Successful project execution requires up-to-date and accurate knowledge of progress of activities on construction projects. Frequent progress information is, hence, eminently desirable to support decision makers and stakeholders identify possible schedule delays early in the project life cycle and take corrective measures when required. Continuous collection of point clouds and images from construction projects, especially when automated, can support the accurate and frequent reporting of construction progress. Hence, automating the construction progress reporting, requires automation of both the on-site data collection as well as the point cloud analysis. The image below provides the overview of the automated framework, proposed in Maalek et al. (2019), for reporting the progress of reinforced concrete structures using point clouds.
With the existence of a detailed 4-dimensional (4D) building information model (BIM), progress can be determined with respect to planned schedule baselines. In this regards, traditional project control methods such as earned value management (EVM) may be employed to estimate the overall performance of the project. In the absence of a 4D BIM (e.g., in specialized sub-contract work) it is possible to perform change detection between consecutive point clouds and/or images of the same scene to determine possible newly built objects. In the latter case, the accuracy of the point clouds processing becomes even more pronounced. An example of an autonomous framework for reporting the progress of mechanical works using smartphones is provided in Maalek et al. (2021), see the video below.