Enhancing local road pavement condition prediction using Bayesian-optimized ensemble machine learning and adaptive synthetic sampling technique-Reference-Cited by-同舟云学术

Enhancing local road pavement condition prediction using Bayesian-optimized ensemble machine learning and adaptive synthetic sampling technique

Published:2024-07-10 Issue:1 Volume:25 Page:
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Elshaboury Nehal¹,Yamany Mohamed S.²,Labi Samuel³,Smadi Omar⁴

Affiliation:

1. Construction and Project Management Research Institute, Housing and Building National Research Centre, Giza, Egypt

2. Department of Construction Engineering, Zagazig University, Zagazig, Egypt

3. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA

4. Department of Civil, Construction and Environmental Engineering, Center for Transportation Research and Education, Iowa State University, Ames, IA, USA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2024.2365957

Reference70 articles.

1. AASHTO 2021. 2021–2026 AASHTO strategic plan. Washington DC: American Association of State Highway and Transportation Professionals. Available from: https://www.aashtoplan.com/.

2. International Roughness Index prediction model for flexible pavements

3. Detection of Unreported Treatments in Pavement Management System of Iowa DOT Using Machine Learning Classification Algorithm

4. Ahmed, N. S., 2021. Predicting pavement structural condition using machine learning methods. Doctoral dissertation. University of South Carolina.

5. Prediction of Pavement Maintenance Performance Using an Expert System

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