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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Soni, Jaykumar
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Pavement health 4.0: a novel AI-enabled PavementVision approach for pavement health monitoring and classification Soni, Jaykumar; Gujar, Rajesh; Malek, Mohammed Shakil
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1163-1171

Abstract

To determine the extent of pavement damage and forms of pavement distress, road pavement conditions must be precisely assessed. As a result, monitoring systems are regarded as an important stage in the maintenance procedure. In recent times, numerous investigations have been carried out to track the condition of pavement and monitor road surfaces. In the undertaken study, we have proposed a novel artificial intelligent (AI) and computer vision-enabled PavementCarevision 4.0 approach to detect and classify pavement health conditions i.e., defects. In this study, a customized pavement-2000 dataset has been designed which contains more than 2,000 images of a variety of pavement defects. In the initial phase, we pre-processed and enhanced pavement images using the customized adjustable linear contrast enhancement methodology. The enhanced pavement image samples were fed to the proposed customized YOLOV8 enabled PavementHealth 4.0 framework for pavement condition detection of a variety of pavement defects such as longitudinal cracks, alligator cracks, transverse cracks, and potholes. The proposed customized YOLOV8 enabled PavementHealth 4.0 framework has achieved an accuracy of 99.20 percent; an receiver operating characteristic (ROC) value of 0.98 and outperformed existing AI-based state-of-the-art methodologies such as pose NET, YOLOv7, YOLOv5, long short-term memory network (LSTM), Mask region-based convolutional neural network (R-CNN), and decision tree.
Analyzing the key factors and perspectives of stakeholders in pavement maintenance Soni, Jaykumar; Gujar, Rajesh; Malek, MohammedShakil S.
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp336-344

Abstract

Road infrastructure is important for societal and economic development; therefore, it is crucial to maintain the durability and safety of the pavements. The present study investigates the domain of pavement maintenance by thoroughly analyzing the factors affecting the quality of pavement considering diverse groups of stakeholders. The study explored various flexible pavement defects (distress factors i.e., potholes, alligator cracks, longitudinal cracks, transverse cracks, hungry surfaces, streaking, shoving, rutting, and raveling). The opinions of stakeholders from various sectors such as public, private, and academia are collected through surveys, interviews, and detailed discussions. The collected data is analyzed using advanced statistical tools such as analysis of variance (ANOVA), post hoc test, criticality index, and Spearman rank correlation, which revealed patterns and correlations between stakeholder views. This study highlights diverse perspectives on pavement distress factors, providing valuable insights into the decision-making process. The findings of this research will help policymakers prioritize pavement maintenance based on the prevailing distresses, highlighting the importance of informed decision-making in pavement maintenance and management practices.
Co-Authors Gujar, Rajesh Malek, Mohammed Shakil Malek, MohammedShakil S.