Reference number : GC-2017-Q4-01959, amendment number : , agreement type : G, recipient legal name : Yu, Jia Yuan (Concordia University)|Yu, Jia Yuan (Université Concordia), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : The goal of this project is to improve the safety, efficiency, and accountability of large construction projects. Such projects are very complex, involving expensive heavy equipment and human resources, and typically very difficult to manage. We propose to design and develop a decision-support system that helps the management through automated safety alerts, performance metrics, and record keeping. The system takes as input data from internet-connected sensors, including cameras, deployed on the construction sites. With the help of domain experts from construction companies and GreenOwl, we develop algorithms for equipment, human, and event detection for use specifically in the construction domain. Our proposed system is a one-of-its-kind application of cutting edge machine learning to the area of construction projects. The record keeping feature of this system will also contribute indirectly to deter safety protocol violations, corruption, and inefficiencies in large-scale construction projects., program purpose french : The goal of this project is to improve the safety, efficiency, and accountability of large construction projects. Such projects are very complex, involving expensive heavy equipment and human resources, and typically very difficult to manage. We propose to design and develop a decision-support system that helps the management through automated safety alerts, performance metrics, and record keeping. The system takes as input data from internet-connected sensors, including cameras, deployed on the construction sites. With the help of domain experts from construction companies and GreenOwl, we develop algorithms for equipment, human, and event detection for use specifically in the construction domain. Our proposed system is a one-of-its-kind application of cutting edge machine learning to the area of construction projects. The record keeping feature of this system will also contribute indirectly to deter safety protocol violations, corruption, and inefficiencies in large-scale construction projects., agreement title english : Advanced system for heightened situational awareness and risk management in construction projects, agreement title french : Advanced system for heightened situational awareness and risk management in construction projects, agreement number : CRDPJ, agreement value : 155999.0, agreement start date : 2018-01-10, 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 2020-2021)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021)., 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