Abstracts – Browse Results
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Hallowell, M R, Hardison, D and Desvignes, M (2016) Information technology and safety: Integrating empirical safety risk data with building information modeling, sensing, and visualization technologies. Construction Innovation, 16(03), 323-47.
Holt, G D (2016) Opposing influences on construction plant and machinery health and safety innovations. Construction Innovation, 16(03), 390-414.
Karimi, H, Taylor, T R B, Goodrum, P M and Srinivasan, C (2016) Quantitative analysis of the impact of craft worker availability on construction project safety performance. Construction Innovation, 16(03), 307-22.
Lee, W and Migliaccio, G C (2016) Physiological cost of concrete construction activities. Construction Innovation, 16(03), 281-306.
Liu, M, Han, S and Lee, S (2016) Tracking-based 3D human skeleton extraction from stereo video camera toward an on-site safety and ergonomic analysis. Construction Innovation, 16(03), 348-67.
Siddula, M, Dai, F, Ye, Y and Fan, J (2016) Classifying construction site photos for roof detection: A machine-learning method towards automated measurement of safety performance on roof sites. Construction Innovation, 16(03), 368-89.
- Type: Journal Article
- Keywords: construction management,construction safety,health and safety,machine learning,image-based methods,roofing industry
- ISBN/ISSN:
- URL: https://doi.org/10.1108/CI-10-2015-0052
- Abstract:
Purpose Roofing is one of the most dangerous jobs in the construction industry. Due to factors such as lack of planning, training and use of precaution, roofing contractors and workers continuously violate the fall protection standards enforced by the US Occupational Safety and Health Administration. A preferable way to alleviate this situation is automating the process of non-compliance checking of safety standards through measurements conducted in site daily accumulated videos and photos. As a key component, the purpose of this paper is to devise a method to detect roofs in site images that is indispensable for such automation process. Design/methodology/approach This method represents roof objects through image segmentation and visual feature extraction. The visual features include colour, texture, compactness, contrast and the presence of roof corner. A classification algorithm is selected to use the derived representation for statistical learning and detection. Findings The experiments led to detection accuracy of 97.50 per cent, with over 15 per cent improvement in comparison to conventional classifiers, signifying the effectiveness of the proposed method. Research limitations/implications This study did not test on images of roofs in the following conditions: roofs initially built without apparent appearance (e.g. structural roof framing completed and undergoing the sheathing process) and flat, barrel and dome roofs. From a standpoint of construction safety, while the present work is vital, coupling with semantic representation and analysis is still needed to allow for risk analysis of fall violations on roof sites. Originality/value This study is the first to address roof detection in site images. Its findings provide a basis to enable semantic representation of roof site objects of interests (e.g. co-existence and correlation among roof site, roofer, guardrail and personal fall arrest system) that is needed to automate the non-compliance checking of safety standards on roof sites.
Teizer, J (2016) Right-time vs real-time pro-active construction safety and health system architecture. Construction Innovation, 16(03), 253-80.