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- W4367553870 abstract "The proliferation of element level condition data recorded by state departments of transportation on bridges makes it pertinent to consider a more efficient use of these data for an improved bridge and asset management. This paper unravels how individual elements can predict the overall condition of the bridge since the direct relationship between component rating and element health index does not follow a regular pattern. For this study, element data from 20,250 bridges across five different states in the Mid-Atlantic United States were collected from the Federal Highway Administration web portals. The health indices of the bridge elements were computed using four different weight distribution ratios based on the four condition states: good, fair, poor, and severe. The efficiency of each weight distribution method was tested by running an artificial neural network (ANN) and logistic regression model with the health index of the elements as the feature variable and the overall bridge condition as the target variable. The results show that the dataset with 1:0.40:0.25:0 weight distribution ratio gave the highest prediction accuracy of 87% and also for the f1-score. The weight of the bridge elements were also determined from this ANN model." @default.
- W4367553870 created "2023-05-01" @default.
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- W4367553870 date "2023-05-01" @default.
- W4367553870 modified "2023-09-23" @default.
- W4367553870 title "Bridge Element Weights Based on Data-Driven Model with Artificial Neural Networks" @default.
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- W4367553870 doi "https://doi.org/10.1061/9780784484777.011" @default.
- W4367553870 hasPublicationYear "2023" @default.
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