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Predicting pavement condition index based on the utilization of machine learning techniques:A case study

道路工程学报(英文) 2023年第3期3卷 266-278页

作者： abdualmtalab abdualaziz ali Abdalrhman Milad Amgad Hussein Nur Izzi Md Yusoff Usama Heneash Department of Civil Engineering Faculty of Engineering and Applied ScienceMemorial UniversitySt.John'sNL A1B1T5Canada Department of Civil Engineering Faculty of EngineeringAzzaytuna UniversityTarhunaLibya Department of Civil and Environmental Engineering College of EngineeringUniversity of NizwaNizwaOman Department of Civil Engineering Universiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Civil Engineering Faculty of EngineeringKafrelsheikh UniversityKafr El-SheikhEgypt

Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable *** accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate *** pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's *** research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement *** authors collected field data spans for 2018 and *** pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and *** study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.

关键词： Pavement condition index Pavement distresses Machine learning Artificial neural network Multiple linear regression

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