Abstract
Integrating the knowledge and experience of fabrication during the design phase can help reduce the cost and duration of steel construction projects. Building Information Modeling (BIM) are technologies and processes that reduce the cost and duration of construction projects by integrating parametric digital models as support of information. These models can contain information about the performance of previous projects and allow a classification by linear regression of design criteria with a high impact on the duration of the fabrication. This paper proposes a quantitative approach that applies linear regressions on previous projects’ BIM models to identify some design rules and production improvement points. A case study applied on 55,444 BIM models of steel joists validates this approach. This case study shows that the camber, the weight of the structure, and its reinforced elements greatly influence the fabrication time of the joists. The approach developed in this article is a practical case where machine learning and BIM models are used rather than interviews with professionals to identify knowledge related to a given steel structure fabrication system.
Highlights
Introduction in Steel Construction ProjectsThe fabrication phase of structural steel projects represents 30 to 40% of the overall building cost [1]
This paper addresses the research question: Is it possible to identify design rules such as design for manufacturing and assembly (DFMA) from Building Information Modeling (BIM) models of previous projects and machine learning algorithms? As an answer to this question, this paper aims to propose an approach to identify design rules from BIM models of previous projects and ML algorithms: To achieve this goal, this paper proposes to validate the possibility of extracting design factors from BIM models of steel structures and ML, the possibility of establishing some design rules to reduce the fabrication time from the obtained design factors
The Gap between predicted and real-time (GBP) obtained from the prediction results with the Lasso algorithm range from
Summary
Introduction in Steel Construction ProjectsThe fabrication phase of structural steel projects represents 30 to 40% of the overall building cost [1]. A few minutes to a few hours are devoted to evaluating models for cost and time reduction and the search for alternative solutions during the design phase [5] These evaluations are made without considering the particularities of the manufacturing plant where the work will be carried out [6]. In the traditional Design Bid Build (DBB) procurement, where a project is carried out in a linear and fragmented process, manufacturing specialists often intervene at the end of the design phase [10,11] At this moment, the modifications they make cause delays and additional costs in completing the projects [12]. This situation is similar to the situation in Product Development Engineering (PDE) in the 1990s
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