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About Bridge Condition Rating Data Modeling Using Deep Learning Algorithm
This article presents a deep learning approach to model bridge condition rating data using selected data from the National Bridge Inventory database. A convolutional neural network model is trained on historical inspection data to predict future condition ratings of highway bridge components. The model is tested on deck, superstructure, and substructure condition data from bridges in Maryland and Delaware from 1992 to 2017. Research findings show the deep learning model can accurately forecast bridge component conditions with over 85% prediction accuracy.
Detailed Information
| Author: | ['Palisa Arafin'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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