Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines
Abstract Longitudinal cracking poses a serious threat to the longevity and functionality of continuously reinforced concrete pavement (CRCP). Using structural, traffic, and climatic data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a machine learning system base...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | Journal of Engineering and Applied Science |
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| Online Access: | https://doi.org/10.1186/s44147-025-00623-x |
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| author | Ali Alnaqbi Ghazi G. Al-Khateeb Waleed Zeiada |
| author_facet | Ali Alnaqbi Ghazi G. Al-Khateeb Waleed Zeiada |
| author_sort | Ali Alnaqbi |
| collection | DOAJ |
| description | Abstract Longitudinal cracking poses a serious threat to the longevity and functionality of continuously reinforced concrete pavement (CRCP). Using structural, traffic, and climatic data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a machine learning system based on a gradient boosting machine (GBM) optimized using particle swarm optimization (PSO) to forecast longitudinal cracking. The proposed PSO-GBM model achieved the lowest mean RMSE (2.661) and highest R 2 (0.984) across fivefold cross-validation, outperforming baseline GBM, linear regression, random forest, artificial neural networks (ANN), and support vector regression (SVR). Compared to traditional and untuned models, the PSO-GBM offers improved generalization and a stronger ability to capture nonlinear interactions among variables. Feature importance and sensitivity analyses identified L3 thickness, age, and AADTT as key predictors. Despite the model’s exceptional predictive accuracy, computational demands and data availability may limit its practical application. However, the results offer useful information for transportation organizations looking to improve maintenance planning techniques and incorporate intelligent predictive tools into pavement management systems. |
| format | Article |
| id | doaj-art-169491815d094a79901285f3a16b4eab |
| institution | Kabale University |
| issn | 1110-1903 2536-9512 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj-art-169491815d094a79901285f3a16b4eab2025-08-20T03:48:18ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122025-05-0172112710.1186/s44147-025-00623-xPredictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machinesAli Alnaqbi0Ghazi G. Al-Khateeb1Waleed Zeiada2Department of Civil and Environmental Engineering, University of SharjahDepartment of Civil and Environmental Engineering, University of SharjahDepartment of Civil and Environmental Engineering, University of SharjahAbstract Longitudinal cracking poses a serious threat to the longevity and functionality of continuously reinforced concrete pavement (CRCP). Using structural, traffic, and climatic data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a machine learning system based on a gradient boosting machine (GBM) optimized using particle swarm optimization (PSO) to forecast longitudinal cracking. The proposed PSO-GBM model achieved the lowest mean RMSE (2.661) and highest R 2 (0.984) across fivefold cross-validation, outperforming baseline GBM, linear regression, random forest, artificial neural networks (ANN), and support vector regression (SVR). Compared to traditional and untuned models, the PSO-GBM offers improved generalization and a stronger ability to capture nonlinear interactions among variables. Feature importance and sensitivity analyses identified L3 thickness, age, and AADTT as key predictors. Despite the model’s exceptional predictive accuracy, computational demands and data availability may limit its practical application. However, the results offer useful information for transportation organizations looking to improve maintenance planning techniques and incorporate intelligent predictive tools into pavement management systems.https://doi.org/10.1186/s44147-025-00623-xLongitudinal crackingConcrete pavementParticle swarm optimizationGradient boosting machineHybrid machine learningPavement management systems |
| spellingShingle | Ali Alnaqbi Ghazi G. Al-Khateeb Waleed Zeiada Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines Journal of Engineering and Applied Science Longitudinal cracking Concrete pavement Particle swarm optimization Gradient boosting machine Hybrid machine learning Pavement management systems |
| title | Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines |
| title_full | Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines |
| title_fullStr | Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines |
| title_full_unstemmed | Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines |
| title_short | Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines |
| title_sort | predictive modeling of longitudinal cracking in crcp using pso tuned gradient boosting machines |
| topic | Longitudinal cracking Concrete pavement Particle swarm optimization Gradient boosting machine Hybrid machine learning Pavement management systems |
| url | https://doi.org/10.1186/s44147-025-00623-x |
| work_keys_str_mv | AT alialnaqbi predictivemodelingoflongitudinalcrackingincrcpusingpsotunedgradientboostingmachines AT ghazigalkhateeb predictivemodelingoflongitudinalcrackingincrcpusingpsotunedgradientboostingmachines AT waleedzeiada predictivemodelingoflongitudinalcrackingincrcpusingpsotunedgradientboostingmachines |