Automated data collection and machine learning methods for civil infrastructure condition assessment in sparsely inhabited regions of Canada

点击翻译按钮获取中文摘要

Canada is the second largest country by total area, but it has one of the lowest population densities in the world. Approximately two-thirds of the population resides within 100 kilometers of the county's southern border, which accounts for only 4% of Canada's total territory. Despite this uneven distribution of the population, vast networks of civil infrastructure are spread throughout the sparsely inhabited regions of Canada. These assets are critical for equitable access and social and economical prosperity of Indigenous and local communities, and many farming and industrial operations depend on their reliable performance. Many research efforts have developed methods for diagnostics and management of the civil infrastructure, but the majority of these projects focused on major assets that exist in densely populated regions. For example, most of the research in the area of road condition assessment developed methods for assessment of paved roads. Unpaved roads, however, have a major role in transportation and service activities in the remote regions of Canada, where about 60% of the country's road network is unpaved. In addition to the roads, many vital infrastructure assets, such as hydraulic structures, are scattered in the remote regions of Canada. Many of these assets were built decades ago and a considerable portion of them are rated in fair or below fair levels by the Canadian Infrastructure Report Card. Conventional infrastructure condition assessment methods are labour-intensive and expensive, and these issues are more problematic in remote regions due to the shortage of experts and the long distances that need to be covered. This research program aims at developing methods to enhance the automation level of condition assessment in remote regions and data analysis by the application of advanced data collection and machine learning methods. The main objectives of this research plan can be summarized as: 1) bridge the gap between the state-of-the-art computer vision and machine learning algorithms and practical applications in the field of infrastructure condition assessment; 2) data-centric management of the infrastructure assets in the remote regions of Canada. The proposed research program consists of several research projects and each one will investigate a specific and related topic in this area. This research program will potentially transform condition assessment of infrastructure assets from conventional to data-driven practices with high level of automation, leading to improvements in efficiency and accuracy of the condition assessment processes. In addition, the developed methods will facilitate the contribution of local communities to the management of infrastructure and reduce the need for outsourcing condition assessment efforts. The increased automation level of the condition assessments in remote regions will result in cost savings and improvement in the serviceability of the infrastructure in the rural regions of Canada.