridge decks, further refining deterioration analysis using survival and hazard functions. This study employs a Bayesian Model-based predictive hazard modeling approach to forecast the condition performance of bridge materials in Maryland, Virginia, and Vermont using NBI inspection data (1993–2024) integrated into the Long-Term Bridge Performance (LTBP) Program. Data Selections Long-Term Bridge Performance (LTBP) Program The LTBP Program represents an ongoing research initiative focused on the systematic collection and dissemination of high-quality bridge data in the U.S. and Canada, thereby aiding the bridge sector's understanding of performance dynamics. The resultant data from this initiative will include an array of data-centric instruments encompassing predictive and forecasting models designed to enable bridge stakeholders to effectively improve their management strategies.( FHWA , 2025) Bridge Condition Rating Bridge condition assessments are critical for performance evaluation and informed decision-making (Liu & Zhang, 2020). The FHWA established the National Bridge Inventory (NBI) over five decades ago to compile extensive bridge evaluation data (Chase et al., 1999; Frangopol et al., 2001). Consequently, U.S. highway bridges have undergone inspections biennially in accordance with the National Bridge Inspection Standards (NBIS). Table 1 illustrates the condition rating (CR) scale ranging from 0 to 9 (FHWA, 1995). Table 1: Condition rating (CR), used in NBI (FHWA 1995). CR Condition cr Condition 0 Failed Condition 5 Fair condition 1 Imminent Failure condition 6 Satisfactory condition 2 Critical condition 7 Good condition 3 Serious condition 8 Very good condition 4 Poor condition 9 Excellent condition Time-in-Condition Rating (TICR) TICR served as the dependent variable for each bridge deck in both datasets, derived from NBI condition ratings (CR) spanning 1992 to 2016 (Nasrollahi & Washer, 2015). TICR signifies the evolution of a bridge deck's condition over time. Due to multiple CRs over time, various TICR values may arise. For example, Fig. 1 depicts a hypothetical bridge deck with TICR instances, emphasizing the problem of data censorship, where certain values are partially observable (Leung et al., 1997). Censorship arises from three main causes: unavailability of CR before 1992 or after 2016, missing CR records, and CR improvements from maintenance actions, defined as any activity enhancing the deck's condition rating (Ghonima et al., 2018). This form of censorship, termed right-censoring, affects over 70% of TICR values in NBI data, potentially leading to underestimation if not adequately managed. Missing CR records were interpolated for gaps of three years or less using predefined rules. For example, when missing data occurred between identical CR values, the gaps were filled with the same value as the adjacent CRs (e.g., the missing 1999 CR in Figure. 1 was replaced with CR = 7). If missing data occurred between differing CR values, the first and last missing values were assigned to the adjacent CRs, while intermediate values were randomly assigned based on those adjacent records (Ghonima et al., 2018). For gaps exceeding three years, no interpolation was performed, and TICR occurrences were considered censored. Properly addressing censorship is essential for accurate TICR statistics, and the methods for handling this issue are discussed in later sections. Survival Analysis Predictive modeling using survival analysis based on Bayes' theorem can help forecast when bridges are likely to degrade into poor condition, enabling proactive maintenance. Survival analysis is a reliability analysis that models time-to-event data while accounting for influencing factors (Fleischhacke et al., 2020). Its extensive use in biomedicine contrasts with its nascent application in bridge engineering (Li et al., 2022). Recent developments reveal its efficacy in predicting bridge deck performance deterioration (Tabatabai & Tabatabai, 2016; Tabatabai et al., 2015). Current research in this domain is categorized into survival process description, influencing factor analysis, and survival outcome prediction (Chen, 2019). A significant benefit of survival analysis lies in its management of incomplete or censored data, classified as left-censoring and right-censoring (Lee & Go, 1997). Left-censoring arises when a bridge’s condition rating (CR) surpasses a defined threshold, while right-censoring indicates the CR has yet to meet this threshold (Stevens et al., 2020). Survival analysis predominantly concentrates on survival time, a nonnegative random variable denoting the interval from a defined starting point to the occurrence of a specified event (Li & Li, 2022). Factors such as material type, traffic load, maintenance responsibility, and functional classification were evaluated in the LTBP program to forecast bridge performance. METHODOLOGY Data Selection This study used LTBP program data from 1993 to 2024, with 623,218 bridges available in the 2024 NBI upload. The selection criteria included Maryland, Vermont, and Virginia; bridge age of 50 years (assuming a 50-year design life); and materials (concrete, steel, and prestressed concrete). After filtering, 92 concrete, 208 steel, and 43 prestressed concrete bridges remained. These states, located in wet-freeze regions (FHWA HPMS, 2014), were chosen based on the 2024 ASCE Report Card, which rated Maryland (B), Vermont (B-), and Virginia (B) as having the highest bridge performance grades among 22 wet-freeze states (ASCE, 2024). LTBP Program Analysis The Bridge Conditions, Average Daily Truck Traffic (ADTT), Maintenance Responsibility, Function Class of Inventory Route, Deck Condition Rating, Superstructure Condition Rating, and Predictive Hazard Modeling (PHDM) for the bridge's future performance were analyzed using the Survival Model, an analytic tool incorporated into LTBP Program. This tool was used to forecast the future performance of the bridge when limited preservation and maximum preservation were done on the bridge (see Appendix A). An increment in the bridge count percentage indicated that either maintenance or rehabilitation had been done, thereby increasing the number of bridges RESULTS AND DISCUSSION The assessment of bridge conditions shows that prestress concrete bridges are the most durable among the materials analyzed. As shown in Figure 2, a significant 37.2% of these bridges are rated in good condition, and only 7% fall into the poor category. In contrast, steel bridges show the most signs of deterioration, with only 23.6% rated as good and 5.3% in poor condition. Concrete bridges are primarily in fair condition, with 67.4% falling into this category. These results highlight the reliability of prestress concrete for long-term infrastructure use. At the same time, steel bridges require more attention due to their higher susceptibility to wear, fatigue, and environmental factors like corrosion (Pipinato & Modena, 2010). When looking at traffic levels as shown in Figure 3, prestress concrete bridges are predominantly located in areas with low truck traffic. Nearly 98% of these bridges handle fewer than 500 trucks daily, with none exposed to more than 1,000 trucks. Steel and concrete bridges, however, show slightly higher truck volumes, with a small but notable percentage (1–1.1%) handling more than 2,000 trucks per day. The heavier traffic loads on steel and concrete bridges likely contribute to their faster rates of deterioration and signal the need for focused maintenance on bridges with high vehicle loads. Maintenance responsibilities for bridges largely fall under state highway agencies, which oversee 64.1% of concrete bridges and 68.3% of steel bridges. Prestress concrete bridges, however, have a more distributed maintenance system, with county and township agencies playing a larger role. This decentralized management highlights the need for better coordination between agencies, especially for urban bridges, where consistent maintenance is critical. Figure 4 shows the maintenance responsibilities of the bridges. The functional classification of bridges reveals that material types are often tied to location and road use. Prestress concrete bridges are frequently found on urban principal arterial interstates (41.9%), reflecting their strength and reliability for high-traffic routes. Steel bridges are primarily used on rural minor collector roads (38.9%), while concrete bridges dominate rural local roads (34.8%). This distribution reflects the tendency to use prestress concrete for critical, high-priority urban infrastructure and steel or concrete for less demanding rural routes. Figure 5 shows the functional classification of the bridge. The condition ratings of bridge components deck, superstructure, and substructure in Figure 6 show that prestress concrete consistently performs better across the board. For instance, 31.7% of prestress concrete decks achieve high ratings of 8 or more, while only 12.9% of steel decks meet the same standard. Similarly, prestress concrete superstructures and substructures are rated significantly higher than their steel and concrete counterparts. Steel, on the other hand, demonstrates significant variability, with a large portion of its components falling into mid-range ratings, indicating a need for regular inspections and maintenance to avoid further deterioration. The results of the predictive and historical performance assessments offer important new information about the longevity and state of bridges according to preservation techniques and material types. Historically, as shown in Figure 7(a-c), bridges made of prestressed concrete (PC) demonstrate superior durability compared to steel (S) and regular concrete (C). For example, from 1993 to 2024, 37.21% of prestressed concrete bridges remained in good condition (PC-G), while 31.52% of concrete bridges (C-G) and only 23.56% of steel bridges (S-G) were in good condition during the same period. This highlights the resilience of prestressed concrete against environmental and traffic-induced stresses (Nasrollahi & Washer, 2015; Fleischhacke et al., 2020). CONCLUSION This study highlights the superior durability of prestressed concrete bridges compared to steel and concrete and demonstrates the resilience of prestressed concrete against environmental and traffic stresses, even with limited maintenance. The survival models further emphasize the importance of preservation strategies. Under limited preservation, all bridge types decline significantly over time, with steel and concrete deteriorating faster than prestressed concrete. However, maximum preservation slows deterioration across all materials, extending the service life of bridges. Prestressed concrete consistently outperforms other materials, even with limited maintenance, while maximum preservation further enhances its durability. In order to improve bridge performance and sustainability, prestressed concrete should be prioritized for future construction, particularly for high-traffic and critical routes. Maximum preservation strategies should be adopted to extend the lifespan of all bridge types, reducing long-term costs. In addition, steel and concrete bridges require frequent monitoring and maintenance to prevent rapid deterioration. Strategic allocation of resources is crucial, with high-risk bridges prioritized while maintaining care for prestressed concrete structures. Importantly, other states within the wet freeze zone should adopt more prestressed concrete bridges and frequently prioritize maximum maintenance on their deteriorating steel and concrete bridges. Other climatic regions. Lastly, material selection and proactive preservation are vital for building resilient and sustainable bridge infrastructure. Prestressed concrete bridges, with maximum preservation efforts, ensure enhanced performance, safety, and cost efficiency for future networks. SIGNIFICANCE OF THE STUDY This study significantly provides a data-driven framework for understanding and improving the resilience of bridge infrastructure using the LTBP program developed by the FHWA, and also provides recommendations for states in the wet freeze zone. Declarations All authors have read through, understood, and complied as appropriate with the statement on “Ethical Responsibilities of Authors” as found in the ‘Instructions for Authors’ and are aware that, with minor exceptions, no changes can be made to authorship once the paper is submitted. ACKNOWLEDGEMENTS I sincerely appreciate the support of the Dean of the School of Engineering, my Advisor, and the Civil Engineering Department Chair throughout this research. I am also grateful to (LTBP) Program for its freely available data, which was essential to this study. Author and co‑author’s contribution: Akosile S. S.: Conceptualization, methodology, writing original draft, data curation; Owolabi O. A.: Supervision and interpretation of results. Data availability: The raw data are available on reasonable requests from the authors. Code availability: Not applicable Ethical approval: Not Applicable. Consent to participate: Not Applicable. Consent to publish: The author and co-author agreed to publish this version of the research article. Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest related to this manuscript. Funding Declaration: This research did not receive any funding. Corresponding Author’s email: [email protected] References ASCE Report Card 2024 - https://infrastructurereportcard.org/?s=2024 Calvert, G., Neves, L., Andrews, J., & Hamer, M. (2020). Multi-defect modelling of bridge deterioration using truncated inspection records. Reliability Engineering & System Safety , 200 , 106962. Chase, S. B., Small, E. P., & Nutakor, C., (1999) An In-Depth Analysis of the National Bridge Inventory Database Utilizing Data Mining, GIS and Advanced Statistical Methods TRB Transportation Research Circular 498, PC-6/1~ C-6/17 Chen, L. P. (2019). Semiparametric estimation for cure survival model with left-truncated and right-censored data and covariate measurement error. Statistics & Probability Letters , 154 , 108547. Chen, S., You, Z., Sharifi, N. P., Yao, H., & Gong, F. (2019). Material selections in asphalt pavement for wet-freeze climate zones: A review. Construction and Building Materials , 201 , 510-525. Fang, Y., & Sun, L. (2019). Developing A semi-markov process model for bridge deterioration prediction in Shanghai. Sustainability , 11 (19), 5524. Federal Highway Administration (FHWA) 1995. Recording and coding guide for the structure inventory and appraisal of the nation’s bridges, Rep. No. FHWA-PD-96-001 Federal Highway Administration, Appendix I: Climate Zone Definitions, Highway Pavement Monitoring System Field Manual, U.S. Department of Transportation Federal Highway Administration, 2000. P. 1-1-1-2 Federal Highway Administration, Highway Performance Monitoring System (FHWA HPMS) Field Manual 2014. Fleischhacker, A., Ghonima, O., & Schumacher, T. (2020). Bayesian survival analysis for US concrete highway bridge decks. Journal of Infrastructure Systems , 26 (1), 04020001. Frangopol, D. M., Kong, J. S., & Gharaibeh, E. S. (2001). Reliability-based life-cycle management of highway bridges. Journal of computing in civil engineering , 15 (1), 27-34. Ghonima, O., Schumacher, T., Unnikrishnan, A., & Fleischhacker, A. (2018). Advancing Bridge Technology, Task 10: Statistical Analysis and Modeling of US Concrete Highway Bridge Deck Performance--Internal Final Report. Greene, W. H. 1997. Econometric analysis. New York: Macmillan. Kong, J. S., & Frangopol, D. M. (2003, April). System reliability modeling in bridge management. In Proc., 9th Int. Bridge Management Conf., IBMC03 (pp. 361-372). Washington, DC: Transportation Research Board of the National Academies. Lee, E. T., & Go, O. T. (1997). Survival analysis in public health research. Annual review of public health , 18 (1), 105-134. Leung, K. M., Elashoff, R. M., & Afifi, A. A. (1997). Censoring issues in survival analysis. Annual review of public health , 18 (1), 83-104. Li, L., Lu, Y., & Peng, M. (2022). Deterioration Model for Reinforced Concrete Bridge Girders Based on Survival Analysis. Mathematics , 10 (23), 4436. Li, Y., Li, L., & Ni, D. (2022). Comparative univariate and regression survival analysis of lane-changing duration characteristic for heavy vehicles and passenger cars. Journal of transportation engineering, Part A: Systems , 148 (12), 04022109. Liu, H., & Zhang, Y. (2020). Bridge condition rating data modeling using deep learning algorithm. Structure and Infrastructure Engineering , 16 (10), 1447-1460. LTBP program - https://infobridge.fhwa.dot.gov/ Madanat, S. M., Karlaftis, M. G., & McCarthy, P. S. (1997). Probabilistic infrastructure deterioration models with panel data. Journal of infrastructure systems , 3 (1), 4-9. 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. Mauch, M., & Madanat, S. (2001). Semiparametric hazard rate models of reinforced concrete bridge deck deterioration. Journal of Infrastructure Systems , 7 (2), 49-57. Medina, P. A., & González, J. L. (2022). Reinforced concrete long-term deterioration prediction for the implementation of a Bridge Management System. Materials Today: Proceedings , 58 , 1265-1271. Mirzaei, Z., & Adey, B. T. (2015). Investigation of the use of three existing methodologies to determine optimal life-cycle activity profiles for bridges. Structure and Infrastructure Engineering , 11 (11), 1484-1509. Morcous, G., Lounis, Z., & Mirza, M. S. (2003). Identification of environmental categories for Markovian deterioration models of bridge decks. Journal of Bridge Engineering , 8 (6), 353-361. Nashville, TE, USA, 22–26 May 2004; Blandford, G.E., Ed.; ASCE: Reston, VA, USA, 2004; pp. 19–23. Nasrollahi, M., & Washer, G. (2015). Estimating inspection intervals for bridges based on statistical analysis of national bridge inventory data. Journal of Bridge Engineering , 20 (9), 04014104. Pipinato, A., & Modena, C. (2010). Structural analysis and fatigue reliability assessment of the Paderno bridge. Practice Periodical on Structural Design and Construction , 15 (2), 109-124. Sobanjo, J., Mtenga, P., & Rambo-Roddenberry, M. (2010). Reliability-based modeling of bridge deterioration hazards. Journal of Bridge Engineering , 15 (6), 671-683. Stevens, N. A., Lydon, M., Marshall, A. H., & Taylor, S. (2020). Identification of bridge key performance indicators using survival analysis for future network-wide structural health monitoring. Sensors , 20 (23), 6894. Stewart, M. G. (2001). Reliability-based assessment of ageing bridges using risk ranking and life cycle cost decision analyses. Reliability Engineering & System Safety , 74 (3), 263-273. Survival analysis of concrete highway bridge decks in Oregon utilizing lasso and stepwise-variable selection. Journal of Bridge Engineering , 25 (10), 04020077. Tabatabai, H., Lee, C. W., & Tabatabai, M. A. (2015). Reliability of bridge decks in the United States. Bridge Structures , 11 (3), 75-85. Tabatabai, H., Lee, C. W., & Tabatabai, M. A. (2016). Survival analyses for bridge decks in Northern United States. Tabatabai, H., Tabatabai, M., & Lee, C. W. (2011). Reliability of bridge decks in Wisconsin. Journal of Bridge Engineering , 16 (1), 53-62. Wettach-Glosser, J., Unnikrishnan, A., & Schumacher, T. (2020). Additional Declarations No competing interests reported. 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13:18:49","extension":"xml","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68498,"visible":true,"origin":"","legend":"","description":"","filename":"4d19ae62d69b476faddd4a6102aacfbc1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/b1616fbb967f860237e55ba0.xml"},{"id":93593899,"identity":"83451429-5cdf-48ab-98e4-78b9cbda42b3","added_by":"auto","created_at":"2025-10-15 13:18:50","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78074,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/13746e0ac0e422a1f180d7a8.html"},{"id":93593638,"identity":"5cfb05f8-6fa6-49ef-bcfb-4b2cc7acca54","added_by":"auto","created_at":"2025-10-15 13:10:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182825,"visible":true,"origin":"","legend":"\u003cp\u003eCondition Rating history of fictitious bridge deck for accessible NBI Data (1992-2016) (Wettach-Glosser et al. 2020)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/8ea4c310b62ec5079bd31460.png"},{"id":93593637,"identity":"a507d209-1357-41aa-8bea-2f0f08da5616","added_by":"auto","created_at":"2025-10-15 13:10:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChart of Bridge Condition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/9e74bf1a404ae298ad79baf3.png"},{"id":93593639,"identity":"4272d045-e22e-4127-b9cf-93d778b7892e","added_by":"auto","created_at":"2025-10-15 13:10:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChart of Average Daily Truck Traffic (ADTT)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/3c171a41ad8ebb85b44425dd.png"},{"id":93593642,"identity":"2c3c616a-f455-4b0a-a5dd-adcae54fd966","added_by":"auto","created_at":"2025-10-15 13:10:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChart showing the Agency Responsible for the Bridge Maintenance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/a55c8f2415a0ec2a0e7cba20.png"},{"id":93593894,"identity":"32d85c9f-337c-455b-a8d0-05ddd0af1e11","added_by":"auto","created_at":"2025-10-15 13:18:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChart of Functional Classification against Bridge Count in Percentage\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/f3a99351bef177a657fb4c71.png"},{"id":93594857,"identity":"4f4c71f1-c3b2-425e-894a-bf62a4d28b7a","added_by":"auto","created_at":"2025-10-15 13:26:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Chart for Deck Condition Rating; (b) Chart for Super Structure Condition Rating; (c) Chart for Substructure Condition Rating\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/a325a59c56a6b46b866d82e5.png"},{"id":93595065,"identity":"b8918633-cfdf-4698-af5b-c28aa81bef66","added_by":"auto","created_at":"2025-10-15 13:34:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":89878,"visible":true,"origin":"","legend":"\u003cp\u003e(a-c): (a) History Performance for Good Bridge CR (b) History Performance for Fair Bridge CR (c) History Performance for Poor Bridge CR from 1993 to 2024\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/1d5678e9c26cceb16cc2e7cd.png"},{"id":93593640,"identity":"0b4dbf33-2f6c-4fe3-8599-287728c2b239","added_by":"auto","created_at":"2025-10-15 13:10:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":88019,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 9 (a-c): (a) Survival Model for Good Bridge CR (b) Survival Model for Fair Bridge CR (c) Survival Model for Poor Bridge CR from 2024 to 2070 for Maximum Preservation\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/8d18584ad87653a6793ac414.png"},{"id":99788123,"identity":"46700aca-c395-4d7b-8bee-64f805f8793c","added_by":"auto","created_at":"2026-01-08 12:45:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1102773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/928f5745-e5b8-47da-beb6-76882d29c2e2.pdf"},{"id":93593648,"identity":"49f4f8f2-15e8-4931-a1f2-f9b6aae7d183","added_by":"auto","created_at":"2025-10-15 13:10:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":90421,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIXA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7661874/v1/828dd3378004331bf3de9564.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Bridge Resilience and Sustainability by Assessing Material Durability and Preservation in Wet-Freeze Zones","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBridges are a vital component of the public road system, enhancing regional connectivity and supporting economic growth (Mirzaei and Adey, 2015). Bridge management organizations throughout the world have spent years creating and improving Bridge Management Systems (BMS) to guarantee their durability and functionality (Calvert et al., 2020; Medina \u0026amp; Gonzalez, 2022). This research focused on material durability and preservation strategies to ensure the long-term sustainability and resilience of bridge infrastructure in wet-freeze zones. The findings aim to enhance performance monitoring and support the development of optimal preservation schedules for different bridge types.\u003c/p\u003e\n\u003cp\u003eEnsuring a safe and efficient bridge transportation system is a complex challenge for transportation agencies. Factors such as material aging, structural deterioration, climatic factors, increasing traffic loads, the need for upgrades, modernization efforts, efficiency, and tight budget constraints all contribute to this challenge (Furuta et al., 2004; Kong \u0026amp; Frangopol, 2003; Stewart, 2003). The wet-freeze climatic zone can cause bridge damage like cracking, spalling, frost heave, and material breakdown. To handle heavy rain and freezing winters, careful choices in design, materials, and maintenance are essential, otherwise, these factors can lead to poor bridge performance over time (Chen et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo facilitate informed decision-making by agencies operating under financial constraints, the implementation of asset management strategies is of paramount importance. The National Bridge Inventory (NBI) constitutes the most extensive and comprehensive database encompassing deck condition data from 1993 to date, thus providing significant opportunities for the development of deterioration models (Fleischhacker, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMadanat et al. (1995) were among the first to use NBI data to model bridge deck deterioration. They focused on estimating the likelihood of a deck transitioning between condition ratings. At the time, most models followed a Markovian approach, meaning they did not account for how long a deck remained in a given condition before deteriorating further. Their study addressed this limitation by introducing an incremental model that factored in gradual deterioration over time, a more realistic approach for bridge decks.\u003c/p\u003e\n\u003cp\u003eBy refining the classification of bridge deck settings, Morcous et al. (2003) endeavored to enhance Markovian deterioration models by employing genetic algorithms to ascertain optimal categorizations of environmental conditions based on variables such as highway classification, average daily traffic (ADT), geographical region, and the average daily truck traffic (ADTT) on bridge data from Minist\u0026egrave;re des Transports du Qu\u0026eacute;bec, Canada. The findings led to the development of improved Markovian deterioration models.\u003c/p\u003e\n\u003cp\u003eMarkovian models exhibit simplicity but possess inherent limitations. They inadequately account for a bridge\u0026apos;s historical condition in deterioration predictions, and estimating transition probabilities, particularly regarding maintenance, poses difficulties. Due to these drawbacks, alternative methods, Madanat et al. (1995) introduced the ordered probit model to establish a more robust correlation between deterioration and critical variables.\u003c/p\u003e\n\u003cp\u003eTime-based models forecast the duration for changes in bridge component conditions. Their notable advantage lies in the capability to process both censored and uncensored datasets (Greene, 1997). Censored data involves partially known condition ratings, often resulting from missing inspections, while uncensored data is completely observed. Mauch \u0026amp; Madanat (2001) conducted this analysis by establishing stochastic duration models using the Cox proportional hazards strategy for Indiana bridge decks. Their framework incorporated variables such as span, deck width, wearing surface type, climate, traffic volume, bridge age, and functional classification.\u003c/p\u003e\n\u003cp\u003eSobanjo et al. (2010) examined Florida\u0026apos;s NBI bridge components to formulate a reliability model, utilizing survival and hazard functions to evaluate deterioration rates. \u0026nbsp;Their findings revealed expedited deterioration in bridge categories influenced by age and roadway type. Similarly, Tabatabai et al. (2011) developed a reliability model for Wisconsin\u0026apos;s NBI bridge decks, further refining deterioration analysis using survival and hazard functions. This study employs a Bayesian Model-based predictive hazard modeling approach to forecast the condition performance of bridge materials in Maryland, Virginia, and Vermont using NBI inspection data (1993\u0026ndash;2024) integrated into the Long-Term Bridge Performance (LTBP) Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Selections\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLong-Term Bridge Performance (LTBP) Program\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LTBP Program represents an ongoing research initiative focused on the systematic collection and dissemination of high-quality bridge data in the U.S. and Canada, thereby aiding the bridge sector\u0026apos;s understanding of performance dynamics. The resultant data from this initiative will include an array of data-centric instruments encompassing predictive and forecasting models designed to enable bridge stakeholders to effectively improve their management strategies.(\u003cem\u003e\u0026nbsp;FHWA\u003c/em\u003e, 2025)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBridge\u003c/strong\u003e \u003cstrong\u003eCondition Rating\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBridge condition assessments are critical for performance evaluation and informed decision-making (Liu \u0026amp; Zhang, 2020). The FHWA established the National Bridge Inventory (NBI) over five decades ago to compile extensive bridge evaluation data (Chase et al., 1999; Frangopol et al., 2001). Consequently, U.S. highway bridges have undergone inspections biennially in accordance with the National Bridge Inspection Standards (NBIS). Table 1 illustrates the condition rating (CR) scale ranging from 0 to 9 (FHWA, 1995).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 1: Condition rating (CR), used in NBI (FHWA 1995).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003ecr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eFailed Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eFair condition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eImminent\u0026nbsp;Failure condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSatisfactory condition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eCritical condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eGood condition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eSerious condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eVery\u0026nbsp;good condition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003ePoor condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eExcellent\u0026nbsp;condition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTime-in-Condition Rating (TICR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTICR served as the dependent variable for each bridge deck in both datasets, derived from NBI condition ratings (CR) spanning 1992 to 2016 (Nasrollahi \u0026amp; Washer, 2015). TICR signifies the evolution of a bridge deck\u0026apos;s condition over time. Due to multiple CRs over time, various TICR values may arise. For example, Fig. 1 depicts a hypothetical bridge deck with TICR instances, emphasizing the problem of data censorship, where certain values are partially observable (Leung et al., 1997). Censorship arises from three main causes: unavailability of CR before 1992 or after 2016, missing CR records, and CR improvements from maintenance actions, defined as any activity enhancing the deck\u0026apos;s condition rating (Ghonima et al., 2018). This form of censorship, termed right-censoring, affects over 70% of TICR values in NBI data, potentially leading to underestimation if not adequately managed.\u003c/p\u003e\n\u003cp\u003eMissing CR records were interpolated for gaps of three years or less using predefined rules. For example, when missing data occurred between identical CR values, the gaps were filled with the same value as the adjacent CRs (e.g., the missing 1999 CR in Figure. 1 was replaced with CR = 7). If missing data occurred between differing CR values, the first and last missing values were assigned to the adjacent CRs, while intermediate values were randomly assigned based on those adjacent records (Ghonima et al., 2018). For gaps exceeding three years, no interpolation was performed, and TICR occurrences were considered censored. Properly addressing censorship is essential for accurate TICR statistics, and the methods for handling this issue are discussed in later sections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePredictive modeling using survival analysis based on Bayes\u0026apos; theorem can help forecast when bridges are likely to degrade into poor condition, enabling proactive maintenance. Survival analysis is a reliability analysis that models time-to-event data while accounting for influencing factors (Fleischhacke et al., 2020). Its extensive use in biomedicine contrasts with its nascent application in bridge engineering (Li et al., 2022). Recent developments reveal its efficacy in predicting bridge deck performance deterioration (Tabatabai \u0026amp; Tabatabai, 2016; Tabatabai et al., 2015). Current research in this domain is categorized into survival process description, influencing factor analysis, and survival outcome prediction (Chen, 2019). A significant benefit of survival analysis lies in its management of incomplete or censored data, classified as left-censoring and right-censoring (Lee \u0026amp; Go, 1997). Left-censoring arises when a bridge\u0026rsquo;s condition rating (CR) surpasses a defined threshold, while right-censoring indicates the CR has yet to meet this threshold (Stevens et al., 2020). Survival analysis predominantly concentrates on survival time, a nonnegative random variable denoting the interval from a defined starting point to the occurrence of a specified event (Li \u0026amp; Li, 2022). Factors such as material type, traffic load, maintenance responsibility, and functional classification were evaluated in the LTBP program to forecast bridge performance.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003e\u003cstrong\u003eData Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used LTBP program data from 1993 to 2024, with 623,218 bridges available in the 2024 NBI upload. The selection criteria included Maryland, Vermont, and Virginia; bridge age of 50 years (assuming a 50-year design life); and materials (concrete, steel, and prestressed concrete). After filtering, 92 concrete, 208 steel, and 43 prestressed concrete bridges remained.\u003c/p\u003e\n\u003cp\u003eThese states, located in wet-freeze regions (FHWA HPMS, 2014), were chosen based on the 2024 ASCE Report Card, which rated Maryland (B), Vermont (B-), and Virginia (B) as having the highest bridge performance grades among 22 wet-freeze states (ASCE, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLTBP Program Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Bridge Conditions, Average Daily Truck Traffic (ADTT), Maintenance Responsibility, Function Class of Inventory Route, Deck Condition Rating, Superstructure Condition Rating, and Predictive Hazard Modeling (PHDM) for the bridge\u0026apos;s future performance were analyzed using the Survival Model, an analytic tool incorporated into LTBP Program. This tool was used to forecast the future performance of the bridge when limited preservation and maximum preservation were done on the bridge (see Appendix A).\u003c/p\u003e\n\u003cp\u003eAn increment in the bridge count percentage indicated that either maintenance or rehabilitation had been done, thereby increasing the number of bridges\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003eThe assessment of bridge conditions shows that prestress concrete bridges are the most durable among the materials analyzed. As shown in Figure 2, a significant 37.2% of these bridges are rated in good condition, and only 7% fall into the poor category. In contrast, steel bridges show the most signs of deterioration, with only 23.6% rated as good and 5.3% in poor condition. Concrete bridges are primarily in fair condition, with 67.4% falling into this category. These results highlight the reliability of prestress concrete for long-term infrastructure use. At the same time, steel bridges require more attention due to their higher susceptibility to wear, fatigue, and environmental factors like corrosion (Pipinato \u0026amp; Modena, 2010).\u003c/p\u003e\n\u003cp\u003eWhen looking at traffic levels as shown in Figure 3, prestress concrete bridges are predominantly located in areas with low truck traffic. Nearly 98% of these bridges handle fewer than 500 trucks daily, with none exposed to more than 1,000 trucks. Steel and concrete bridges, however, show slightly higher truck volumes, with a small but notable percentage (1\u0026ndash;1.1%) handling more than 2,000 trucks per day.\u003c/p\u003e\n\u003cp\u003eThe heavier traffic loads on steel and concrete bridges likely contribute to their faster rates of deterioration and signal the need for focused maintenance on bridges with high vehicle loads.\u003c/p\u003e\n\u003cp\u003eMaintenance responsibilities for bridges largely fall under state highway agencies, which oversee 64.1% of concrete bridges and 68.3% of steel bridges. Prestress concrete bridges, however, have a more distributed maintenance system, with county and township agencies playing a larger role. This decentralized management highlights the need for better coordination between agencies, especially for urban bridges, where consistent maintenance is critical. Figure 4 shows the maintenance responsibilities of the bridges.\u003c/p\u003e\n\u003cp\u003eThe functional classification of bridges reveals that material types are often tied to location and road use. Prestress concrete bridges are frequently found on urban principal arterial interstates (41.9%), reflecting their strength and reliability for high-traffic routes. Steel bridges are primarily used on rural minor collector roads (38.9%), while concrete bridges dominate rural local roads (34.8%). This distribution reflects the tendency to use prestress concrete for critical, high-priority urban infrastructure and steel or concrete for less demanding rural routes. Figure 5 shows the functional classification of the bridge.\u003c/p\u003e\n\u003cp\u003eThe condition ratings of bridge components deck, superstructure, and substructure in Figure 6 show that prestress concrete consistently performs better across the board. For instance, 31.7% of prestress concrete decks achieve high ratings of 8 or more, while only 12.9% of steel decks meet the same standard. Similarly, prestress concrete superstructures and substructures are rated significantly higher than their steel and concrete counterparts. Steel, on the other hand, demonstrates significant variability, with a large portion of its components falling into mid-range ratings, indicating a need for regular inspections and maintenance to avoid further deterioration.\u003c/p\u003e\n\u003cp\u003eThe results of the predictive and historical performance assessments offer important new information about the longevity and state of bridges according to preservation techniques and material types. Historically, as shown in Figure 7(a-c), bridges made of prestressed concrete (PC) demonstrate superior durability compared to steel (S) and regular concrete (C). For example, from 1993 to 2024, 37.21% of prestressed concrete bridges remained in good condition (PC-G), while 31.52% of concrete bridges (C-G) and only 23.56% of steel bridges (S-G) were in good condition during the same period. This highlights the resilience of prestressed concrete against environmental and traffic-induced stresses (Nasrollahi \u0026amp; Washer, 2015; Fleischhacke et al., 2020).\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study highlights the superior durability of prestressed concrete bridges compared to steel and concrete and demonstrates the resilience of prestressed concrete against environmental and traffic stresses, even with limited maintenance. The survival models further emphasize the importance of preservation strategies. Under limited preservation, all bridge types decline significantly over time, with steel and concrete deteriorating faster than prestressed concrete. However, maximum preservation slows deterioration across all materials, extending the service life of bridges. Prestressed concrete consistently outperforms other materials, even with limited maintenance, while maximum preservation further enhances its durability.\u003c/p\u003e\u003cp\u003eIn order to improve bridge performance and sustainability, prestressed concrete should be prioritized for future construction, particularly for high-traffic and critical routes. Maximum preservation strategies should be adopted to extend the lifespan of all bridge types, reducing long-term costs. In addition, steel and concrete bridges require frequent monitoring and maintenance to prevent rapid deterioration. Strategic allocation of resources is crucial, with high-risk bridges prioritized while maintaining care for prestressed concrete structures. Importantly, other states within the wet freeze zone should adopt more prestressed concrete bridges and frequently prioritize maximum maintenance on their deteriorating steel and concrete bridges. Other climatic regions.\u003c/p\u003e\u003cp\u003eLastly, material selection and proactive preservation are vital for building resilient and sustainable bridge infrastructure. Prestressed concrete bridges, with maximum preservation efforts, ensure enhanced performance, safety, and cost efficiency for future networks.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSIGNIFICANCE OF THE STUDY\u003c/h2\u003e\u003cp\u003eThis study significantly provides a data-driven framework for understanding and improving the resilience of bridge infrastructure using the LTBP program developed by the FHWA, and also provides recommendations for states in the wet freeze zone.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors have read through, understood, and complied as appropriate with the statement on \u0026ldquo;Ethical Responsibilities of Authors\u0026rdquo; as found in the \u0026lsquo;Instructions for Authors\u0026rsquo; and are aware that, with minor exceptions, no changes can be made to authorship once the paper is submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI sincerely appreciate the support of the Dean of the School of Engineering, my Advisor, and the Civil Engineering Department Chair throughout this research. I am also grateful to (LTBP) Program for its freely available data, which was essential to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor and co‑author\u0026rsquo;s contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAkosile S. S.: Conceptualization, methodology, writing original draft, data curation; Owolabi O. A.: Supervision and interpretation of results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe raw data are available on reasonable requests from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author and co-author agreed to publish this version of the research article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest related to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u0026rsquo;s email:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\[email protected] \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eASCE Report Card 2024 - https://infrastructurereportcard.org/?s=2024\u003c/li\u003e\n\u003cli\u003eCalvert, G., Neves, L., Andrews, J., \u0026amp; Hamer, M. (2020). Multi-defect modelling of bridge deterioration using truncated inspection records. \u003cem\u003eReliability Engineering \u0026amp; System Safety\u003c/em\u003e, \u003cem\u003e200\u003c/em\u003e, 106962.\u003c/li\u003e\n\u003cli\u003eChase, S. B., Small, E. P., \u0026amp; Nutakor, C., (1999) An In-Depth Analysis of the National Bridge Inventory Database Utilizing Data Mining, GIS and Advanced Statistical Methods TRB Transportation Research Circular 498, PC-6/1~ C-6/17\u003c/li\u003e\n\u003cli\u003eChen, L. P. (2019). Semiparametric estimation for cure survival model with left-truncated and right-censored data and covariate measurement error. \u003cem\u003eStatistics \u0026amp; Probability Letters\u003c/em\u003e, \u003cem\u003e154\u003c/em\u003e, 108547.\u003c/li\u003e\n\u003cli\u003eChen, S., You, Z., Sharifi, N. P., Yao, H., \u0026amp; Gong, F. (2019). Material selections in asphalt pavement for wet-freeze climate zones: A review. \u003cem\u003eConstruction and Building Materials\u003c/em\u003e, \u003cem\u003e201\u003c/em\u003e, 510-525.\u003c/li\u003e\n\u003cli\u003eFang, Y., \u0026amp; Sun, L. (2019). Developing A semi-markov process model for bridge deterioration prediction in Shanghai. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(19), 5524.\u003c/li\u003e\n\u003cli\u003eFederal Highway Administration (FHWA) 1995. Recording and coding guide for the structure inventory and appraisal of the nation\u0026rsquo;s bridges, Rep. No. FHWA-PD-96-001\u003c/li\u003e\n\u003cli\u003eFederal Highway Administration, Appendix I: Climate Zone Definitions, Highway Pavement Monitoring System Field Manual, U.S. Department of Transportation Federal Highway Administration, 2000. P. 1-1-1-2\u003c/li\u003e\n\u003cli\u003eFederal Highway Administration, Highway Performance Monitoring System (FHWA HPMS) Field Manual 2014. \u003c/li\u003e\n\u003cli\u003eFleischhacker, A., Ghonima, O., \u0026amp; Schumacher, T. (2020). Bayesian survival analysis for US concrete highway bridge decks. \u003cem\u003eJournal of Infrastructure Systems\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(1), 04020001.\u003c/li\u003e\n\u003cli\u003eFrangopol, D. M., Kong, J. S., \u0026amp; Gharaibeh, E. S. (2001). Reliability-based life-cycle management of highway bridges. \u003cem\u003eJournal of computing in civil engineering\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 27-34.\u003c/li\u003e\n\u003cli\u003eGhonima, O., Schumacher, T., Unnikrishnan, A., \u0026amp; Fleischhacker, A. (2018). Advancing Bridge Technology, Task 10: Statistical Analysis and Modeling of US Concrete Highway Bridge Deck Performance--Internal Final Report.\u003c/li\u003e\n\u003cli\u003eGreene, W. H. 1997. Econometric analysis. New York: Macmillan.\u003c/li\u003e\n\u003cli\u003eKong, J. S., \u0026amp; Frangopol, D. M. (2003, April). System reliability modeling in bridge management. In \u003cem\u003eProc., 9th Int. Bridge Management Conf., IBMC03\u003c/em\u003e (pp. 361-372). Washington, DC: Transportation Research Board of the National Academies.\u003c/li\u003e\n\u003cli\u003eLee, E. T., \u0026amp; Go, O. T. (1997). Survival analysis in public health research. \u003cem\u003eAnnual review of public health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 105-134.\u003c/li\u003e\n\u003cli\u003eLeung, K. M., Elashoff, R. M., \u0026amp; Afifi, A. A. (1997). Censoring issues in survival analysis. \u003cem\u003eAnnual review of public health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 83-104.\u003c/li\u003e\n\u003cli\u003eLi, L., Lu, Y., \u0026amp; Peng, M. (2022). Deterioration Model for Reinforced Concrete Bridge Girders Based on Survival Analysis. \u003cem\u003eMathematics\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(23), 4436.\u003c/li\u003e\n\u003cli\u003eLi, Y., Li, L., \u0026amp; Ni, D. (2022). Comparative univariate and regression survival analysis of lane-changing duration characteristic for heavy vehicles and passenger cars. \u003cem\u003eJournal of transportation engineering, Part A: Systems\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e(12), 04022109.\u003c/li\u003e\n\u003cli\u003eLiu, H., \u0026amp; Zhang, Y. (2020). Bridge condition rating data modeling using deep learning algorithm. \u003cem\u003eStructure and Infrastructure Engineering\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(10), 1447-1460.\u003c/li\u003e\n\u003cli\u003eLTBP program - https://infobridge.fhwa.dot.gov/\u003c/li\u003e\n\u003cli\u003eMadanat, S. M., Karlaftis, M. G., \u0026amp; McCarthy, P. S. (1997). Probabilistic infrastructure deterioration models with panel data. \u003cem\u003eJournal of infrastructure systems\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 4-9.\u003c/li\u003e\n\u003cli\u003eMadanat, S., Mishalani, R., \u0026amp; Ibrahim, W. H. W. (1995). Estimation of infrastructure transition probabilities from condition rating data. \u003cem\u003eJournal of infrastructure systems\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 120-125.\u003c/li\u003e\n\u003cli\u003eMauch, M., \u0026amp; Madanat, S. (2001). Semiparametric hazard rate models of reinforced concrete bridge deck deterioration. \u003cem\u003eJournal of Infrastructure Systems\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 49-57.\u003c/li\u003e\n\u003cli\u003eMedina, P. A., \u0026amp; Gonz\u0026aacute;lez, J. L. (2022). Reinforced concrete long-term deterioration prediction for the implementation of a Bridge Management System. \u003cem\u003eMaterials Today: Proceedings\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e, 1265-1271.\u003c/li\u003e\n\u003cli\u003eMirzaei, Z., \u0026amp; Adey, B. T. (2015). Investigation of the use of three existing methodologies to determine optimal life-cycle activity profiles for bridges. \u003cem\u003eStructure and Infrastructure Engineering\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(11), 1484-1509.\u003c/li\u003e\n\u003cli\u003eMorcous, G., Lounis, Z., \u0026amp; Mirza, M. S. (2003). Identification of environmental categories for Markovian deterioration models of bridge decks. \u003cem\u003eJournal of Bridge Engineering\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(6), 353-361.\u003c/li\u003e\n\u003cli\u003eNashville, TE, USA, 22\u0026ndash;26 May 2004; Blandford, G.E., Ed.; ASCE: Reston, VA, USA, 2004; pp. 19\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eNasrollahi, M., \u0026amp; Washer, G. (2015). Estimating inspection intervals for bridges based on statistical analysis of national bridge inventory data. \u003cem\u003eJournal of Bridge Engineering\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(9), 04014104.\u003c/li\u003e\n\u003cli\u003ePipinato, A., \u0026amp; Modena, C. (2010). Structural analysis and fatigue reliability assessment of the Paderno bridge. \u003cem\u003ePractice Periodical on Structural Design and Construction\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 109-124.\u003c/li\u003e\n\u003cli\u003eSobanjo, J., Mtenga, P., \u0026amp; Rambo-Roddenberry, M. (2010). Reliability-based modeling of bridge deterioration hazards. \u003cem\u003eJournal of Bridge Engineering\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6), 671-683.\u003c/li\u003e\n\u003cli\u003eStevens, N. A., Lydon, M., Marshall, A. H., \u0026amp; Taylor, S. (2020). Identification of bridge key performance indicators using survival analysis for future network-wide structural health monitoring. \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(23), 6894.\u003c/li\u003e\n\u003cli\u003eStewart, M. G. (2001). Reliability-based assessment of ageing bridges using risk ranking and life cycle cost decision analyses. \u003cem\u003eReliability Engineering \u0026amp; System Safety\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(3), 263-273.\u003c/li\u003e\n\u003cli\u003eSurvival analysis of concrete highway bridge decks in Oregon utilizing lasso and stepwise-variable selection. \u003cem\u003eJournal of Bridge Engineering\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(10), 04020077.\u003c/li\u003e\n\u003cli\u003eTabatabai, H., Lee, C. W., \u0026amp; Tabatabai, M. A. (2015). Reliability of bridge decks in the United States. \u003cem\u003eBridge Structures\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 75-85.\u003c/li\u003e\n\u003cli\u003eTabatabai, H., Lee, C. W., \u0026amp; Tabatabai, M. A. (2016). Survival analyses for bridge decks in Northern United States.\u003c/li\u003e\n\u003cli\u003eTabatabai, H., Tabatabai, M., \u0026amp; Lee, C. W. (2011). Reliability of bridge decks in Wisconsin. \u003cem\u003eJournal of Bridge Engineering\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 53-62.\u003c/li\u003e\n\u003cli\u003eWettach-Glosser, J., Unnikrishnan, A., \u0026amp; Schumacher, T. (2020). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bridge Resilience, Wet-Freeze Zones, Material Durability, Bridge Materials, Survival Analysis, Long-Term Bridge Performance (LTBP), Structural Deterioration, Predictive Modeling, Condition Ratings","lastPublishedDoi":"10.21203/rs.3.rs-7661874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7661874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study analyzes the historical and future deterioration of concrete, steel, and prestressed concrete bridges in wet-freeze zones using National Bridge Inventory (NBI) data from 1993\u0026ndash;2024 and predictive survival models through 2070. Results show that prestressed concrete bridges are the most durable. Survival modeling under limited (PHDM-L) and maximum (MPM) preservation strategies highlights the impact of maintenance. Without adequate preservation, all bridge types decline significantly, while maximum preservation extends lifespan and maintains structural integrity. The findings emphasize the need for proactive maintenance and material selection. Prioritizing prestressed concrete for new construction can enhance sustainability and reduce long-term costs.\u003c/p\u003e","manuscriptTitle":"Enhancing Bridge Resilience and Sustainability by Assessing Material Durability and Preservation in Wet-Freeze Zones","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 13:10:43","doi":"10.21203/rs.3.rs-7661874/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad437092-83a0-4c0b-86ce-e72db2cd076d","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T12:25:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 13:10:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7661874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7661874","identity":"rs-7661874","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}