Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking
Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/14/3/709 |
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| author | Ali Alnaqbi Waleed Zeiada Ghazi G. Al-Khateeb Muamer Abuzwidah |
| author_facet | Ali Alnaqbi Waleed Zeiada Ghazi G. Al-Khateeb Muamer Abuzwidah |
| author_sort | Ali Alnaqbi |
| collection | DOAJ |
| description | Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R<sup>2</sup> of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking. |
| first_indexed | 2024-04-24T18:29:27Z |
| format | Article |
| id | doaj.art-c494366f89f84801aeb1ebaa1efcbc0d |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-04-24T18:29:27Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-c494366f89f84801aeb1ebaa1efcbc0d2024-03-27T13:29:18ZengMDPI AGBuildings2075-53092024-03-0114370910.3390/buildings14030709Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal CrackingAli Alnaqbi0Waleed Zeiada1Ghazi G. Al-Khateeb2Muamer Abuzwidah3Department of Civil and Environmental Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesDepartment of Civil and Environmental Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesDepartment of Civil and Environmental Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesDepartment of Civil and Environmental Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesRoads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R<sup>2</sup> of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.https://www.mdpi.com/2075-5309/14/3/709longitudinal crackingtop-down fatiguemachine learningLTPPprediction models |
| spellingShingle | Ali Alnaqbi Waleed Zeiada Ghazi G. Al-Khateeb Muamer Abuzwidah Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking Buildings longitudinal cracking top-down fatigue machine learning LTPP prediction models |
| title | Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking |
| title_full | Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking |
| title_fullStr | Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking |
| title_full_unstemmed | Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking |
| title_short | Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking |
| title_sort | machine learning modeling of wheel and non wheel path longitudinal cracking |
| topic | longitudinal cracking top-down fatigue machine learning LTPP prediction models |
| url | https://www.mdpi.com/2075-5309/14/3/709 |
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