Statistical Analysis and Predictive Modeling for Bridge Deterioration in Pennsylvania

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Vidalis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8491604/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract With over 30,000 bridges, Pennsylvania ranks third in the number of bridges and sixth in the nation for bridges in poor condition, according to a 2023 analysis by the Transportation Advisory Committee (TRIP) 1 . The Pennsylvania Department of Transportation (PennDOT) handles supporting these structures. Given the essential role bridges play in supporting commercial, personal, and public transportation, the Pennsylvania Department of Transportation (PennDOT) handles supporting these structures. This involves activities such as painting, superstructure replacement, substructure repair, rehabilitation, bridge replacement, and scour protection. PennDOT faces several factors, including the age of bridges, span length, material type, average daily traffic (ADT), and environmental factors. These factors contribute to the deterioration of bridge conditions and potential failure if no action is taken in the long term. This paper will provide a detailed statistical analysis of bridge deterioration across Pennsylvania, examining the factors and also exploring statistical methods, including multiple regression and survival analysis, to assess bridge conditions and predict hazard and time of bridge failure. The paper aims to offer solutions for rehabilitating and improving the condition of Pennsylvania's bridges. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Bridges are essential civil engineering structures designed to span obstacles without obstructing the features beneath them. To qualify as a bridge, the span must measure at least eight feet. Each bridge is evaluated by its key components (e.g., deck, superstructure, and substructure) and assigned a condition code. Table 1 outlines the Federal Highway Administration (FHWA) codes (zero to nine and N) adopted by PennDOT to classify structural health. For this study, ratings of seven to nine indicate Good condition, five to six represent Fair , and less than or equal to four denote Poor , consistent with PennDOT’s Good/Fair/Poor system used to guide load rating, posting, maintenance, rehabilitation, or closure decisions. These standardized codes provide a common language for inspectors and engineers, forming the basis for all subsequent analysis. Inspectors collect condition data statewide, which structural engineers then use to perform load rating analyses, to compare findings with historical performance, and determine necessary actions to maintain safety and serviceability. Table 1 Bridge Condition Coding 2 Codes Condition Description 0 Failed Bridge collapsed 1 Imminent Failure The bridge is closed due to severe deterioration of critical structural components, including the deck, superstructure, and substructure. 2 Critical The bridge must be closed due to significant structural concerns, advanced section loss in steel members, cracking in concrete elements, or scour holes affecting the substructure. 3 Serious Failure of bridge elements due to advanced cracking and section loss. The bridge will be posted with weight restrictions. 4 Poor Significant deterioration in multiple components, concrete cracking, wearing surface damage, section loss in beams, rust, exposed reinforcement, and scour near substructure elements. 5 Fair Major components exhibit moderate deterioration, noticeable section loss and cracking, but remain functional. 6 Satisfactory Minor deterioration is observed in some elements, with no immediate impact on structural performance. 7 Good Only slight changes are present, with negligible effect on the bridge’s serviceability. 8 Very Good Inspection reveals no deficiencies; all components are in sound condition. 9 Excellent Bridge is newly constructed or recently rehabilitated and meets current design standards. N NA Not Applicable Studies are done on bridge condition ratings and deterioration to investigate the performance of structural behavior during the service life of bridges. Environmental factors and challenges play a key role in substructure rating as debris jammed in the waterway and bedrock material. “The study of scour formed by the influence of Debris Jam Formed by Trees on Bridge Pier Scour has focused on how large trees (debris) were changing the stream shape and scour depth. A large tree and a camera are placed in the stream to record the changes in streambed material over time” 3 . Thus, the large tree blocked part of the stream, caused a change in the shape of the cross-sectional area, and enhanced the water velocity, leading to a deep scour hole. Many researchers found that ADT, materials, and weather influence the performance of bridge condition ratings. Understanding the impact of ADT on bridges allows stakeholders to oversee traffic closely and assign money for maintenance activities. The study, which was conducted on daily traffic on multiple bridges, focused on how ADT in different states influences the bridge condition ratings. Collected ADT data on Bridge deck materials was taken from the NBI database to see the changes in bridge rating in the long run. Applying a non-linear regression model for ADT volumes on bridge deck ratings proved that large ADT volumes crossing concrete decks improve the superstructure condition in cold states” 4 . However, warmer states with large ADT volumes deteriorate faster from good to poor conditions. PennDOT has faced a significant funding challenge in recent years. Vehicle-related revenue is declining as cars become increasingly fuel-efficient and the adoption of electric vehicles accelerates, including within public transportation fleets. At the same time, travel patterns have shifted due to online shopping, remote work, and broader technological changes, reducing fuel consumption and, consequently, fuel tax revenue, the primary source for infrastructure funding. PennDOT anticipates that this trend will continue, making it difficult to sustain bridge maintenance and rehabilitation across the state. A 2019 Transportation Advisory Committee (TAC) study identified six major risks to the transportation system, with fuel tax dependency and federal grant uncertainty ranking highest. TAC further noted that, following the COVID-19 pandemic, adapting to evolving travel behaviors and demands on an aging infrastructure network has become even more challenging 5 . The material used for a bridge’s main span is a critical factor in assessing long-term durability and overall condition. The National Concrete Bridge Council (NCBC), in its Market Use and Condition Report for U.S. Concrete Bridges 6 , analyzed 2023 NBI data to identify which materials correspond to the lowest incidence of poor-condition ratings. Their findings show that concrete bridges consistently outperform steel and timber in terms of condition, indicating superior durability and a longer service life. This reinforces the role of material selection as a key determinant of structural longevity and maintenance needs. The NCBC Report supports the findings of this paper regarding main span material and bridge condition. NCBC report stated. PS concrete bridges exhibit the lowest cases of poor condition among all concrete bridges, which is consistent with the results shown in this study. 2. Results All methods were generated using Statistical Package for the Social Sciences (SPSS) based on the data collected from the PennDOT website for bridge condition ratings for both local and state bridges. SPSS is one of the most used programs for directing and assessing research data. SPSS provides tools for managing, analyzing, and visualizing data, which helps the reader to understand statistical studies. 2.1 Survival Analysis Tables Bridge condition ratings were recorded in SPSS to run the survival analysis method. The values for bridge condition ratings: Fair, Good, and Poor were assigned numerical values one, two, and three, respectively for this study, to answer which bridges reach poor condition first based on the material which had been used in the construction of the bridge. The Kaplan-Meier test was selected for the survival analysis run, and the Breslow test was selected as the default for testing the run. For a successful run, the defined value for this method is chosen to be three, which means the study will compare the bridge material at the moment of transfer of rating to poor condition. Also, a survival plot is checked to make the result easier for the reader to understand statistical analysis visually, and it has a significant conclusion. 2.1.2 Multiple Linear Regression The multiple linear regression analysis in SPSS was conducted through a systematic process to ensure accurate modeling and interpretation. The first step was to determine the dependent and independent variables. The dependent variable in this study is the bridge condition rating, which reflects structural health. The independent variables are ADT, Year Built, Main Span, and Bridge Length. The second step is selecting from the linear regression statistic window check the boxes for estimate and model fit. As a result, Linear Regression Chart, ANOVA, Coefficient, and Model Summary Tables were generated inside SPSS. Further information regarding Multiple linear regression will be discussed in this paper to explain the information generated in SPSS to the reader. 2.1.3 Method In Pennsylvania, bridge conditions are classified into three categories: Good, Fair, and Poor. This paper employs various statistical techniques to analyze these conditions and highlight the challenges faced by engineers in maintaining and improving bridge infrastructure. As illustrated in Fig. 2, approximately 32.38% of bridges are in good condition, 55.73% are in fair condition, and 11.75% are in poor condition. These proportions are influenced by several key factors, including the average age of the bridges, construction material, ADT, span length, and environmental conditions such as weather and exposure to corrosive elements. To better understand the relationship between these factors and bridge condition, this study applies regression analysis to identify significant predictors and survival analysis to estimate the expected lifespan of bridges under varying conditions. These methods provide insights into how structural characteristics and external influences contribute to deterioration trends, enabling engineers and policymakers to prioritize maintenance and allocate resources effectively. Survival analysis is a statistical method used to estimate the probability of an event occurring over time, compare survival distributions across different groups, and evaluate how long items or structures remain in a certain state. In this study, the “event” refers to a significant decline in bridge condition ratings during their service life. The analysis focuses on how the type of material used for the main span influences the rate of deterioration. The summary table for survival analysis includes three key columns: Material Main Span: This column lists the material categories analyzed, including Aluminum, Concrete, Masonry, Steel, Timber, and other materials. Bridges with unspecified or uncommon materials were excluded due to insufficient data. Total N: This column represents the total number of bridges in each material group. For instance, there are 23,343 concrete bridges in the dataset. N of Events: This column indicates the number of bridges within each group that have reached a poor condition rating, serving as a measure of deterioration events. X-axis: bridge condition categories (Good, Fair, Poor, and Not applicable) Y-axis : percentage of bridges in each category Figure 2: Bridge Conditions Across Pennsylvania By comparing survival curves for these material groups, the analysis reveals which materials are associated with faster declines in condition ratings, providing valuable insights for maintenance planning and material selection in future bridge projects. Table 2 summarizes these by using the Kaplan–Meier survival setup. Bridges were grouped according to their main span material—Aluminum, Concrete, Masonry, Steel, and Timber (with “Other” displayed but excluded from analysis due to insufficient data). For each group, the table reports Total N, representing the number of bridges in that category, and N of Events, indicating the count of bridges that transitioned to a Poor condition rating. Table 2 serves two purposes: (1) it shows the sample size balance across materials (e.g., Concrete and Steel dominate the inventory), and (2) it indicates the relative burden of deterioration events within each material group before any covariate adjustment. In conjunction with the survival curves, these counts support the finding that concrete bridges tend to remain in serviceable condition longer than timber and masonry, which exhibit earlier drops in survival probability. Table 2 Survival Analysis Summary Material Main Span Total N N of Events Aluminum 151 26 Concrete 23,343 1,781 Masonry 608 181 Other 63 5 Steel 7,808 1,680 Timber 159 38 Overall 32,132 3,711 As seen in Fig. 3 , the estimated survival of bridges remaining in good condition before requiring maintenance is categorized by the main span material. The survival chart begins with all bridges in good condition and tracks their deterioration over time. The curves reveal distinct patterns for different materials: Masonry and Timber exhibit the steepest decline early in the service life, indicating they deteriorate fastest. Steel and aluminum show a moderate drop during the second stage, while concrete demonstrates the slowest rate of deterioration, maintaining good condition for the longest period. This suggests that concrete bridges generally have the greatest durability compared to other materials analyzed. Concrete bridges in the dataset include several construction types: Cast-in-Place Concrete: Concrete poured and cured directly on-site during construction. Prestressed Precast Concrete: Concrete elements manufactured off-site with steel reinforcement stressed before placement, enhancing strength and durability. Precast Concrete: Concrete components formed in a factory and transported to the site for assembly. Concrete-Encased Steel: Steel elements encased in concrete to improve structural integrity and corrosion resistance. These variations in concrete construction methods contribute to its superior performance in survival analysis, making it a preferred choice for long-lasting bridge structures. Among the concrete bridge types analyzed, 9,810 bridges are Cast-in-Place Concrete, 9,314 are Prestressed Precast Concrete, 4,219 are Precast Concrete, and 764 are Concrete-Encased Steel. As shown in Fig. 4, the majority of concrete bridges fall into the first two categories: Cast-in-Place and Prestressed Precast Concrete. These methods dominate bridge construction primarily because they offer a balance of durability, structural performance, and cost efficiency. Cast-in-Place Concrete is widely used for its adaptability to complex site conditions and strong monolithic structure, which enhances long-term durability. Prestressed Precast Concrete is favored for its ability to resist tensile stresses and minimize cracking, resulting in longer service life and reduced maintenance needs. Additionally, precasting components off-site accelerates construction schedules and lowers labor costs. The prevalence of these two types reflects their proven reliability and economic advantages, making them the preferred choice for modern bridge projects. X-axis: year built for bridges within Pennsylvania Y-axis: survival range (A higher survival value reflects better bridge condition). Note This study tests bridge performance from 1800s to 2000s X-axis: Total number of bridges within Pennsylvania Y-axis: Methods used during their construction. Figure 4: Concrete Bridges Material The Concrete Bridges Survival Analysis Chart provides an estimate of how long bridges remain in good condition before requiring maintenance, based on the type of concrete material used for the main span. As shown in Fig. 5, all bridges begin in good condition, but their performance diverges over time. The survival curves indicate that Precast Concrete bridges experience the earliest decline, suggesting they deteriorate faster compared to other concrete types. In contrast, Prestressed Precast Concrete bridges maintain good condition for a significantly longer period, reflecting the benefits of prestressing, which enhances structural strength and reduces cracking underload. This extended lifespan makes prestressed concrete a preferred choice for high-traffic or long-span bridges, where durability and reduced maintenance costs are critical. X-axis: year built for bridges within Pennsylvania Y-axis: survival range (A higher survival value reflects better bridge condition). Note This study tested concrete bridge performance from 1800s to 2000s Figure 5: Survival Analysis of Concrete Bridges 2.2.4 Multiple Linear Regression Analysis Multiple linear regression was used to examine how several independent variables collectively influence a dependent variable. In this study, the goal was to determine whether factors such as ADT, Year Built, Main Span Length, and Overall Bridge Length significantly impact the bridge condition rating. The hypotheses are defined as follows: Null Hypothesis (H₀): There is no relationship between bridge condition and the selected predictors. Alternative Hypothesis (H₁): There is a significant relationship between bridge condition and the predictors. The decision rule is based on the p-value: If p < 0.05, we reject the null hypothesis and accept the alternative, indicating a statistically significant relationship between bridge condition and the predictors. If p ≥ 0.05, we fail to reject the null hypothesis, meaning no significant relationship exists. The Model Summary Table provides key metrics for evaluating the regression model. The most critical measure is R-Square (Coefficient of Determination), which indicates the proportion of variance in the dependent variable explained by the independent variables. A higher R-Square value suggests a stronger model fit. Other important metrics include: R (Correlation Coefficient): Ranges from − 1 to 1, showing the strength and direction of the linear relationship. Adjusted R-Square: Adjusts the R-Square value to account for the number of predictors, providing a more accurate measure of model fit when multiple variables are included. Table 3 reports the overall fit statistics for the multiple linear regression model with Condition as the dependent variable and ADT Total, Year Built, Main Spans, and Bridge Length as predictors. The R and R² values indicate the strength of the linear association; here, R² ≈ 0.364 shows that roughly 36% of the variation in bridge condition is explained by the chosen predictors. The Adjusted R² matches R² (reflecting the large sample and four predictors), and the very large F Change with p < 0.001 confirms that the model improves significantly over a constant-only baseline. The Std. Error of the Estimate quantifies typical residual variation in the coded condition measure. Together, these statistics establish that the model is both meaningful and statistically robust for inferential purposes. By analyzing these coefficients, one can assess how well the predictors explain variations in bridge condition and identify which factors have the greatest influence. The ANOVA Summary Results (Analysis of Variance) table is a critical component of regression analysis because it evaluates whether the overall model is statistically significant. That is, whether the predictors collectively explain variation in the dependent variable beyond random chance. Key elements include: F (ANOVA Statistic): Indicates whether the regression model provides a better fit than a model with no predictors. A higher F-value generally suggests the model is statistically significant. df (Degrees of Freedom): Represents the number of values that can vary independently in the analysis. In regression, this relates to the number of predictors and observations used in the model. Sig (Significance Level): Corresponds to the p-value. A low significance value (typically less than 0.05) indicates that the model is statistically significant, meaning there is a strong relationship between the predictors and the dependent variable. In short, the ANOVA table helps confirm whether the regression model is meaningful and worth interpreting further. Table 4 breaks down total variability in the dependent variable into Regression and Residual components. The Sum of Squares, df, and Mean Square entries culminate in an F statistic (≈ 4593) with Sig < 0.001, demonstrating that the predictor set (ADT Total, Year Built, Main Spans, Bridge Length) collectively explains a statistically significant share of the variance in bridge condition. This table is the formal omnibus test that justifies interpreting individual coefficients and effect directions in the next table. Table 3 Model Summary Results Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change 1 .603 .364 .364 .505 .364 4593.136 4 32127 < .001 a. Predictors: (constant), ADT Total, Year Built, Main Spans, Bridge length b. Dependent Variable: Condition Table 4 ANOVA Summary Results ANOVA Model Sum of Squares df Mean Square F Sig Regression 4689.685 4 1172.421 4593.136 < 0.001b Residual 8200.580 32127 .225 Total 12890.264 32131 a. Dependent Variable: Condition b. Predictors: ( Constant), ADT Total, Year Built, Main Spans, Bridge Length 2.2.5 Significance Value and Coefficients The significance value (Sig.) is the most critical metric for interpreting the ANOVA results. In Table 5 , the Sig. value is less than 0.05, which means the null hypothesis is rejected. This indicates that the regression model is statistically significant and that there is a strong relationship between the dependent variable (bridge condition) and the predictors. Table 5 provides detailed information about the contribution of each predictor in the multiple regression model. Key components include: Unstandardized Coefficients (B): These values represent the actual regression coefficients for each predictor, showing how much the dependent variable changes with a one-unit change in the predictor. Standardized Coefficients (Beta): These coefficients are standardized to allow comparison of the relative importance of each predictor, regardless of their original scale. t-values: These values test whether each coefficient is significantly different from zero. A higher t-value indicates a stronger impact of the predictor on the dependent variable. p-values: These determine whether each predictor is statistically significant. A low p-value (typically < 0.05) suggests that the predictor has a meaningful relationship with the bridge condition rating. Together, these metrics help identify which factors most strongly influence bridge condition and guide decisions for maintenance and design priorities. Table 5 presents the unstandardized coefficients (B), their standard errors, standardized betas, t-statistics, p-values, and 95% confidence intervals for each predictor. All four predictors are statistically significant (p < 0.05). The negative coefficient for Year Built implies that, holding other factors constant, newer construction years are associated with lower coded condition values (i.e., better condition), consistent with the expectation that older bridges are more likely to be in poorer condition. Positive coefficients for Main Spans, Bridge Length, and ADT Total indicate that increases in span count/length and traffic are associated with higher coded condition values (i.e., movement toward poorer condition), which is sensible given greater demand and structural exposure. The standardized betas allow comparing relative influence after unit scaling, and the confidence intervals corroborate the precision of these estimates. Table 5 Coefficients Summary Analysis Coefficients Model Unstandardized Coefficient (B) Unstandardized Coefficient (Std. Error) Standardized Coefficients (Beta) t Sig. 95.0% Confidence Interval for B Lower Bond 95.0% Confidence Interval for B Higher Bond Constant 23.318 .167 139.705 < .001 22.991 23.645 Year Built -0.11 .000 − .603 -135.110 < .001 − .012 − .011 Main Spans .006 .001 .026 4.216 < .001 .003 .008 Bridge Length 5.022E-5 .000 .017 2..732 .006 .000 .000 ADT Total 4.947E-7 .000 .011 2.403 .016 .000 .000 a. Dependent Variable: Condition 3. Discussion and Results Survival analysis was applied in this research to test the hypothesis that bridge condition ratings decline at different rates over time depending on the material used for the main structure. Figure 3 illustrates these results: Masonry and Timber bridges exhibit the fastest deterioration in the early stages, while Concrete and Aluminum bridges maintain good condition for a longer period. PennDOT primarily constructs reinforced concrete bridges, which combine concrete (a mixture of cement, aggregate, and water) with steel reinforcement. This combination significantly enhances durability for several reasons: Strength in Compression and Tension: Reinforced concrete benefits from concrete’s high compressive strength and steel’s tensile strength, allowing it to carry heavy loads without cracking or losing structural integrity. This makes it more durable than Masonry, Aluminum, Timber, or plain Steel bridges, as confirmed by the survival analysis. High Compressive Strength: Reinforced concrete can achieve compressive strengths up to 10,000 psi, according to industry standards, enabling it to withstand substantial live loads before failure. The survival results also show that Timber bridges last longer than Masonry bridges, primarily due to load design. Timber bridges in Pennsylvania are typically designed for light traffic only, prohibiting heavy vehicles, whereas Masonry bridges often carry both light and heavy loads. This difference in load capacity explains why Masonry bridges deteriorate faster under stress 7 . To further understand factors influencing bridge condition, a Multiple Regression Analysis was conducted using SPSS. The research question was: “Do ADT, Year Built, Main Span, and Bridge Length impact the bridge condition rating?” The hypotheses were: Null Hypothesis (H₀): No relationship exists between bridge condition and the predictors. Alternative Hypothesis (H₁): A significant relationship exists between bridge condition and the predictors. The regression results rejected the null hypothesis because the significance value (p < 0.005) indicated a strong relationship between bridge condition and the predictors. Specifically: Higher ADT, longer bridge length, and greater main span are associated with lower condition ratings, reflecting increased stress and wear. Older bridges tend to be in poorer condition, as shown by the negative correlation between Year Built and condition rating. Figure 5 visualizes these relationships, confirming that traffic volume, structural dimensions, and age are critical factors in predicting deterioration. 4. Conclusions, Implications, and Future Research This paper highlights the significant challenges in maintaining Pennsylvania’s bridge infrastructure, where a large proportion of bridges are currently rated as fair or poor. PennDOT faces multiple obstacles, including funding constraints, aging structures, span length, material type, ADT, and environmental factors, all of which accelerate deterioration and increase the risk of structural failure if not addressed proactively. One of the most pressing issues is declining transportation revenue. PennDOT reports that fuel tax revenue, a primary source of funding for infrastructure, has been steadily decreasing due to several trends: the rise of fuel-efficient and electric vehicles, shifts in travel behavior driven by teleworking and e-commerce, and broader technological changes. These factors reduce fuel consumption, making it increasingly difficult to sustain traditional funding models for bridge maintenance and rehabilitation. To address these challenges, this study employed statistical methods including linear regression, multiple regression, and survival analysis to evaluate bridge conditions and predict deterioration patterns over time. These models identified key predictors such as traffic volume, bridge age, span length, and material type, providing actionable insights for prioritizing maintenance and allocating resources effectively. While these methods offer robust tools for planning, natural hazards such as sinkholes and scour holes remain difficult to predict and mitigate due to their complexity and the specialized engineering techniques required. The findings of this research are critical for engineering decision-making and policy development. They support more informed scheduling and planning of maintenance activities, enabling PennDOT to optimize limited resources. Furthermore, the results underscore the need for innovative funding strategies that account for the growing prevalence of fuel-efficient, hybrid, and electric vehicles. Engaging stakeholders and educating policymakers about these challenges will be essential to secure sustainable funding and foster collaboration for long-term infrastructure resilience. Sinkholes and scour holes represent environmental challenges that are difficult to predict using traditional statistical methods. They occur when subsurface layers collapse over time, often due to chemical reactions between rainwater and soluble rock formations such as limestone. Pennsylvania is particularly susceptible to sinkholes because of its underlying geology. These formations often begin as small depressions with little immediate impact on bridge components but can expand significantly, ultimately compromising structural integrity and lowering bridge condition ratings 8 . Similarly, scour holes develop when fast-moving water erodes streambed material around bridge foundations, exposing and undermining structural supports. Contributing factors include fallen trees, debris accumulation, and bridge alignment, which can accelerate erosion. Scour holes pose a severe risk because they can form beneath foundations, creating hidden vulnerabilities that may lead to catastrophic failure if undetected 9 . To address these risks, specialized tools are essential. Ground Penetrating Radar (GPR) is effective for detecting subsurface anomalies by measuring variations in material depth beneath bridge structures, helping identify early signs of sinkhole formation. Fathometers, on the other hand, measure streambed elevation and depth changes 9 , 10 , enabling engineers to monitor scour development and take preventive action. These technologies provide critical data for proactive maintenance strategies, complementing statistical models that cannot fully capture these unpredictable environmental hazards. Abbreviations ADT Average Daily Traffic FHWA Federal Highway Administration NCBC National Concrete Bridge Council NBI National Bridge Inventory PennDOT Pennsylvania Department of Transportation SPSS Statistical Package for the Social Sciences TAC Transportation Advisory Committee Declarations Author Contribution M.S. = data, statistical analysis, wrote manuscriptS.M.V. = advisor, guided research, checked data and analysis, wrote manuscript Data Availability The dataset is publicly available through PennDOT and is regularly updated to reflect any maintenance activities, inspections, or structural changes: https://data-pennshare.opendata.arcgis.com/datasets/PennShare::pennsylvania-bridges/about References TRIP (2024) (June Preserving Pennsylvania’s Bridges: The Condition and Funding Needs of Pennsylvania’s Aging Bridge System, National Transportation Research Nonprofit , https://tripnet.org/reports/preserving-pennsylvanias-bridges-june-2024/ , Accessed December 2025. Commonwealth of Pennsylvania (2025) Bridge Inspection Terminology. PennDOT. https://www.pa.gov/agencies/penndot/programs-and-doing-business/bridges/bridge-inspection-terminology , Accessed December 2025 Zhang W, Nistor I, Colin D, Rennei (2024) 10, (July Influence of Debris Jam Formed by Trees on Bridge Pier Scour, Journal of Hydraulic Engineering , Vol. 150, No. 5. https://doi.org/10.1061/jhend8.hyeng-13688 , Accessed December 2025 Chetti P, Hesham H, Ali (2022) (June Impact of Daily Traffic on Various Bridge Decks in Different Climatic Regions, European Workshop on Structural Health Monitoring, EWSHM 2022 - Volume 2, pp.1116–1121. https://www.researchgate.net/publication/361337160_Impact_of_Daily_Traffic_on_Various_Bridge_Decks_in_Different_Climatic_Regions , Accessed December 2025 Commonwealth of Pennsylvania (2025) Transportation Funding PennDOT. . Accessed December 25 Tremblay A, Keith Ramsey (2023) (June Market Use and Condition Report for US Concrete Bridges: Based on NBI Data as of Fall 2024, National Concrete Bridge Council. https://nationalconcretebridge.org/resources/ConcreteBridgesMarketShareAndPerformance-ePub.pdf , Accessed December 2025 Ronald J, Ladyka PE, Wigton BT EIT. CONDITION OF LOCALLY OWNED BRIDGES, PennDOT LTAP technical INFORMATION SHEET #171 SUMMER/ (2016), TS_171.pdf, Accessed December 2025, TS_171.pdf, Accessed December 2025 Commonwealth of Pennsylvania (2025) Sinkholes, Department of Conservation and Natural Resources , . Accessed December 2025 Hayes DC, Drummond FE (1995) Use of Fathometers and Electrical-Conductivity Probes to Monitor Riverbed Scour at Bridge Piers, U.S. Geological Survey, Water-Resources Investigations Report 94-4164, Delaware, Maryland, and Virginia DOTs. https://pubs.usgs.gov/wri/1994/4164/report.pdf , Accessed December 202 Placzek G, Haeni FP (1995) Surface-Geophysical Techniques Used to Detect Existing and Infilled Scour Holes Near Bridge Piers, U.S. Geological Survey, Water-Resources Investigations Report 95-4009, Federal Highway Administration. https://pubs.usgs.gov/publication/wri954009 , Accessed December 2025 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8491604","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603247765,"identity":"2dc4d912-44df-4325-bdde-4dcd8ff82c05","order_by":0,"name":"Marina Sara","email":"","orcid":"","institution":"Pennsylvania State University - Harrisburg","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Sara","suffix":""},{"id":603247766,"identity":"3db9a8cf-02f1-43dc-834b-b0259b04702a","order_by":1,"name":"Sofia M. Vidalis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYJCCAwkVEIYEsToYDzw4Q6IW5oMP20jRIj/tjMGBxHl37A0OMB+8zUOMFoPbOUAt254lbjjAlmxNnBbptASglsMJBgd4zKSJ0iI/G6RlzmGgw/i/EaeF4XbygQOJDYcZNxzgYSNOiwFIS8KxZ4kzD7MZW84hzmGJzR9/1Nyx5zve/PDGG6IcBgEHgNFDgnKollEwCkbBKBgFuAAArtw4svMb3egAAAAASUVORK5CYII=","orcid":"","institution":"Pennsylvania State University - Harrisburg","correspondingAuthor":true,"prefix":"","firstName":"Sofia","middleName":"M.","lastName":"Vidalis","suffix":""}],"badges":[],"createdAt":"2025-12-31 18:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8491604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8491604/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104306712,"identity":"1b7f7cfe-b868-4f1b-9028-a75513b8953a","added_by":"auto","created_at":"2026-03-10 10:06:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134535,"visible":true,"origin":"","legend":"\u003cp\u003eLowest Rates of Poor Condition Bridges\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8491604/v1/80a906248d28dad50598700c.jpg"},{"id":104306711,"identity":"32427a1f-69cc-4e89-94ff-3d5840a16c0a","added_by":"auto","created_at":"2026-03-10 10:06:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82626,"visible":true,"origin":"","legend":"\u003cp\u003eBridge Conditions Across Pennsylvania\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8491604/v1/b3f4305edcd026b806101bf8.jpg"},{"id":104405083,"identity":"6f93ff63-e63e-4b67-91de-79c457f8f93f","added_by":"auto","created_at":"2026-03-11 12:21:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110677,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Analysis of Main Bridge Materials\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8491604/v1/5b0c0aee50118493b8242ae1.jpg"},{"id":104306714,"identity":"ccf28aac-9861-4a7e-ba28-d64eb0c520fd","added_by":"auto","created_at":"2026-03-10 10:06:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74744,"visible":true,"origin":"","legend":"\u003cp\u003eConcrete Bridges Material\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8491604/v1/e3b28943f749723ede860355.jpg"},{"id":104306713,"identity":"24c868bc-19cd-47bb-b63f-b9dc32a3b35b","added_by":"auto","created_at":"2026-03-10 10:06:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105837,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Analysis of Concrete Bridges\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8491604/v1/9c86e11d86174748d07e1249.jpg"},{"id":104408807,"identity":"f860f541-9ba8-405f-ad72-1e70b67216d8","added_by":"auto","created_at":"2026-03-11 12:43:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1088629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8491604/v1/a2219f49-7b25-4df7-8524-515994cd3410.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Statistical Analysis and Predictive Modeling for Bridge Deterioration in Pennsylvania","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBridges are essential civil engineering structures designed to span obstacles without obstructing the features beneath them. To qualify as a bridge, the span must measure at least eight feet. Each bridge is evaluated by its key components (e.g., deck, superstructure, and substructure) and assigned a condition code. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the Federal Highway Administration (FHWA) codes (zero to nine and N) adopted by PennDOT to classify structural health. For this study, ratings of seven to nine indicate \u003cem\u003eGood\u003c/em\u003e condition, five to six represent \u003cem\u003eFair\u003c/em\u003e, and less than or equal to four denote \u003cem\u003ePoor\u003c/em\u003e, consistent with PennDOT\u0026rsquo;s Good/Fair/Poor system used to guide load rating, posting, maintenance, rehabilitation, or closure decisions. These standardized codes provide a common language for inspectors and engineers, forming the basis for all subsequent analysis. Inspectors collect condition data statewide, which structural engineers then use to perform load rating analyses, to compare findings with historical performance, and determine necessary actions to maintain safety and serviceability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBridge Condition Coding\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCodes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFailed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBridge collapsed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImminent Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe bridge is closed due to severe deterioration of critical structural components, including the deck, superstructure, and substructure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe bridge must be closed due to significant structural concerns, advanced section loss in steel members, cracking in concrete elements, or scour holes affecting the substructure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFailure of bridge elements due to advanced cracking and section loss. The bridge will be posted with weight restrictions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignificant deterioration in multiple components, concrete cracking, wearing surface damage, section loss in beams, rust, exposed reinforcement, and scour near substructure elements.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMajor components exhibit moderate deterioration, noticeable section loss and cracking, but remain functional.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfactory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinor deterioration is observed in some elements, with no immediate impact on structural performance.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOnly slight changes are present, with negligible effect on the bridge\u0026rsquo;s serviceability.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInspection reveals no deficiencies; all components are in sound condition.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBridge is newly constructed or recently rehabilitated and meets current design standards.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStudies are done on bridge condition ratings and deterioration to investigate the performance of structural behavior during the service life of bridges. Environmental factors and challenges play a key role in substructure rating as debris jammed in the waterway and bedrock material. \u0026ldquo;The study of scour formed by the influence of Debris Jam Formed by Trees on Bridge Pier Scour has focused on how large trees (debris) were changing the stream shape and scour depth. A large tree and a camera are placed in the stream to record the changes in streambed material over time\u0026rdquo; \u003csup\u003e3\u003c/sup\u003e. Thus, the large tree blocked part of the stream, caused a change in the shape of the cross-sectional area, and enhanced the water velocity, leading to a deep scour hole. Many researchers found that ADT, materials, and weather influence the performance of bridge condition ratings. Understanding the impact of ADT on bridges allows stakeholders to oversee traffic closely and assign money for maintenance activities. The study, which was conducted on daily traffic on multiple bridges, focused on how ADT in different states influences the bridge condition ratings. Collected ADT data on Bridge deck materials was taken from the NBI database to see the changes in bridge rating in the long run. Applying a non-linear regression model for ADT volumes on bridge deck ratings proved that large ADT volumes crossing concrete decks improve the superstructure condition in cold states\u0026rdquo; \u003csup\u003e4\u003c/sup\u003e. However, warmer states with large ADT volumes deteriorate faster from good to poor conditions.\u003c/p\u003e \u003cp\u003ePennDOT has faced a significant funding challenge in recent years. Vehicle-related revenue is declining as cars become increasingly fuel-efficient and the adoption of electric vehicles accelerates, including within public transportation fleets. At the same time, travel patterns have shifted due to online shopping, remote work, and broader technological changes, reducing fuel consumption and, consequently, fuel tax revenue, the primary source for infrastructure funding. PennDOT anticipates that this trend will continue, making it difficult to sustain bridge maintenance and rehabilitation across the state. A 2019 Transportation Advisory Committee (TAC) study identified six major risks to the transportation system, with fuel tax dependency and federal grant uncertainty ranking highest. TAC further noted that, following the COVID-19 pandemic, adapting to evolving travel behaviors and demands on an aging infrastructure network has become even more challenging \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe material used for a bridge\u0026rsquo;s main span is a critical factor in assessing long-term durability and overall condition. The National Concrete Bridge Council (NCBC), in its \u003cem\u003eMarket Use and Condition Report for U.S. Concrete Bridges\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, analyzed 2023 NBI data to identify which materials correspond to the lowest incidence of poor-condition ratings. Their findings show that concrete bridges consistently outperform steel and timber in terms of condition, indicating superior durability and a longer service life. This reinforces the role of material selection as a key determinant of structural longevity and maintenance needs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe NCBC Report supports the findings of this paper regarding main span material and bridge condition. NCBC report stated. PS concrete bridges exhibit the lowest cases of poor condition among all concrete bridges, which is consistent with the results shown in this study.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eAll methods were generated using Statistical Package for the Social Sciences (SPSS) based on the data collected from the PennDOT website for bridge condition ratings for both local and state bridges. SPSS is one of the most used programs for directing and assessing research data. SPSS provides tools for managing, analyzing, and visualizing data, which helps the reader to understand statistical studies.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Survival Analysis Tables\u003c/h2\u003e \u003cp\u003eBridge condition ratings were recorded in SPSS to run the survival analysis method. The values for bridge condition ratings: Fair, Good, and Poor were assigned numerical values one, two, and three, respectively for this study, to answer which bridges reach poor condition first based on the material which had been used in the construction of the bridge. The Kaplan-Meier test was selected for the survival analysis run, and the Breslow test was selected as the default for testing the run. For a successful run, the defined value for this method is chosen to be three, which means the study will compare the bridge material at the moment of transfer of rating to poor condition. Also, a survival plot is checked to make the result easier for the reader to understand statistical analysis visually, and it has a significant conclusion.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Multiple Linear Regression\u003c/h2\u003e \u003cp\u003eThe multiple linear regression analysis in SPSS was conducted through a systematic process to ensure accurate modeling and interpretation. The first step was to determine the dependent and independent variables. The dependent variable in this study is the bridge condition rating, which reflects structural health. The independent variables are ADT, Year Built, Main Span, and Bridge Length.\u003c/p\u003e \u003cp\u003eThe second step is selecting from the linear regression statistic window check the boxes for estimate and model fit. As a result, Linear Regression Chart, ANOVA, Coefficient, and Model Summary Tables were generated inside SPSS.\u003c/p\u003e \u003cp\u003eFurther information regarding Multiple linear regression will be discussed in this paper to explain the information generated in SPSS to the reader.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Method\u003c/h2\u003e \u003cp\u003eIn Pennsylvania, bridge conditions are classified into three categories: Good, Fair, and Poor. This paper employs various statistical techniques to analyze these conditions and highlight the challenges faced by engineers in maintaining and improving bridge infrastructure. As illustrated in Fig.\u0026nbsp;2, approximately 32.38% of bridges are in good condition, 55.73% are in fair condition, and 11.75% are in poor condition. These proportions are influenced by several key factors, including the average age of the bridges, construction material, ADT, span length, and environmental conditions such as weather and exposure to corrosive elements. To better understand the relationship between these factors and bridge condition, this study applies regression analysis to identify significant predictors and survival analysis to estimate the expected lifespan of bridges under varying conditions. These methods provide insights into how structural characteristics and external influences contribute to deterioration trends, enabling engineers and policymakers to prioritize maintenance and allocate resources effectively.\u003c/p\u003e \u003cp\u003eSurvival analysis is a statistical method used to estimate the probability of an event occurring over time, compare survival distributions across different groups, and evaluate how long items or structures remain in a certain state. In this study, the \u0026ldquo;event\u0026rdquo; refers to a significant decline in bridge condition ratings during their service life. The analysis focuses on how the type of material used for the main span influences the rate of deterioration.\u003c/p\u003e \u003cp\u003eThe summary table for survival analysis includes three key columns:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMaterial Main Span: This column lists the material categories analyzed, including Aluminum, Concrete, Masonry, Steel, Timber, and other materials. Bridges with unspecified or uncommon materials were excluded due to insufficient data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTotal N: This column represents the total number of bridges in each material group. For instance, there are 23,343 concrete bridges in the dataset.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eN of Events: This column indicates the number of bridges within each group that have reached a poor condition rating, serving as a measure of deterioration events.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eX-axis: bridge condition categories (Good, Fair, Poor, and Not applicable)\u003c/p\u003e \u003cp\u003eY-axis : percentage of bridges in each category\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 2: Bridge Conditions Across Pennsylvania\u003c/p\u003e \u003cp\u003eBy comparing survival curves for these material groups, the analysis reveals which materials are associated with faster declines in condition ratings, providing valuable insights for maintenance planning and material selection in future bridge projects.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes these by using the Kaplan\u0026ndash;Meier survival setup. Bridges were grouped according to their main span material\u0026mdash;Aluminum, Concrete, Masonry, Steel, and Timber (with \u0026ldquo;Other\u0026rdquo; displayed but excluded from analysis due to insufficient data). For each group, the table reports Total N, representing the number of bridges in that category, and N of Events, indicating the count of bridges that transitioned to a Poor condition rating. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e serves two purposes: (1) it shows the sample size balance across materials (e.g., Concrete and Steel dominate the inventory), and (2) it indicates the relative burden of deterioration events within each material group before any covariate adjustment. In conjunction with the survival curves, these counts support the finding that concrete bridges tend to remain in serviceable condition longer than timber and masonry, which exhibit earlier drops in survival probability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvival Analysis Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial Main Span\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN of Events\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAluminum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcrete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23,343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasonry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32,132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the estimated survival of bridges remaining in good condition before requiring maintenance is categorized by the main span material. The survival chart begins with all bridges in good condition and tracks their deterioration over time. The curves reveal distinct patterns for different materials: Masonry and Timber exhibit the steepest decline early in the service life, indicating they deteriorate fastest. Steel and aluminum show a moderate drop during the second stage, while concrete demonstrates the slowest rate of deterioration, maintaining good condition for the longest period. This suggests that concrete bridges generally have the greatest durability compared to other materials analyzed.\u003c/p\u003e \u003cp\u003eConcrete bridges in the dataset include several construction types:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCast-in-Place Concrete: Concrete poured and cured directly on-site during construction.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrestressed Precast Concrete: Concrete elements manufactured off-site with steel reinforcement stressed before placement, enhancing strength and durability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrecast Concrete: Concrete components formed in a factory and transported to the site for assembly.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConcrete-Encased Steel: Steel elements encased in concrete to improve structural integrity and corrosion resistance.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese variations in concrete construction methods contribute to its superior performance in survival analysis, making it a preferred choice for long-lasting bridge structures.\u003c/p\u003e \u003cp\u003eAmong the concrete bridge types analyzed, 9,810 bridges are Cast-in-Place Concrete, 9,314 are Prestressed Precast Concrete, 4,219 are Precast Concrete, and 764 are Concrete-Encased Steel. As shown in Fig.\u0026nbsp;4, the majority of concrete bridges fall into the first two categories: Cast-in-Place and Prestressed Precast Concrete. These methods dominate bridge construction primarily because they offer a balance of durability, structural performance, and cost efficiency. Cast-in-Place Concrete is widely used for its adaptability to complex site conditions and strong monolithic structure, which enhances long-term durability. Prestressed Precast Concrete is favored for its ability to resist tensile stresses and minimize cracking, resulting in longer service life and reduced maintenance needs. Additionally, precasting components off-site accelerates construction schedules and lowers labor costs.\u003c/p\u003e \u003cp\u003eThe prevalence of these two types reflects their proven reliability and economic advantages, making them the preferred choice for modern bridge projects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eX-axis: year built for bridges within Pennsylvania\u003c/p\u003e \u003cp\u003eY-axis: survival range (A higher survival value reflects better bridge condition).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThis study tests bridge performance from 1800s to 2000s\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eX-axis: Total number of bridges within Pennsylvania\u003c/p\u003e \u003cp\u003eY-axis: Methods used during their construction.\u003c/p\u003e \u003cp\u003eFigure 4: Concrete Bridges Material\u003c/p\u003e \u003cp\u003eThe Concrete Bridges Survival Analysis Chart provides an estimate of how long bridges remain in good condition before requiring maintenance, based on the type of concrete material used for the main span. As shown in Fig.\u0026nbsp;5, all bridges begin in good condition, but their performance diverges over time. The survival curves indicate that Precast Concrete bridges experience the earliest decline, suggesting they deteriorate faster compared to other concrete types. In contrast, Prestressed Precast Concrete bridges maintain good condition for a significantly longer period, reflecting the benefits of prestressing, which enhances structural strength and reduces cracking underload. This extended lifespan makes prestressed concrete a preferred choice for high-traffic or long-span bridges, where durability and reduced maintenance costs are critical.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eX-axis: year built for bridges within Pennsylvania\u003c/p\u003e \u003cp\u003eY-axis: survival range (A higher survival value reflects better bridge condition).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThis study tested concrete bridge performance from 1800s to 2000s\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFigure 5: Survival Analysis of Concrete Bridges\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Multiple Linear Regression Analysis\u003c/h2\u003e \u003cp\u003eMultiple linear regression was used to examine how several independent variables collectively influence a dependent variable. In this study, the goal was to determine whether factors such as ADT, Year Built, Main Span Length, and Overall Bridge Length significantly impact the bridge condition rating. The hypotheses are defined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNull Hypothesis (H₀): There is no relationship between bridge condition and the selected predictors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlternative Hypothesis (H₁): There is a significant relationship between bridge condition and the predictors.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe decision rule is based on the p-value:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIf p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we reject the null hypothesis and accept the alternative, indicating a statistically significant relationship between bridge condition and the predictors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIf p\u0026thinsp;\u0026ge;\u0026thinsp;0.05, we fail to reject the null hypothesis, meaning no significant relationship exists.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe Model Summary Table provides key metrics for evaluating the regression model. The most critical measure is R-Square (Coefficient of Determination), which indicates the proportion of variance in the dependent variable explained by the independent variables. A higher R-Square value suggests a stronger model fit. Other important metrics include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eR (Correlation Coefficient): Ranges from \u0026minus;\u0026thinsp;1 to 1, showing the strength and direction of the linear relationship.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdjusted R-Square: Adjusts the R-Square value to account for the number of predictors, providing a more accurate measure of model fit when multiple variables are included.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the overall fit statistics for the multiple linear regression model with Condition as the dependent variable and ADT Total, Year Built, Main Spans, and Bridge Length as predictors. The R and R\u0026sup2; values indicate the strength of the linear association; here, R\u0026sup2; \u0026asymp; 0.364 shows that roughly 36% of the variation in bridge condition is explained by the chosen predictors. The Adjusted R\u0026sup2; matches R\u0026sup2; (reflecting the large sample and four predictors), and the very large F Change with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 confirms that the model improves significantly over a constant-only baseline. The Std. Error of the Estimate quantifies typical residual variation in the coded condition measure. Together, these statistics establish that the model is both meaningful and statistically robust for inferential purposes. By analyzing these coefficients, one can assess how well the predictors explain variations in bridge condition and identify which factors have the greatest influence.\u003c/p\u003e \u003cp\u003eThe ANOVA Summary Results (Analysis of Variance) table is a critical component of regression analysis because it evaluates whether the overall model is statistically significant. That is, whether the predictors collectively explain variation in the dependent variable beyond random chance.\u003c/p\u003e \u003cp\u003eKey elements include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eF (ANOVA Statistic): Indicates whether the regression model provides a better fit than a model with no predictors. A higher F-value generally suggests the model is statistically significant.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003edf (Degrees of Freedom): Represents the number of values that can vary independently in the analysis. In regression, this relates to the number of predictors and observations used in the model.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSig (Significance Level): Corresponds to the p-value. A low significance value (typically less than 0.05) indicates that the model is statistically significant, meaning there is a strong relationship between the predictors and the dependent variable.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn short, the ANOVA table helps confirm whether the regression model is meaningful and worth interpreting further. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e breaks down total variability in the dependent variable into Regression and Residual components. The Sum of Squares, df, and Mean Square entries culminate in an F statistic (\u0026asymp;\u0026thinsp;4593) with Sig\u0026thinsp;\u0026lt;\u0026thinsp;0.001, demonstrating that the predictor set (ADT Total, Year Built, Main Spans, Bridge Length) collectively explains a statistically significant share of the variance in bridge condition. This table is the formal omnibus test that justifies interpreting individual coefficients and effect directions in the next table.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Summary Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eModel Summary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted R Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR Square Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSig. F Change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4593.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003ea. Predictors: (constant), ADT Total, Year Built, Main Spans, Bridge length\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eb. Dependent Variable: Condition\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA Summary Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4689.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1172.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4593.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8200.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12890.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ea. Dependent Variable: Condition\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eb. Predictors: ( Constant), ADT Total, Year Built, Main Spans, Bridge Length\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Significance Value and Coefficients\u003c/h2\u003e \u003cp\u003eThe significance value (Sig.) is the most critical metric for interpreting the ANOVA results. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the Sig. value is less than 0.05, which means the null hypothesis is rejected. This indicates that the regression model is statistically significant and that there is a strong relationship between the dependent variable (bridge condition) and the predictors. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides detailed information about the contribution of each predictor in the multiple regression model. Key components include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUnstandardized Coefficients (B): These values represent the actual regression coefficients for each predictor, showing how much the dependent variable changes with a one-unit change in the predictor.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStandardized Coefficients (Beta): These coefficients are standardized to allow comparison of the relative importance of each predictor, regardless of their original scale.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003et-values: These values test whether each coefficient is significantly different from zero. A higher t-value indicates a stronger impact of the predictor on the dependent variable.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ep-values: These determine whether each predictor is statistically significant. A low p-value (typically\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggests that the predictor has a meaningful relationship with the bridge condition rating.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTogether, these metrics help identify which factors most strongly influence bridge condition and guide decisions for maintenance and design priorities.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the unstandardized coefficients (B), their standard errors, standardized betas, t-statistics, p-values, and 95% confidence intervals for each predictor. All four predictors are statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The negative coefficient for Year Built implies that, holding other factors constant, newer construction years are associated with lower coded condition values (i.e., better condition), consistent with the expectation that older bridges are more likely to be in poorer condition. Positive coefficients for Main Spans, Bridge Length, and ADT Total indicate that increases in span count/length and traffic are associated with higher coded condition values (i.e., movement toward poorer condition), which is sensible given greater demand and structural exposure. The standardized betas allow comparing relative influence after unit scaling, and the confidence intervals corroborate the precision of these estimates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients Summary Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficient\u003c/p\u003e \u003cp\u003e(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficient\u003c/p\u003e \u003cp\u003e(Std. Error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized Coefficients (Beta)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.0% Confidence Interval for B Lower Bond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95.0% Confidence Interval for B Higher Bond\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear Built\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-135.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Spans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBridge Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.022E-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2..732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADT Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.947E-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ea. Dependent Variable: Condition\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Discussion and Results","content":"\u003cp\u003eSurvival analysis was applied in this research to test the hypothesis that bridge condition ratings decline at different rates over time depending on the material used for the main structure. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates these results: Masonry and Timber bridges exhibit the fastest deterioration in the early stages, while Concrete and Aluminum bridges maintain good condition for a longer period.\u003c/p\u003e \u003cp\u003ePennDOT primarily constructs reinforced concrete bridges, which combine concrete (a mixture of cement, aggregate, and water) with steel reinforcement. This combination significantly enhances durability for several reasons:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStrength in Compression and Tension: Reinforced concrete benefits from concrete\u0026rsquo;s high compressive strength and steel\u0026rsquo;s tensile strength, allowing it to carry heavy loads without cracking or losing structural integrity. This makes it more durable than Masonry, Aluminum, Timber, or plain Steel bridges, as confirmed by the survival analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigh Compressive Strength: Reinforced concrete can achieve compressive strengths up to 10,000 psi, according to industry standards, enabling it to withstand substantial live loads before failure.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe survival results also show that Timber bridges last longer than Masonry bridges, primarily due to load design. Timber bridges in Pennsylvania are typically designed for light traffic only, prohibiting heavy vehicles, whereas Masonry bridges often carry both light and heavy loads. This difference in load capacity explains why Masonry bridges deteriorate faster under stress\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo further understand factors influencing bridge condition, a Multiple Regression Analysis was conducted using SPSS. The research question was: \u003cem\u003e\u0026ldquo;Do ADT, Year Built, Main Span, and Bridge Length impact the bridge condition rating?\u0026rdquo;\u003c/em\u003e The hypotheses were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNull Hypothesis (H₀): No relationship exists between bridge condition and the predictors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlternative Hypothesis (H₁): A significant relationship exists between bridge condition and the predictors.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe regression results rejected the null hypothesis because the significance value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005) indicated a strong relationship between bridge condition and the predictors. Specifically:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHigher ADT, longer bridge length, and greater main span are associated with lower condition ratings, reflecting increased stress and wear.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOlder bridges tend to be in poorer condition, as shown by the negative correlation between Year Built and condition rating.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure 5 visualizes these relationships, confirming that traffic volume, structural dimensions, and age are critical factors in predicting deterioration.\u003c/p\u003e"},{"header":"4. Conclusions, Implications, and Future Research","content":"\u003cp\u003eThis paper highlights the significant challenges in maintaining Pennsylvania\u0026rsquo;s bridge infrastructure, where a large proportion of bridges are currently rated as fair or poor. PennDOT faces multiple obstacles, including funding constraints, aging structures, span length, material type, ADT, and environmental factors, all of which accelerate deterioration and increase the risk of structural failure if not addressed proactively.\u003c/p\u003e \u003cp\u003eOne of the most pressing issues is declining transportation revenue. PennDOT reports that fuel tax revenue, a primary source of funding for infrastructure, has been steadily decreasing due to several trends: the rise of fuel-efficient and electric vehicles, shifts in travel behavior driven by teleworking and e-commerce, and broader technological changes. These factors reduce fuel consumption, making it increasingly difficult to sustain traditional funding models for bridge maintenance and rehabilitation.\u003c/p\u003e \u003cp\u003eTo address these challenges, this study employed statistical methods including linear regression, multiple regression, and survival analysis to evaluate bridge conditions and predict deterioration patterns over time. These models identified key predictors such as traffic volume, bridge age, span length, and material type, providing actionable insights for prioritizing maintenance and allocating resources effectively. While these methods offer robust tools for planning, natural hazards such as sinkholes and scour holes remain difficult to predict and mitigate due to their complexity and the specialized engineering techniques required.\u003c/p\u003e \u003cp\u003eThe findings of this research are critical for engineering decision-making and policy development. They support more informed scheduling and planning of maintenance activities, enabling PennDOT to optimize limited resources. Furthermore, the results underscore the need for innovative funding strategies that account for the growing prevalence of fuel-efficient, hybrid, and electric vehicles. Engaging stakeholders and educating policymakers about these challenges will be essential to secure sustainable funding and foster collaboration for long-term infrastructure resilience.\u003c/p\u003e \u003cp\u003eSinkholes and scour holes represent environmental challenges that are difficult to predict using traditional statistical methods. They occur when subsurface layers collapse over time, often due to chemical reactions between rainwater and soluble rock formations such as limestone. Pennsylvania is particularly susceptible to sinkholes because of its underlying geology. These formations often begin as small depressions with little immediate impact on bridge components but can expand significantly, ultimately compromising structural integrity and lowering bridge condition ratings\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, scour holes develop when fast-moving water erodes streambed material around bridge foundations, exposing and undermining structural supports. Contributing factors include fallen trees, debris accumulation, and bridge alignment, which can accelerate erosion. Scour holes pose a severe risk because they can form beneath foundations, creating hidden vulnerabilities that may lead to catastrophic failure if undetected \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address these risks, specialized tools are essential. Ground Penetrating Radar (GPR) is effective for detecting subsurface anomalies by measuring variations in material depth beneath bridge structures, helping identify early signs of sinkhole formation. Fathometers, on the other hand, measure streambed elevation and depth changes \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, enabling engineers to monitor scour development and take preventive action. These technologies provide critical data for proactive maintenance strategies, complementing statistical models that cannot fully capture these unpredictable environmental hazards.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage Daily Traffic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFHWA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFederal Highway Administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Concrete Bridge Council\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Bridge Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePennDOT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePennsylvania Department of Transportation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransportation Advisory Committee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.S. = data, statistical analysis, wrote manuscriptS.M.V. = advisor, guided research, checked data and analysis, wrote manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset is publicly available through PennDOT and is regularly updated to reflect any maintenance activities, inspections, or structural changes: https://data-pennshare.opendata.arcgis.com/datasets/PennShare::pennsylvania-bridges/about\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTRIP (2024) (June \u003cem\u003ePreserving Pennsylvania\u0026rsquo;s Bridges: The Condition and Funding Needs of Pennsylvania\u0026rsquo;s Aging Bridge System, National Transportation Research Nonprofit\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tripnet.org/reports/preserving-pennsylvanias-bridges-june-2024/\u003c/span\u003e\u003cspan address=\"https://tripnet.org/reports/preserving-pennsylvanias-bridges-june-2024/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cem\u003eAccessed December 2025.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommonwealth of Pennsylvania (2025) Bridge Inspection Terminology. PennDOT. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pa.gov/agencies/penndot/programs-and-doing-business/bridges/bridge-inspection-terminology\u003c/span\u003e\u003cspan address=\"https://www.pa.gov/agencies/penndot/programs-and-doing-business/bridges/bridge-inspection-terminology\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed December 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Nistor I, Colin D, Rennei (2024) 10, (July Influence of Debris Jam Formed by Trees on Bridge Pier Scour, \u003cem\u003eJournal of Hydraulic Engineering\u003c/em\u003e, Vol. 150, No. 5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1061/jhend8.hyeng-13688\u003c/span\u003e\u003cspan address=\"10.1061/jhend8.hyeng-13688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed December 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChetti P, Hesham H, Ali (2022) (June Impact of Daily Traffic on Various Bridge Decks in Different Climatic Regions, European Workshop on Structural Health Monitoring, EWSHM 2022 - Volume 2, pp.1116\u0026ndash;1121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/361337160_Impact_of_Daily_Traffic_on_Various_Bridge_Decks_in_Different_Climatic_Regions\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/361337160_Impact_of_Daily_Traffic_on_Various_Bridge_Decks_in_Different_Climatic_Regions\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed December 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommonwealth of Pennsylvania (2025) Transportation Funding PennDOT. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.pa.gov/agencies/penndot/about-penndot/transportation-funding.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed December 25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTremblay A, Keith Ramsey (2023) (June Market Use and Condition Report for US Concrete Bridges: Based on NBI Data as of Fall 2024, National Concrete Bridge Council. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nationalconcretebridge.org/resources/ConcreteBridgesMarketShareAndPerformance-ePub.pdf\u003c/span\u003e\u003cspan address=\"https://nationalconcretebridge.org/resources/ConcreteBridgesMarketShareAndPerformance-ePub.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed December 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRonald J, Ladyka PE, Wigton BT EIT. CONDITION OF LOCALLY OWNED BRIDGES, \u003cem\u003ePennDOT LTAP technical INFORMATION SHEET #171 SUMMER/\u003c/em\u003e(2016), TS_171.pdf, Accessed December 2025, TS_171.pdf, Accessed December 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommonwealth of Pennsylvania (2025) Sinkholes, \u003cem\u003eDepartment of Conservation and Natural Resources\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.pa.gov/agencies/dcnr/conservation/geology/geologic-hazards/sinkholes.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed December 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayes DC, Drummond FE (1995) Use of Fathometers and Electrical-Conductivity Probes to Monitor Riverbed Scour at Bridge Piers, U.S. Geological Survey, Water-Resources Investigations Report 94-4164, Delaware, Maryland, and Virginia DOTs. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubs.usgs.gov/wri/1994/4164/report.pdf\u003c/span\u003e\u003cspan address=\"https://pubs.usgs.gov/wri/1994/4164/report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed December 202\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlaczek G, Haeni FP (1995) Surface-Geophysical Techniques Used to Detect Existing and Infilled Scour Holes Near Bridge Piers, U.S. Geological Survey, Water-Resources Investigations Report 95-4009, Federal Highway Administration. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubs.usgs.gov/publication/wri954009\u003c/span\u003e\u003cspan address=\"https://pubs.usgs.gov/publication/wri954009\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed December 2025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-infrastructure-preservation-and-resilience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jipr","sideBox":"Learn more about [Journal of Infrastructure Preservation and Resilience](https://jipr.springeropen.com)","snPcode":"43065","submissionUrl":"https://submission.nature.com/new-submission/43065/3","title":"Journal of Infrastructure Preservation and Resilience","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8491604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8491604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith over 30,000 bridges, Pennsylvania ranks third in the number of bridges and sixth in the nation for bridges in poor condition, according to a 2023 analysis by the Transportation Advisory Committee (TRIP) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The Pennsylvania Department of Transportation (PennDOT) handles supporting these structures. Given the essential role bridges play in supporting commercial, personal, and public transportation, the Pennsylvania Department of Transportation (PennDOT) handles supporting these structures. This involves activities such as painting, superstructure replacement, substructure repair, rehabilitation, bridge replacement, and scour protection.\u003c/p\u003e \u003cp\u003ePennDOT faces several factors, including the age of bridges, span length, material type, average daily traffic (ADT), and environmental factors. These factors contribute to the deterioration of bridge conditions and potential failure if no action is taken in the long term.\u003c/p\u003e \u003cp\u003eThis paper will provide a detailed statistical analysis of bridge deterioration across Pennsylvania, examining the factors and also exploring statistical methods, including multiple regression and survival analysis, to assess bridge conditions and predict hazard and time of bridge failure. The paper aims to offer solutions for rehabilitating and improving the condition of Pennsylvania's bridges.\u003c/p\u003e","manuscriptTitle":"Statistical Analysis and Predictive Modeling for Bridge Deterioration in Pennsylvania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 10:06:08","doi":"10.21203/rs.3.rs-8491604/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"165925600182095588573192153115406378950","date":"2026-04-11T05:38:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60268144753561227861990307024613286066","date":"2026-04-03T02:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T05:34:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T03:43:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T03:42:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Infrastructure Preservation and Resilience","date":"2025-12-31T17:48:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-infrastructure-preservation-and-resilience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jipr","sideBox":"Learn more about [Journal of Infrastructure Preservation and Resilience](https://jipr.springeropen.com)","snPcode":"43065","submissionUrl":"https://submission.nature.com/new-submission/43065/3","title":"Journal of Infrastructure Preservation and Resilience","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c905ba8e-f50a-42ac-915f-ef43c4877d72","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T10:06:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 10:06:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8491604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8491604","identity":"rs-8491604","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}