Development of a time-synchronised multi-input computer vision system for structural monitioring utilising deep learning for vehicle identification

A reliable transport infrastructure is vital to the commercial and lifestyle demands of a developed country, with the majority of journeys occurring by road. Bridges are a key component of this infrastructure, if a bridge fails or is unnecessarily closed it has widespread adverse effects throughout the surrounding area. Detailed monitoring is essential to ensure adequate maintenance of these structures is carried out, this is not currently the case as many bridges are only sporadically checked by visual inspection, often by a junior engineer. Structural Health Monitoring (SHM) has been developed to counteract this shortfall, to date the instrumentation used has primarily been contact based and usually requires bridge closure. Computer Vision is the process of using cameras to obtain data from images, this method is now being applied to monitor civil structures worldwide. With regards to the monitoring of bridge displacement from applied vehicle load, the primary focus of existing research has been on single camera studies to monitor one point on the bridge without a means of identifying the cause of the measured displacement. The work presented in this thesis details the development of an accurate, time synchronised multiple camera solution for the monitoring of bridge displacement. The system has been validated for accuracy in numerous laboratory and field trials against a diverse array of instrumentation and under a variety of environmental conditions. To facilitate load identification from vehicles, a Deep learning based method for Vehicle Identification has also been developed in the course of the work presented. The load identification solution is capable of precise location and fine-grained classification of vehicles from images captured in millisecond level synchronisation with captured displacement readings. This composite system has been successfully verified in a field trial, and with further development including incorporation of a weights database for approximate load calculation can provide the basis of a total system for bridge displacement monitoring.

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

Queen's University Belfast

Year dc:date.issued

2020

Author dc:creator

• Lydon, Darragh

Advisors dc:contributor.advisor

• Taylor, Susan
• Martinez del Rincon, Jesus
• Hester, David

• Computer Vision
• Deep Learning
• Structural Health Monitoring

Language dc:language

eng

Identifier

oai:pure.qub.ac.uk/portal:studenttheses/ab45f7cc-aebb-4eef-ade0-5bc07e9c3b40

OAI identifier oai:identifier

oai:pure.qub.ac.uk/portal:studenttheses/ab45f7cc-aebb-4eef-ade0-5bc07e9c3b40