Bridge Infrastructure Management System: Autoencoder Approach for Predicting Bridge Condition Ratings-Reference-Cited by-同舟云学术

Bridge Infrastructure Management System: Autoencoder Approach for Predicting Bridge Condition Ratings

Published:2023-03 Issue:1 Volume:29 Page:
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Rajkumar Monica¹^ORCID,Nagarajan Sudhagar²,Arockiasamy Madasamy³

Affiliation:

1. Graduate Research Assistant, Dept. of Civil, Environmental, and Geomatics Engineering, Florida Atlantic Univ., 777 Glades Rd., Boca Raton, FL 33431 (corresponding author). ORCID: .

2. Associate Professor, Dept. of Civil, Environmental, and Geomatics Engineering, Florida Atlantic Univ., 777 Glades Rd., Boca Raton, FL 33431.

3. Professor, Dept. of Civil, Environmental, and Geomatics Engineering, Florida Atlantic Univ., 777 Glades Rd., Boca Raton, FL 33431.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JITSE4.ISENG-2123

Reference40 articles.

1. ASCE. 2017. “ASCE’s 2021 infrastructure report card.” Accessed March 3 2021. https://www.infrastructurereportcard.org/.

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

3. Bergstra J. D. Yamins and D. D. Cox. 2013. “Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms.” In Vol. 13 of Proc. 12th Python in Science Conf. 20. Austin TX: SciPy.

4. Prediction of Unit Price Bids of Resurfacing Highway Projects through Ensemble Machine Learning

5. Bridges across borders: A clustering approach to support EU regional policy

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