Reference number : GC-2017-Q1-01963, amendment number : , agreement type : G, recipient legal name : Liu, Zheng (The University of British Columbia), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The condition of buried infrastructure including water, wastewater, and storm water networks is of great importance to people&rsquo;s quality of life and has strong environmental, economic and social implications. A recent municipal buried infrastructure survey across Canada identified water pollution, safety and state of water supply systems as the greatest issues to this country. City water leaks are wasting millions of tax dollars. Utilities are addressing these issues primarily with a reactive approach. A performance-based approach is needed to identify if individual water pipelines meet the required level of service. However, performance-based management of these buried assets has historically not been performed due to technical and financial limitations. A proactive, uniform, and systematic approach for maintenance of buried infrastructure would be more efficient and preferred. 
 
To achieve the proactive maintenance of buried infrastructure, it is essential to collect all the relevant data and information from the pipelines to determine future pipe condition. Currently, in-situ monitoring as well as non-destructive inspection are performed to collect the data. However, these data are saved in different formats and stored in varied places. As these data are not properly matched and aligned to each other, predictive models can only be built with isolated data. A comprehensive prediction based on accumulated inspection data can be used to develop next-generation proactive maintenance. This research program will fill the technical gaps by incorporating modern information and communications technologies, e.g. Internet of Things, cloud computing, and advanced machine learning, into the buried infrastructure management. 
 
The long-term objective of the research program is to advance condition assessment technologies and predictive analytics to achieve the sustainability of buried infrastructure through proactive maintenance. In the short-term, the research program will develop a cloud-based sensing framework, which will enable centralized data collection, storage, and management, and develop predictive analytics to derive actionable information from big inspection data. 
 
Proactive maintenance allows preventive and corrective actions from water utilities to be properly and efficiently scheduled. Thus, the impacts brought to Canada&rsquo;s buried infrastructure include higher system reliability, reduced costs of pipe failures, minimized time on maintenance, and optimized maintenance interval. The proposed research will also offer a unique cross-disciplinary training opportunity for HQP. The HQP will gain in-depth knowledge and understanding of more than one discipline, e.g. electrical, civil, and mechanical engineering, and be able to solve practical problems without regard to disciplinary boundaries., program purpose french : The condition of buried infrastructure including water, wastewater, and storm water networks is of great importance to people&rsquo;s quality of life and has strong environmental, economic and social implications. A recent municipal buried infrastructure survey across Canada identified water pollution, safety and state of water supply systems as the greatest issues to this country. City water leaks are wasting millions of tax dollars. Utilities are addressing these issues primarily with a reactive approach. A performance-based approach is needed to identify if individual water pipelines meet the required level of service. However, performance-based management of these buried assets has historically not been performed due to technical and financial limitations. A proactive, uniform, and systematic approach for maintenance of buried infrastructure would be more efficient and preferred. 
 
To achieve the proactive maintenance of buried infrastructure, it is essential to collect all the relevant data and information from the pipelines to determine future pipe condition. Currently, in-situ monitoring as well as non-destructive inspection are performed to collect the data. However, these data are saved in different formats and stored in varied places. As these data are not properly matched and aligned to each other, predictive models can only be built with isolated data. A comprehensive prediction based on accumulated inspection data can be used to develop next-generation proactive maintenance. This research program will fill the technical gaps by incorporating modern information and communications technologies, e.g. Internet of Things, cloud computing, and advanced machine learning, into the buried infrastructure management. 
 
The long-term objective of the research program is to advance condition assessment technologies and predictive analytics to achieve the sustainability of buried infrastructure through proactive maintenance. In the short-term, the research program will develop a cloud-based sensing framework, which will enable centralized data collection, storage, and management, and develop predictive analytics to derive actionable information from big inspection data. 
 
Proactive maintenance allows preventive and corrective actions from water utilities to be properly and efficiently scheduled. Thus, the impacts brought to Canada&rsquo;s buried infrastructure include higher system reliability, reduced costs of pipe failures, minimized time on maintenance, and optimized maintenance interval. The proposed research will also offer a unique cross-disciplinary training opportunity for HQP. The HQP will gain in-depth knowledge and understanding of more than one discipline, e.g. electrical, civil, and mechanical engineering, and be able to solve practical problems without regard to disciplinary boundaries., agreement title english : Towards proactive maintenance of buried infrastructure with cloud-based sensing and predictive analytics, agreement title french : Towards proactive maintenance of buried infrastructure with cloud-based sensing and predictive analytics, agreement number : RGPIN, agreement value : 105000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada