Research on Driving Factors of the RIOHTrack Rutting Prediction Model Based on Interpretable Ensemble Learning

LI Min; LI Zhuoxuan; SHI Xinli; CAO Jinde

doi:10.21656/1000-0887.450066

Volume 46 Issue 1

Jan. 2025

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Article Navigation > Applied Mathematics and Mechanics > 2025 > 46(1): 92-104

LI Min, LI Zhuoxuan, SHI Xinli, CAO Jinde. Research on Driving Factors of the RIOHTrack Rutting Prediction Model Based on Interpretable Ensemble Learning[J]. Applied Mathematics and Mechanics, 2025, 46(1): 92-104. doi: 10.21656/1000-0887.450066

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Research on Driving Factors of the RIOHTrack Rutting Prediction Model Based on Interpretable Ensemble Learning

doi: 10.21656/1000-0887.450066

LI Min^1
,,
LI Zhuoxuan^{2, 3
,},
SHI Xinli^{1, 2
,
,},
CAO Jinde^{2, 3
,}

1.
School of Cyber Science and Engineering, Southeast University, Nanjing 210096, P.R.China
2.
Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Nanjing 211100, P.R.China
3.
School of Mathematics, Southeast University, Nanjing 211189, P.R.China

Received Date: 2024-03-12
Rev Recd Date: 2024-11-28
Publish Date: 2025-01-01

Abstract

Abstract

The transport infrastructure is the foundation of modern social and economic development, where the asphalt pavement plays an important role as a key component. Accurate prediction of asphalt pavement conditions is of great significance to guide pavement maintenance work. Rutting is an important indicator for evaluating the health condition of asphalt pavement. Existing asphalt pavement condition prediction models are mainly based on mechanical experience models or machine learning technologies. However, these methods lack interpretability and cannot provide relevant information on the extent to which the input features affect rutting. Herein, an interpretable integrated learning framework (FI-EL-SHAP) was established, in which the FI module filters features with the entropy weight method and the Pareto analysis, the EL module evaluates the performances of different models and selects the optimal model, and the SHAP module performs visual analysis on the relationship between input features and model outputs to reveal the impacts of different features on model prediction results. This study realizes a quantitative analysis of the rut formation mechanism while ensuring the model accuracy.
- asphalt pavement,
- rutting,
- ensemble learning,
- interpretability

(Contributed by CAO Jinde, M.AMM Editorial Board)

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References(43)

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