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A hybrid approach to condition assessment of paved roads

dc.contributor.advisorAzar, Ehsan Rezazadeh
dc.contributor.authorSprague, William
dc.date.accessioned2021-09-27T14:42:44Z
dc.date.available2021-09-27T14:42:44Z
dc.date.created2021
dc.date.issued2021
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/4858
dc.description.abstractRoad reconstruction is drastically more expensive than an appropriate maintenance program. Performing targeted maintenance requires a comprehensive inventory of road conditions. To create and maintain a road condition inventory, many cities use expensive, purpose-built road profiling vehicles. With the abundance of smartphones, and the variety of sensors they hold, there is an opportunity to collect road condition data more pervasively and at a lower cost. This study shows that smartphones can be used to collect road surface data for the purpose of creating an inventory of road conditions. The data sources within the phone are its onboard accelerometer and camera. Readings from these sensors are taken simultaneously while the phone is mounted to the windshield of a vehicle driving over paved city streets. The collected data was used for measuring road surface roughness and automatically identifying specific defects, such as cracks and potholes. Road roughness is quantified using the range between acceleration values and pairing them with readings from the smartphone’s GPS antenna to create roughness intensity maps. Defect identification is done by a convolutional neural network that can label the pixels of an image it receives into one of several defect pixel label classes. The neural networks used for this study were trained using a manually labelled image set of several hundred images. The novel hybrid approach used in this study combines the accelerometer data with the camera footage. The data are synchronized so that frames can be extracted from the video footage based on anomalies in the acceleration data. Peaks in the acceleration data are used as a trigger to extract and analyze images from the video footage. This approach to defect identification can be done at a much lower computational burden than if all the video footage was processed by a convolutional neural network. The defect identification results show strong performance in terms of identifying locations of defects, as determined by the acceleration data, and identifying the defects at the identified locations themselves. Our study found that an unmodified vehicle with a smartphone mounted to its windshield can generate data that our system, developed in MATLAB, can find over 60% of road defects. This is achieved in just one pass over the road surface.en_US
dc.language.isoen_USen_US
dc.subjectRoad structures & surfacesen_US
dc.subjectRoad condition dataen_US
dc.subjectSmartphone-based vehicle telematicsen_US
dc.subjectData collection (smartphones)en_US
dc.subjectPavement management systemsen_US
dc.subjectRoad infrastructureen_US
dc.titleA hybrid approach to condition assessment of paved roadsen_US
dc.typeThesisen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineEngineering : Civilen_US
etd.degree.grantorLakehead Universityen_US


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