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<p>The long-term performance of Continuously Reinforced Concrete Pavement (CRCP) and the optimization of maintenance strategies depend on the accurate forecasting of the International Roughness Index (IRI). For the purpose of accurately predicting the IRI in CRCP, this study offers a strong hybrid modeling framework that combines Support Vector Regression (SVR) with Genetic Algorithm (GA) optimization. Utilizing an extensive dataset from the Long-Term Pavement Performance (LTPP) program that included 395 observations and 33 CRCP sections, the suggested GA-SVR model was assessed against a number of benchmark models, such as Artificial Neural Networks (ANN), Decision Trees, Random Forests, Linear Regression, and SVR. The GA-optimized SVR model significantly outperformed all alternatives, achieving a mean RMSE of 0.039 and a coefficient of determination (R²) of 0.991 across five-fold cross-validation. Comprehensive residual analysis confirmed the model’s stability, while sensitivity analysis and feature importance rankings identified key influential variables such as Initial IRI, Layer 4 Type, and Layer 3 Thickness. Partial Dependence Plots and 3D visualizations further demonstrated how these factors affect IRI trends. The findings underscore the model’s high reliability, interpretability, and potential to support proactive pavement maintenance and design decisions. This research contributes a scalable and interpretable tool for enhancing the predictive capabilities of pavement performance models in data-driven infrastructure management.</p>

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High-Accuracy prediction of roughness in CRCP using a hybrid genetic Algorithm–SVR approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Ali Alnaqbi,
  • Ghazi Al-Khateeb,
  • Waleed Zeiada,
  • Khaled Hamad

摘要

The long-term performance of Continuously Reinforced Concrete Pavement (CRCP) and the optimization of maintenance strategies depend on the accurate forecasting of the International Roughness Index (IRI). For the purpose of accurately predicting the IRI in CRCP, this study offers a strong hybrid modeling framework that combines Support Vector Regression (SVR) with Genetic Algorithm (GA) optimization. Utilizing an extensive dataset from the Long-Term Pavement Performance (LTPP) program that included 395 observations and 33 CRCP sections, the suggested GA-SVR model was assessed against a number of benchmark models, such as Artificial Neural Networks (ANN), Decision Trees, Random Forests, Linear Regression, and SVR. The GA-optimized SVR model significantly outperformed all alternatives, achieving a mean RMSE of 0.039 and a coefficient of determination (R²) of 0.991 across five-fold cross-validation. Comprehensive residual analysis confirmed the model’s stability, while sensitivity analysis and feature importance rankings identified key influential variables such as Initial IRI, Layer 4 Type, and Layer 3 Thickness. Partial Dependence Plots and 3D visualizations further demonstrated how these factors affect IRI trends. The findings underscore the model’s high reliability, interpretability, and potential to support proactive pavement maintenance and design decisions. This research contributes a scalable and interpretable tool for enhancing the predictive capabilities of pavement performance models in data-driven infrastructure management.