Next-generation Indirect Bridge Health Monitoring Technology

点击翻译按钮获取中文摘要

Bridge infrastructure in North America is ageing and enduring increasingly adverse weather conditions. The recent infrastructure report card indicated that one-third of America's infrastructure is at risk of rapid deterioration, with ~240,000 bridges at least 50 years old. On average, 188 million daily trips occur across these structurally deficient bridges, which requires rehabilitation worth ~$123 billion. Traditional bridge health monitoring (BHM) involves the costly and time-consuming installation of contact sensors. One attractive alternative is indirect BHM (iBHM), where sensors are installed in a passing vehicle, enabling mobile bridge inspection. However, existing iBHM has several challenges, such as sensor-vehicle-bridge interaction, lack of guidelines for vehicle configuration, the need for a large image database, and inadequate visualization of bridge health. The long-term objective of the proposed research is to develop a robust iBHM technology that will be free of any physical instrumentation on the bridge and a large image database to achieve cost-effective bridge inspection and maintenance. The long-term objective will be achieved through the following five short-term objectives organized in three pressing themes. Theme 1 will develop global damage detection technology using mobile sensors by i) developing robust diagnostic algorithms to solve the challenges of vehicle-sensor-bridge interaction in vehicle measurements and ii) developing the optimal configuration of the test vehicle. Theme 2 will produce robust local damage assessment technology using a fewer training database through iii) a suite of novel generative adversarial networks and iv) data fusion technology by augmenting images and videos. Theme 3 will develop a novel visualization technology to track the progression of damage and optimize inspection frequency using v) a novel bridge information modelling. This proposal will, for the first time, develop guidelines for the test vehicle and its operation, which are required for accurate and autonomous iBHM. The proposed data fusion technology and vision-based multi-damage classifier will be free of the requirement for a massive number of images, resulting in cost-effective iBHM systems for thousands of impaired bridges in America. In consultation with technical committees of the American Society of Civil Engineers (ASCE), the Canadian Society for Civil Engineering (CSCE), the Ministry of Transportation of Ontario (MTO) and the Professional Engineers of Ontario (PEO), the outcome of the proposed research will result in BHM codes to provide best practices and guidelines for bridge inspection in North America. A diverse team of 3 PhD, 2 MESc, and 5 BESc students will be trained to contribute in this emerging BHM technology and promote EDI in the engineering community. It is anticipated that this research will lead to safe transport infrastructure for our citizens and billions of dollars of cost savings for governments.