The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-5/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-5/W1-2022, 205–210, 2022
https://doi.org/10.5194/isprs-archives-XLVI-5-W1-2022-205-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-5/W1-2022, 205–210, 2022
https://doi.org/10.5194/isprs-archives-XLVI-5-W1-2022-205-2022

  03 Feb 2022

03 Feb 2022

CLASSIFICATION OF CONSTRUCTION FIRMS BASED ON BIM ROLES AND BIM LEVELS USING MACHINE LEARNING TECHNIQUES

H. Sadeh, C. Mirarchi, and A. Pavan H. Sadeh et al.
  • Dept. of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Milan, Italy

Keywords: BIM Roles, BIM Levels, Machine Learning, BIM Education, Construction Management

Abstract. Application of Building Information Modelling (BIM) within the AEC industry has been evolving. With new developments and increasing capabilities, BIM is reshaping the design, construction, and operation, and maintenance processes and revolutionizing the entire functions of building life cycles. To maximize BIM benefits and take advantage of its capabilities, it is imperative that project stakeholders define specific roles and responsibilities within projects; to employ professionals with high levels of BIM proficiency, expertise, and knowledge. This study aims to classify the construction firms into different clusters based on their BIM capabilities, implementation, BIM levels, and type of BIM roles they employ for construction projects. It will further predict and classify BIM levels at company level according to its usage. The methodology was based on a survey design which consisted of application an online questionnaire that was distributed to AEC professionals in the industry. 61 suitable responses were analysed, using different supervised and unsupervised machine learning algorithms, including Cluster Analysis, K-Nearest Neighbours algorithm (k-NN), Random Forest, and Gradient Boosting. The findings showed most firms were not applying BIM on their projects and the majority of those that did were not utilizing it in its full potential. Firms were further classified in terms of BIM levels and types of BIM applications they utilize on construction projects. The results showed that Random Forest had the highest performance and the most accuracy, comparing with KNN and Gradient Boosting, even though the performance and predictions results produced by all models were in proximity of one another.