An interpretation framework of machine learning models: prediction of pavement long-term performance-Reference-Cited by-同舟云学术

An interpretation framework of machine learning models: prediction of pavement long-term performance

Published:2024-08-09 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:

Wu Jiantao¹,Chen Jiaqi¹,Liu Quan¹,Ma Xinyuan²

Affiliation:

1. College of Civil and Transportation Engineering, Hohai University, Nanjing, People’s Republic of China

2. Technical Quality Department, General Contracting and Operation Branch of China Communications Construction Company, Beijing, People’s Republic of China

Funder

Natural Science Foundation of Jiangsu Province

National Science Foundation of China

Natural Science Youth Fund of Jiangsu Province

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2024.2387737

Reference20 articles.

1. Deterministic Performance Prediction Model for Rehabilitation and Management of Flexible Pavement

2. Visualizing the effects of predictor variables in black box supervised learning models

3. A gradient boosting approach to understanding airport runway and taxiway pavement deterioration

4. Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

5. Fathi, A., Mazari, M., Saghafi, M., Hosseini, A., & Kumar, S., (2019). Parametric study of pavement deterioration using machine learning algorithms. In: I. Al-Qadi, H. Ozer, A. Loizos, and S. Murrell, eds. Airfield and highway pavements 2019: innovation and sustainability in highway and airfield pavement technology. Reston, VA: American Society of Civil Engineers, 31–41.