Qualitative analysis of Request For Information to identify design flaws in steel construction projects

Request for information (RFI) is a formal process used in the Architecture, Engineering and Construction industry to address design flaws that affect communication between designers and contractors. A large number of RFIs are a sign of a lack of precision or coordination in the design documents. However, RFIs produce rich, precise, and structured information. Analyzing their content can help to identify recurring problems between designers and construction teams and better tailor future projects to the working context of the contractors. This article presents a method for identifying recurring issues during the design phase of steel construction projects through the analysis of the contents of RFIs. It is original in using a qualitative content analysis tool that can analyze large quantities of RFIs rapidly. Identifying the recurrent problems of contractors will allow the establishment of rules to be taken into consideration during the design phase of future steel construction projects. A case study of 26 steel construction projects demonstrates the feasibility of this method. This case study shows that, given the same designers and construction teams, recurring problems shown in RFIs do not differ according to the scale of the projects. In this case, the main issue between designers and contractors is the lack and inadequate presentation of information related to the connection of steel components. Identifying these problems can pave the way for initiatives to improve the design phase and can be an essential step in making contractors’ knowledge available to designers early in the projects

Bim Machine Learning and Design Rules to Improve the Assembly Time in Steel Construction Projects

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