Ali, A., Esekbi, M., Sreh, M. (2023). Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs). test, 4(1), 179-192. doi: 10.21608/ijaebs.2023.165192.1054
Abdualmtalab Abdualaziz Ali; Mohamed Imbarek Esekbi; Muftah Mohamed Sreh. "Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs)". test, 4, 1, 2023, 179-192. doi: 10.21608/ijaebs.2023.165192.1054
Ali, A., Esekbi, M., Sreh, M. (2023). 'Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs)', test, 4(1), pp. 179-192. doi: 10.21608/ijaebs.2023.165192.1054
Ali, A., Esekbi, M., Sreh, M. Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs). test, 2023; 4(1): 179-192. doi: 10.21608/ijaebs.2023.165192.1054

Development of International Roughness Index from Pavement Distress Using Artificial Neural Networks (ANNs)

International Journal of Advanced Engineering and Business Sciences
Volume 4, Issue 1 - Serial Number 4, February 2023, Pages 179-192    PDF (1016.8 K)
Document Type: Original Article
DOI: 10.21608/ijaebs.2023.165192.1054
Authors
Abdualmtalab Abdualaziz Ali* 1; Mohamed Imbarek Esekbi2; Muftah Mohamed Sreh3
1Lecturer, Azzaytuna University, Civil Engineering Departement, Tarhuna, Libya
2Professor, Tripoli University, Civil Engineering Departement, Tripoli, Libya
3Lecturer, Elmergib University, Civil Engineering Departement, Al-Khoms, Libya
Abstract
Roughness index forecasts are essential for optimizing pavement rehabilitation and treatment programs. The main objective of this study is to investigate the effect of pavement distress on pavement performance and develop International Roughness Index models (IRI) for dry no freeze regions in the U.S. Data for this research was collected from the Long-Term Pavement Performance (LTPP) database. The data include a total of 138 records of pavement distress with no maintenance and rehabilitation. Based on these data, IRI prediction models were developed using two modelling approaches: Multiple Linear Regression analysis (MLR) and Artificial Neural Networks (ANNs). The proposed models predict the IRI as a function of pavement distress variables such as or including fatigue cracking block cracking, edge cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and ravelling. This study showed that the (ANNs) model yielded a higher prediction accuracy than the (MLR) model.This study showed that the (ANNs) model yielded a higher prediction accuracy than the (MLR) model.
Keywords
Pavement Performance; Artificial Neural Networks (ANNs); Pavement Condition Index (PCI); International roughness index (IRI); Multiple Linear Regression analysis (MLR)
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