Image Based Pavement Condition Assessment
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Date
2022-08
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Addis Ababa University
Abstract
For the purpose of developing and implementing pavement management plans, it is the duty of all road authorities to gather pavement distress data from their network of roads. The Addis Ababa Road Authority invests a considerable sum of money in paving upkeep each year. The major goal of this thesis is to suggest a creative, sound method for evaluating the quality of asphalt pavement in order to maintain it effectively using a semi-automated system.
This study makes use of video data that was gathered from a camera mounted on a moving car. Pavement photos were collected from Addis Ababa’s pavement road segments that were chosen at random. Images of pavement defects such as cracks, potholes, pitching, raveling, and rutting were imported straight into an image-j program for analysis. The analysis's findings are also utilized to calculate the pavement condition index (PCI). The pavement condition has been assessed in this study in terms of the surface distress present at the time of the field evaluation. Because it covers the topic of pavement distress identification the most thoroughly and is based on a reliable statistical technique of pavement samples, the PCI procedure has been employed in this case.
Based on the PCI of the road sections, the ASTM D 6433 condition rating process was applied to the evaluation. The research's findings show that the maximum PCI value for 10 sections of asphalt-damaged pavement is 98.5, indicating that the PCI curve is in its typical position for recently constructed pavement. The minimum value of PCI is 20, which indicates a very serious severity level. It needs reconstruction. Each pavement section's PCI values may have been used to determine the priority of its upkeep. Due to Ethiopia's restricted maintenance fund availability, prompt and logical determination of maintenance and rehabilitation are required. The study also states that it is crucial to prioritize repairing deteriorated pavement components.
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Keywords
Condition assessment, maintenance, pavement priority, pavement condition index, rehabilitation.