Reference number : GC-2017-Q1-02286, amendment number : , agreement type : G, recipient legal name : Ruwanpura, Janaka (University of Calgary), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Construction project progress monitoring and structural dimensional compliance control are essential to allow decision makers to identify discrepancies between the planned and the as-built states of a project, and take timely measures where required. In practice, monitoring is performed manually, a time consuming, error-prone and labour intensive task, particularly in large scale projects. To reduce the time and cost of manual monitoring, limited onsite data is collected, restricting the project management team to identify the causes of delays, cost overruns, and rework on time. On average, the direct cost of rework is 5% of the total construction cost, mainly caused by errors during construction. In addition, the correct determination of the project&rsquo;s performance relies heavily on the accuracy of the collected data during the monitoring process. Currently, site supervisory personnel spend 30-50% of their time just to manually inspect and control the quality of the manually accumulated onsite data. To improve the quality of the collected data, current research has aimed to use remote sensing technologies such as LiDAR and cameras to acquire 3D point clouds of the construction element; however, recent research studies use the planned model as a priori knowledge to assign the accumulated point clouds to their corresponding structural element, which provide inaccurate results when the planned and the actual differ and /or the planned model is not available with sufficient detail. Therefore, in the PI&rsquo;s previous work as a part of the NSERC Discovery grant, an automated as-built model generation system, independent from the details of the planned model, was developed. Although this system addresses some of the main limitations of the current literature, three main concerns must be solved in order to provide a comprehensive solution to automating the monitoring and control process: 1) automatic real-time data acquisition; 2) stochastic prediction of project outcomes and decision support solution to meet project constraints; and 3) holographic visualisation of the project outcomes and data validity control. This proposal expects to overcome these limitations through the design and implementation of three interrelated projects. This research program aligns with the &ldquo;New Earth Space Technologies&rdquo; research strategy of the University of Calgary launched in October 2016, specifically targeting 1 of 5 identified research challenges, the Geospatial Communication. The efficient implementation of this system can benefit the construction industry by: 1) improving the time, cost and quality of data collection and analysis; 2) timely identification of rework and delays; and 3) minimizing construction claims and disputes., program purpose french : Construction project progress monitoring and structural dimensional compliance control are essential to allow decision makers to identify discrepancies between the planned and the as-built states of a project, and take timely measures where required. In practice, monitoring is performed manually, a time consuming, error-prone and labour intensive task, particularly in large scale projects. To reduce the time and cost of manual monitoring, limited onsite data is collected, restricting the project management team to identify the causes of delays, cost overruns, and rework on time. On average, the direct cost of rework is 5% of the total construction cost, mainly caused by errors during construction. In addition, the correct determination of the project&rsquo;s performance relies heavily on the accuracy of the collected data during the monitoring process. Currently, site supervisory personnel spend 30-50% of their time just to manually inspect and control the quality of the manually accumulated onsite data. To improve the quality of the collected data, current research has aimed to use remote sensing technologies such as LiDAR and cameras to acquire 3D point clouds of the construction element; however, recent research studies use the planned model as a priori knowledge to assign the accumulated point clouds to their corresponding structural element, which provide inaccurate results when the planned and the actual differ and /or the planned model is not available with sufficient detail. Therefore, in the PI&rsquo;s previous work as a part of the NSERC Discovery grant, an automated as-built model generation system, independent from the details of the planned model, was developed. Although this system addresses some of the main limitations of the current literature, three main concerns must be solved in order to provide a comprehensive solution to automating the monitoring and control process: 1) automatic real-time data acquisition; 2) stochastic prediction of project outcomes and decision support solution to meet project constraints; and 3) holographic visualisation of the project outcomes and data validity control. This proposal expects to overcome these limitations through the design and implementation of three interrelated projects. This research program aligns with the &ldquo;New Earth Space Technologies&rdquo; research strategy of the University of Calgary launched in October 2016, specifically targeting 1 of 5 identified research challenges, the Geospatial Communication. The efficient implementation of this system can benefit the construction industry by: 1) improving the time, cost and quality of data collection and analysis; 2) timely identification of rework and delays; and 3) minimizing construction claims and disputes., agreement title english : An Automated Monitoring and Control System for Construction Projects using Advanced Technologies, agreement title french : An Automated Monitoring and Control System for Construction Projects using Advanced Technologies, agreement number : RGPIN, agreement value : 205000.0, 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