Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, the efficacy of Random Forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) in predi...

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Bibliographic Details
Main Authors: Fariba Fard, Fereshteh Sadeghi Naieni Fard
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Remote Sensing
Subjects:
NBI
traffic
climate regions
bridge deck
condition rating prediction
Random Forest
Online Access:https://www.mdpi.com/2072-4292/16/2/367

Internet

https://www.mdpi.com/2072-4292/16/2/367

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