Understanding the process of concrete
bridge deck deterioration and evaluating its condition
are an important for maintaining a healthy
transportation infrastructure and for allocating the
necessary funds for bridge maintenance,
rehabilitation, or reconstruction actions. Therefore, it
is very important to investigate the factors impacting
bridge condition to enable the development of
predictive techniques. The main objective of this
paper is to study the impact of average daily traffic
(ADT), age, and deck area on the concrete bridge deck
deterioration. Michigan concrete bridge deck
condition data for the past 25 years were analyzed to
determine the impact of these factors on concrete
decks. An optimum machine learning algorithm that
is based on nonlinear regression modeling has been
developed to predict the deterioration rates of bridge
decks under these impacting factors. This study has
revealed that ADT, age, and deck area have a
significant effect on the deterioration of concrete
bridge decks.
1st International Conference on Mechanical Engineering and Technologies (MechaniTek 2020)
Congress
International Congress on Engineering Technologies (EngiTek 2020), 16-18 June 2020, Irbid, Jordan
Pages
7-11
Topics
Structural Health Monitoring Structural Control
ISSN
2227-331X
DOI
BibTeX
@inproceedings{1062EngiTek2020,
title={A Nonlinear Regression-Based Machine Learning Model for Predicting Concrete Bridge Deck Condition},
author={Aqeed M. Chyad, and Osama Abudayyeh, and Maha Alkasisbeh},
booktitle={International Congress on Engineering Technologies (EngiTek 2020)},
year={2020},
pages={7-11},
doi={}},
organization={Mosharaka for Research and Studies}
}