Development and Utilization of Big Bridge Data for Predicting Deck Condition Rating Using Machine Learning Algorithms
Description:
Accurately predicting the deck condition rating of a bridge is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, a nationwide model has not been developed. This study aims to identify an appropriate machine learning (ML) algorithm that can accurately predict the deck condition ratings of the nation's bridges. To achieve this, the study collected big bridge data (BBD), which includes NBI, traffic, climate, and hazard data…
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This item is restricted from view until June 1, 2025.
Date:
May 2023
Creator:
Fard, Fariba
Partner:
UNT Libraries