A novel approach for asphalt pavement invisible distress classification prediction by machine learning algorithms-Reference-Cited by-同舟云学术

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A novel approach for asphalt pavement invisible distress classification prediction by machine learning algorithms

Published:2024-04-25 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:

Shi Jicun¹^ORCID,Jia Shili²,Bai Chengxiang¹,Wang Duanyi³

Affiliation:

1. School of Civil Engineering and Architecture, Xinxiang University, Xinxiang, People's Republic of China

2. School of Foreign Languages, Xinxiang University, Xinxiang, People's Republic of China

3. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, People's Republic of China

Funder

Science and Technology Project of Henan Province

National Key R&D Program of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2024.2343087

Reference48 articles.

1. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis

2. Aberasturi D. 2023. Violin plot. New York: Wiley Online Library. 1–7.

3. Improving Systemwide Sustainability in Pavement Preservation Programming

4. Ben-Dov M. and Feldman R. 2010. Data mining and knowledge discovery handbook. Boston: Springer Science+Business Media 809–835 Ch. 3..

5. Automated Pavement Distress Detection, Classification and Measurement: A Review

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