Modeling and Predicting Deterioration of Concrete Bridge Elements Using Machine Learning-Reference-Cited by-同舟云学术

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Modeling and Predicting Deterioration of Concrete Bridge Elements Using Machine Learning

Published:2024-03-18 Issue: Volume:317 Page:769-777
ISSN:
Container-title:Construction Research Congress 2024
language:
Short-container-title:

Author:

Ghafoori Mahdi¹²³,Abdallah Moatassem¹²³,Egemen Ozbek Mehmet¹²³

Affiliation:

1. Assistant Professor, Dept. of Building Construction Science, Mississippi State Univ., Starkville, MS.

2. Associate Professor, Dept. of Civil Engineering, Univ. of Colorado Denver, Denver, CO.

3. Professor and Joseph Phelps Endowed Chair, Dept. of Construction Management, Colorado State Univ., Fort Collins, CO.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485279.077

Reference14 articles.

1. Data-driven prediction of long-term deterioration of RC bridges;Alonso Medina P.;Construction and Building Materials,2022

2. Developing Bridge Deterioration Models Using an Artificial Neural Network;Althaqafi E.;Infrastructures,2022

3. Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach;Farahani M. A.;Entropy,2022

4. FHWA. (2018). FHWA Bridge Preservation Guide Maintaining a Resilient Infrastructure to Preserve Mobility Quality Assurance Statement.

5. Simulation-Based Optimization Model to Minimize Equivalent Annual Cost of Existing Buildings;Ghafoori M.;Journal of Construction Engineering and Management,2022

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