This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Developing and constructing oil sand megaprojects are huge technological, engineering, and construction achievements for Alberta and Canada. Thousands of engineering and project management professionals and workers are contributing to the current effort with great pride and sense of satisfaction. But recent large capital oil sands construction projects have all experienced significant cost and schedule overruns. There are a number of reasons for these overruns. This paper provides a definition of a “megaproject,” illustrates the challenges in delivering oil sands megaprojects, and lists a number of reasons for cost and schedule overruns.

The expansion and advancement of businesses, cities, municipalities, and populations in Canada are increasingly demanding the effective usage of land and resources in building development. Companies, organizations, and individuals are seeking to construct more environmentally responsible buildings to help minimize the negative effects that structures place on the natural environment, as well as on the people that have either direct or indirect contact with them. In an effort to set a benchmark for what constitutes a green building, the Canada Green Building Council provides a certification system called Leadership in Energy and Environmental Design (LEED), which is a point scheme that seeks to measure sustainability. Many stakeholders in the construction industry in Canada are increasingly employing this system to validate their achievement in sustainable development. This paper reviews the usage of the LEED system in Canadian building construction and the potential challenges and barriers associated with...

The image processing based human recognition is yet a challenging task because of series of complications such as variations in pose, lighting conditions and complexity of background in the tracking environment. This study introduces a novel methodology to track construction workers using image processing techniques and depth information generated from the Microsoft Kinect sensor. Kinect is a new game controller technology introduced by Microsoft in November 2010. This automated real-time worker tracking system provides an opportunity to track the construction worker location and their movements in a specified indoor work area. The research study proposes a properly color coded "construction hardhat" as a key tracking object which can be used to differentiate site personnel (worker, supervisor, engineer, etc.). The proposed method detects construction workers in three major stages which includes human recognition, hardhat recognition and 3D localization. The human recognition is done by analysing human body parts. 3D positions of body joints are accurately predicted from a single depth image. The construction hardhat detection is based on characteristics of the hardhat such as unique shape and color. Template based template matching is used as the pattern recognition technique.