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Bashouri, J and William, D G (2014) A model for sharing knowledge in architectural firms. Construction Innovation, 14(02), 168-85.

Chen, Y, Dib, H and Cox, R F (2014) A measurement model of building information modelling maturity. Construction Innovation, 14(02), 186-209.

Hughes, R and Thorpe, D (2014) A review of enabling factors in construction industry productivity in an Australian environment. Construction Innovation, 14(02), 210-28.

Mohammed, A A and Abdul-Rahman, H (2014) Aspects of project learning in construction: A socio-technical model. Construction Innovation, 14(02), 229-44.

Teriö, O, Sorri, J, Kähkönen, K and Hämäläinen, J (2014) Environmental index for Finnish construction sites. Construction Innovation, 14(02), 245-62.

Teriö, O, Sorri, J, Kähkönen, K and Hämäläinen, J (2014) Environmental index for Finnish construction sites. Construction Innovation, 14(02), 245-62.

Turkan, Y, Bosché, F, Haas, C T and Haas, R (2014) Tracking of secondary and temporary objects in structural concrete work. Construction Innovation, 14(02), 145-67.

  • Type: Journal Article
  • Keywords: BIM,construction progress tracking,laser scanning,object recognition,secondary objects,temporary objects
  • ISBN/ISSN:
  • URL: https://doi.org/10.1108/CI-12-2012-0063
  • Abstract:
    Purpose - Previous research has shown that “Scan-vs-BIM” object recognition systems, which fuse three dimensional (3D) point clouds from terrestrial laser scanning (TLS) or digital photogrammetry with 4D project building information models (BIM), provide valuable information for tracking construction works. However, until now, the potential of these systems has been demonstrated for tracking progress of permanent structural works only; no work has been reported yet on tracking secondary or temporary structures. For structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. Together, they constitute most of the earned value in concrete work. The impact of tracking secondary and temporary objects would thus be added veracity and detail to earned value calculations, and subsequently better project control and performance. The paper aims to discuss these issues. Design/methodology/approach - Two techniques for recognizing concrete construction secondary and temporary objects in TLS point clouds are implemented and tested using real-life data collected from a reinforced concrete building construction site. Both techniques represent significant innovative extensions of existing “Scan-vs-BIM” object recognition frameworks. Findings - The experimental results show that it is feasible to recognise secondary and temporary objects in TLS point clouds with good accuracy using the two novel techniques; but it is envisaged that superior results could be achieved by using additional cues such as colour and 3D edge information. Originality/value - This article makes valuable contributions to the problem of detecting and tracking secondary and temporary objects in 3D point clouds. The power of Scan-vs-BIM object recognition approaches to address this problem is demonstrated, but their limitations are also highlighted.