Evaluating and Predicting Deterioration of Bridges Using Machine Learning Applications-Reference-Cited by-同舟云学术

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Evaluating and Predicting Deterioration of Bridges Using Machine Learning Applications

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:Structures Congress 2023
language:
Short-container-title:

Author:

Sabellano Ruel¹²³,Bandpey Zeinab¹²³,Shokouhian Mehdi¹²³

Affiliation:

1. Doctoral Candidate, Dept. of Civil Engineering, Morgan State Univ., Baltimore City, MD.

2. Postdoctoral Research Associate, Dept. of Civil Engineering, Morgan State Univ., Baltimore City, MD.

3. Assistant Professor, Dept. of Civil Engineering, Morgan State Univ., Baltimore City, MD.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484777.014

Reference47 articles.

1. AASHTO. (June 2013). Transportation Asset Management Guide: A Foucs on Implementation. FHWA. Retrieved from https://www.fhwa.dot.gov/asseet/

2. Applications of Artificial Intelligence in Transport: An Overview;Abduljabbar, R. a.;Sustainability,2019

3. Abu-Tair A. McParland C. Lyness J. & Nadjai A. (March 2022). Predictive models of deterioration rates of bridges uisng the factor method based on historic inspection data. Proceedings of the 9th International Conference on Durability of Building Materials and Components (DBMC). Brisbane Australia.

4. Image-based retrieval of concrete crack properties for bridge inspection

5. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research

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