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Prediction of International Roughness Index Based on Stacking Fusion Model

Author

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  • Zhiyuan Luo

    (School of Civil Engineering, Chongqing University, Chongqing 400045, China
    College of Artificial Intelligence, Southwest University, Chongqing 400715, China)

  • Hui Wang

    (School of Civil Engineering, Chongqing University, Chongqing 400045, China
    Key Laboratory of New Technology for Construction of Cities in Mountain Area (Chongqing University), Ministry of Education, Chongqing 400045, China)

  • Shenglin Li

    (College of Artificial Intelligence, Southwest University, Chongqing 400715, China)

Abstract

Pavement performance prediction is necessary for road maintenance and repair (M&R) management and plans. The accuracy of performance prediction affects the allocation of maintenance funds. The international roughness index (IRI) is essential for evaluating pavement performance. In this study, using the road pavement data of LTPP (Long-Term Pavement Performance), we screened the feature parameters used for IRI prediction using the mean decrease impurity (MDI) based on random forest (RF). The effectiveness of this feature selection method was proven suitable. The prediction accuracies of four promising prediction models were compared, including Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). The two integrated learning algorithms, GBDT and XGBoost, performed well in prediction. GBDT performs best with the lowest root mean square error (RMSE) of 0.096 and the lowest mean absolute error (MAE) of 6.2% and the coefficient of determination (R 2 ) reaching 0.974. However, the prediction accuracy varies in numerical intervals, with some deviations. The stacking fusion model with a powerful generalization capability is proposed to build a new prediction model using GBDT and XGBoost as the base learners and bagging as the meta-learners. The R 2 , RMSE, and MAE of the stacking fusion model are 0.996, 0.040, and 1.3%, which further improves the prediction accuracy and verifies the superiority of this fusion model in pavement performance prediction. Besides, the prediction accuracy is generally consistent across different numerical intervals.

Suggested Citation

  • Zhiyuan Luo & Hui Wang & Shenglin Li, 2022. "Prediction of International Roughness Index Based on Stacking Fusion Model," Sustainability, MDPI, vol. 14(12), pages 1-13, June.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:6949-:d:832958
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    Citations

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    Cited by:

    1. Bozhi & Mahmoud Mohamed & Vahid Najafi Moghaddam Gilani & Ayesha Amjad & Mohammed Sh. Majid & Khalid Yahya & Mohamed Salem, 2023. "A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    2. Hui Wang & Xun Zhang & Shengchuan Jiang, 2022. "A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    3. Kelum Sandamal & Sachini Shashiprabha & Nitin Muttil & Upaka Rathnayake, 2023. "Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    4. Hadi Nayyeri & Lei Xu & Atefeh Ahmadi Dehrashid & Payam Mohammadi Khanghah, 2024. "A development in the approach of assessing the sensitivity of road networks to environmental hazards using functional machine learning algorithm and fractal methods," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 28033-28061, November.

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