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: | Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada, Muamer Abuzwidah |
|---|---|
| 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!
|
Similar Items
-
Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines
by: Ali Alnaqbi, et al.
Published: (2025-05-01) -
Machine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement
by: Ali Alnaqbi, et al.
Published: (2025-03-01) -
A hybrid machine learning method of support vector regression with particle swarm optimization for predicting IRI in continuously reinforced concrete pavement
by: Ali Alnaqbi, et al.
Published: (2025-08-01) -
Hybrid machine learning applications in pavement engineering: predicting spalling with PSO-GBM
by: Ali Alnaqbi, et al.
Published: (2025-06-01) -
Leveraging Maintenance Management Techniques to Evaluate Pavement Condition Index: Central Iraq Highway System
by: Ahmed Aljubory, et al.
Published: (2025-03-01)