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Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network
Article describes how accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. This study aims to assess the effectiveness of these algorithms for deck condition rating prediction at the national level.
Date:
January 16, 2024
Creator:
Fard, Fariba & Fard, Fereshteh Sadeghi Naieni
System:
The UNT Digital Library