Using Machine Learning to Identify the Most Critical Factors Affecting Maintenance of Tunnels-Reference-Cited by-同舟云学术

Using Machine Learning to Identify the Most Critical Factors Affecting Maintenance of Tunnels

Published:2022-05-24 Issue: Volume: Page:
ISSN:
Container-title:Computing in Civil Engineering 2021
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Author:

Ahmed Muaz O.¹¹¹²,Khalef Ramy¹¹¹²,Ali Gasser G.¹¹¹²,El-Adaway Islam H.¹¹¹²

Affiliation:

1. Ph.D. Candidate, Dept. of Civil, Architectural, and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO.

2. Hurst-McCarthy Professor of Construction Engineering and Management, Dept. of Civil, Architectural, and Environmental Engineering and Dept. of Engineering Management and Systems Engineering, Missouri Univ. of Science and Technology, Rolla, MO.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784483893.175

Reference25 articles.

1. Ali G. Elsayegh A. Assaad R. and El-Adaway I. H. (2019). “Artificial Neural Network Model for Bridge Deterioration and Assessment.” CSCE Annual Conference 11.

2. Bridge Infrastructure Asset Management System: Comparative Computational Machine Learning Approach for Evaluating and Predicting Deck Deterioration Conditions

3. Comparing the Impact of Learning in Bidding Decision-Making Processes Using Algorithmic Game Theory

4. Secondary triage classification using an ensemble random forest technique

5. Risk-cost optimised maintenance strategy for tunnel structures

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