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  • EngiTek 2020
  • 51.Cnf-1062
EngiTek Congresses with Published Papers
EngiTek 2020, Irbid
Papers Published at EngiTek 2020
All 8 Papers
IDAuthors and TitlePages
51.Cnf-17 Mr. Ameer Mubaslat
Mr. Ahmad Al Haj
Prof. Saud Khashan
Simulation assisted leak detection in pressurized systems using machine learning
51.Cnf-1062 Dr. Aqeed M. Chyad
Dr. Osama Abudayyeh
Dr. Maha Alkasisbeh
A Nonlinear Regression-Based Machine Learning Model for Predicting Concrete Bridge Deck Condition
47.Cnf-1071 Dr. Mohammad M. Banat
Mr. Ahmed Al-Shwmi
Detailed Simplified Implementation of Filter Bank Multicarrier Modulation Using Sub-Channel Prototype Filters
47.Cnf-1097 Dr. Mohammad M. Banat
Mrs. Haifaa Elayyan
Academic Rewards Coin (ARC) Concept: Mining Academic Contribution Into Bitcoin
47.Cnf-1098 Mr. Chris Matrakidis
Dr. Dimitris Uzunidis
Alexandros Stavdas
Prof. Gerasimos Pagiatakis
Analytical modeling of dispersion penalty for NRZ transmission
47.Cnf-1099 Dr. Dimitris Uzunidis
Mr. Chris Matrakidis
Alexandros Stavdas
Prof. Gerasimos Pagiatakis
On the Attainable Transparent Length of Multi-Band Optical Systems Employing Rare – Earth Doped Fiber Amplifiers
47.Cnf-1101 Mr. Georgios D. Kolezas
Dr. Grigorios Zouros
Prof. Gerasimos Pagiatakis
Complex Magnetic and Electric Dipolar Resonances of Subwavelength GaAs Prolate Spheroids
51.Cnf-1104 Dr. Rafat Al-Waked
Underground Car Park - Mechanical Ventilation
51.Cnf-1062 Paper View Page
Title A Nonlinear Regression-Based Machine Learning Model for Predicting Concrete Bridge Deck Condition
Authors Dr. Aqeed M. Chyad, General Directorate of Power Transmission Projects, Babylon, Iraq
Dr. Osama Abudayyeh, Western Michigan University, Kalamazoo, MI, USA
Dr. Maha Alkasisbeh, Hashemite University, Zarqa, Jordan
Abstract 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.
Track MDM: Materials, Design and Manufacturing
Conference 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
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)},
organization={Mosharaka for Research and Studies} }
Paper Views 87 Paper Views Rank 49/524
Paper Downloads 37 Paper Downloads Rank 53/524
EngiTek 2020 Visits: 48784||MechaniTek 2020 Visits: 22733||MDM Track Visits: 4902