Correlations Between Sets of Data for Investigating Bridge Conditions in Virginia
Open Access DepositedThe main objective of this thesis is to investigate the predominant linear correlations between data sets in the National Bridge Inventory (NBI) Bridge Condition Rating. An overall bridge condition rating can be any of the following three: “Poor”, “Fair” and “Good”. According to the NBI criteria, a structure is rated as “Good” if all its structural components are rated as 7 or better, rated “Fair” when all structural components are rated in the range 5 to 6, and rated “Poor” when all structural components are rated 4 and below. The NBI also states the bridge condition rating is determined by the lowest rating of substructure, superstructure, and deck condition ratings, respectively. Using the LTBP InfoBridgeTM web portal, a query of bridges in the commonwealth of Virginia was performed in achieving the main objective of this project. This query provided a total of 14,068 bridges. Because the topic of this thesis deals with overall bridge condition ratings, only bridges that cross over at least one lane of traffic were investigated in this research. The logic behind excluding bridges that have no traffic lanes under the structure is that these bridges are not susceptible to either overhead and/or pier collisions resulting from heavy truck collisions. A bridge’s component condition rating plays a significant role in determining the likelihood of collapse resulting from heavy truck collisions. As a result, only 2,719 bridges across the commonwealth of Virginia were investigated in this research. Data mining of these bridges led to the development of a correlation between sets of data that are auxiliary predictors of overall bridge condition ratings. Linear correlations between these sets of data were established and are presented herein with the aid of maps, scatter plots, distribution plots and the Pearson Correlation Coefficients. In conclusion, data correlations play a crucial role in identifying key factors influencing bridge condition ratings. This insight is essential for evaluating if the current classification method adequately represents bridge health, thus supporting effective maintenance and safety measures.
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Commissiong_gwu_0075M_16884.pdf | 2024-10-02 | Open Access |
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