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Title: Digital images for road surface monitoring
Author: Adam, Sarhat M.
ISNI:       0000 0004 7959 7769
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2015
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Road networks are regarded as probably the most-important infrastructure in modern day travel. Regular assessment of their condition is necessary for implementing proper maintenance and minimizing the cost. For example, early crack detection and maintenance has proved to be an effective technique of prolonging the age of roads and maintaining safe travel conditions. Although, detection of the cracks has been the aim of a number of researches in recent years, many challenges still exist. For example, crack detection in rough texture surfaces needs more attention and investigation, as rough texture can make crack detection difficult in a digital image. In addition, the pavement surface texture can change rapidly within a few metres which affects the ride quality, skid resistance, and road safety. Existing traditional surface texture measurement techniques such as the Sand Patch Test (SPT) tend to be time consuming and of variable quality. Whereas, modern methods which include Mobile Laser Scanning Sensors (MLSS) can provide accurate results but are often regarded as too expensive. In this study an alternative method for measuring pavement surface texture will be investigated, with the aim of testing the potential of digital images for road surface monitoring and pavement evaluation. This method is based on the rapid advances in the field of image processing and image-based 3D modelling. This research is aimed to investigate the possibility of identifying road surface distresses due to cracks and potholes, as well as characterising surface texture depth. This project is split into two parts, the first part is to investigate or examine the use of digital images in video mode for identifying the cracks and potholes. In the second part, the project will concentrate on using digital still images for characterising the road surface texture in order to extract relevant safety parameters such as texture depth. This research showed that it is possible to measure the texture depth from digital images using different cameras with comparable results to SPT. A good accuracy and high correlation with SPT were achieved even with smartphone cameras. It has also been shown that it is possible to assemble a system with cost effective tools such as camera and hand-held GPS. Then, through algorithm development, it was possible to detect important road damages such as cracks and potholes with good accuracy when compared with measured trust data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TA Engineering (General). Civil engineering (General)