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.

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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.

For decades, many researchers have reported the decline in construction productivity, but it is impossible to generalize the productivity problems due to issues such as geographical differences, weather changes, skill level differences in the labour force, and workload demand. There are currently approximately 160 000 construction workers in Alberta, and the value of ongoing and future projects in the oil and gas sector alone is over Can$200 billion. Because of the high demand and necessity for a comprehensive research study for Alberta to improve construction productivity, a 2 year study commenced with the collaboration of four eminent contractors to investigate human, management, and external issues. Under human issues, 101 construction workers in the carpentry trade were observed, interviewed, and surveyed by questionnaire. This paper discusses the research method, results, and recommendations of the productivity research specific to the human issues. These recommendations were communicated to the contractors for improving the human issues by developing and analyzing a new expectancy model considering construction workers' effort and performance and the tool times observed to mitigate the reasons for non-tool time.Résumé : Les chercheurs ont fait état, depuis plusieurs décennies, de la baisse de productivité en construction. Cependant, les problèmes de productivité ne peuvent pas être généralisés en raison des questions telles que les différences géographiques, les changements de température, les différences dans les niveaux de compétence de la main-d'oeuvre, les charges de travail, etc. L'Alberta compte environ 160 000 travailleurs de la construction et des projets en développe-ment pour une valeur de 200 milliards de dollars. En raison de la forte demande et de la nécessité d'avoir une étude de recherche complète pour que l'Alberta puisse améliorer la productivité en construction, il a été recommandé d'entreprendre, avec la collaboration de quatre grands entrepreneurs, une étude de deux ans afin d'examiner les questions humaines, externes et de gestion. Pour ce qui est des questions humaines, 101 charpentiers ont été observés, interviewés et ont eu à répondre à un sondage. Cet article aborde la méthode de recherche, les résultats et les recommandations de la recherche sur la productivité spécifiques aux questions humaines. Ces recommandations ont été communiquées aux entrepreneurs afin d'améliorer les aspects humains en développant et en analysant un nouveau modèle d'attentes qui tient compte de l'effort et du rendement des travailleurs de la construction, ainsi que des temps réels de travail observés afin d'atténuer les raisons des temps non consacrés au travail.

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Green buildings help in sustainability, in terms of achieving energy efficiency and minimizing the utilization of natural resources. Additional benefits include long-term sustainable building management and maintenance. There is, therefore, compelling motivation for the building of sustainable projects. This inspiration has led to the development of the leadership in energy and environmental design (LEED) rating systems and projects by the United States Green Building Council and the Canadian Green Building Council. Proper building project management (BPM) of such projects is warranted. Pre-project planning is a crucial part of BPM that ensures delivery and performance of construction projects. Pre-project planning is defined as the process that encompasses all the tasks between project initiation and detailed design. There is a positive relationship between comprehensive pre-project planning and enhanced project performance. Given the motivation for environmentally sustainable projects and proper management of constructing such projects, diligent pre-project planning for such projects is required. This paper investigates the use of the LEED rating system in pre-project planning of sustainable construction projects by developing a matrix that combines the LEED and the Project Definition Rating Index (PDRI) developed by the Construction Industry Institute. The conceptual matrix and its application to a case study demonstrates that the value of linking pre-project planning with LEED to improve the decision making process during planning and designing of building projects to improve sustainability.

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