Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach
Abstract This study presents a data-driven framework to identify and evaluate key variables contributing to four major distresses in Continuously Reinforced Concrete Pavement (CRCP): longitudinal cracking, spalling, punchouts, and transverse cracking. A Random Forest regression approach was applied...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
|
| Series: | Discover Civil Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44290-025-00302-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849331648784498688 |
|---|---|
| author | Ali Alnaqbi Ghazi G. Al-Khateeb Waleed Zeiada Muamer Abuzwidah |
| author_facet | Ali Alnaqbi Ghazi G. Al-Khateeb Waleed Zeiada Muamer Abuzwidah |
| author_sort | Ali Alnaqbi |
| collection | DOAJ |
| description | Abstract This study presents a data-driven framework to identify and evaluate key variables contributing to four major distresses in Continuously Reinforced Concrete Pavement (CRCP): longitudinal cracking, spalling, punchouts, and transverse cracking. A Random Forest regression approach was applied to 395 observations extracted from the Long-Term Pavement Performance (LTPP) database, incorporating structural, climatic, and traffic-related inputs. Feature importance analysis revealed that temperature, Annual Average Daily Truck Traffic (AADTT), and Layer 3 thickness consistently ranked among the top predictors across multiple distress types. Spalling was highly predictable using only a few variables (R² >0.85), whereas longitudinal and transverse cracking required more features to reach moderate accuracy, indicating complex, multifactorial mechanisms. Punchouts showed moderate predictability, influenced primarily by freeze index and heavy truck traffic. Sequential feature addition analysis confirmed that model performance improved significantly with the top-ranked variables, with diminishing returns thereafter. The proposed framework supports the development of parsimonious, interpretable models that can inform efficient CRCP design and maintenance strategies across varying environmental and operational conditions. |
| format | Article |
| id | doaj-art-7f8f033470a142059cffbf5d202cd938 |
| institution | Kabale University |
| issn | 2948-1546 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Civil Engineering |
| spelling | doaj-art-7f8f033470a142059cffbf5d202cd9382025-08-20T03:46:27ZengSpringerDiscover Civil Engineering2948-15462025-08-012112010.1007/s44290-025-00302-zRandom forest-based frame work for multi-distress prediction in CRCP: a feature importance approachAli Alnaqbi0Ghazi G. Al-Khateeb1Waleed Zeiada2Muamer Abuzwidah3Department of Civil and Environmental Engineering, University of SharjahDepartment of Civil and Environmental Engineering, University of SharjahDepartment of Civil and Environmental Engineering, University of SharjahDepartment of Civil and Environmental Engineering, University of SharjahAbstract This study presents a data-driven framework to identify and evaluate key variables contributing to four major distresses in Continuously Reinforced Concrete Pavement (CRCP): longitudinal cracking, spalling, punchouts, and transverse cracking. A Random Forest regression approach was applied to 395 observations extracted from the Long-Term Pavement Performance (LTPP) database, incorporating structural, climatic, and traffic-related inputs. Feature importance analysis revealed that temperature, Annual Average Daily Truck Traffic (AADTT), and Layer 3 thickness consistently ranked among the top predictors across multiple distress types. Spalling was highly predictable using only a few variables (R² >0.85), whereas longitudinal and transverse cracking required more features to reach moderate accuracy, indicating complex, multifactorial mechanisms. Punchouts showed moderate predictability, influenced primarily by freeze index and heavy truck traffic. Sequential feature addition analysis confirmed that model performance improved significantly with the top-ranked variables, with diminishing returns thereafter. The proposed framework supports the development of parsimonious, interpretable models that can inform efficient CRCP design and maintenance strategies across varying environmental and operational conditions.https://doi.org/10.1007/s44290-025-00302-zContinuously reinforced concrete pavementPavement distressesMachine learningFeature importanceSustainable pavement management |
| spellingShingle | Ali Alnaqbi Ghazi G. Al-Khateeb Waleed Zeiada Muamer Abuzwidah Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach Discover Civil Engineering Continuously reinforced concrete pavement Pavement distresses Machine learning Feature importance Sustainable pavement management |
| title | Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach |
| title_full | Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach |
| title_fullStr | Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach |
| title_full_unstemmed | Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach |
| title_short | Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach |
| title_sort | random forest based frame work for multi distress prediction in crcp a feature importance approach |
| topic | Continuously reinforced concrete pavement Pavement distresses Machine learning Feature importance Sustainable pavement management |
| url | https://doi.org/10.1007/s44290-025-00302-z |
| work_keys_str_mv | AT alialnaqbi randomforestbasedframeworkformultidistresspredictionincrcpafeatureimportanceapproach AT ghazigalkhateeb randomforestbasedframeworkformultidistresspredictionincrcpafeatureimportanceapproach AT waleedzeiada randomforestbasedframeworkformultidistresspredictionincrcpafeatureimportanceapproach AT muamerabuzwidah randomforestbasedframeworkformultidistresspredictionincrcpafeatureimportanceapproach |