Élaboration de règles de conception pour réduire la durée de fabrication des composantes de structures d’acier

Abstract

In the Architecture, Engineering and Construction (AEC) industry, the failure to integrate the knowledge and experience of specialty contractors (such as fabricators) during the design phase contributes to a lot of waste in terms of quality, cost and construction time. The problem stems from the traditional Design Bid Build (DBB) mode of project delivery, which imposes a linear and fragmented delivery, dictated by plans and specifications, and which does not offer trade contractors the opportunity to participate in the design. Initiatives such as Value Engineering (VE) and constructability have been proposed to mitigate this problem. While allowing for some knowledge transfer between designers and specialty contractors, these initiatives have focused primarily on reducing the cost and risk of projects. In this way, knowledge transfer is limited and specialized contractors have little opportunity to intervene during design. In a second phase, initiatives have focused on the DBB procurement mode to make it evolve by favouring the integration of manufacturing knowledge in the design phase. However, the proposed solutions do not really question the opportunity to understand and control processes such as fabrication in order to identify constraints to consider for the design. However, in construction projects with steel structures, the fabrication phase of the components represents 30 to 40% of the overall project cost. In Product Development Engineering (PDE), knowledge transfer is formalized through different design approaches and rules such as Design For Manufacturing and Assembly (DFMA). Through these design rules, the knowledge and experience of manufacturers is available to designers very early in the product development. In this way, designers can anticipate product manufacturing and assembly requirements. An application of design rules in the AEC industry can allow for better integration of knowledge and experience of specialized contractors at the design phase of projects. However, there is very little work proposing the development of design rules for the AEC industry. This thesis aims to contribute to resolving this problem by proposing rules for construction projects with steel structures that allow designers to consider factors that have a strong impact on the fabrication time of steel structure components. To achieve this objective, this thesis proposes to identify fabrication factors that will be used later to formulate design rules. Three methods are proposed to identify these factors: a qualitative method that uses open coding on the contents of Requests For Information (RFIs), a quantitative method that applies Machine Learning (ML) algorithms on Building Information Modeling (BIM) models, and a qualitative method that applies mixed coding on the verbatim of interviews with designers and fabricators of structural steel components. To validate these methods, case studies are conducted at an industrial partner, a leader in the field of structural steel component fabrication in North America. As result, this work proposes 20 fabrication factors, 15 of which are completely new in the literature, 33 design rules, 24 of which are completely new in the literature, and 17 initiatives for better consideration of fabrication factors during the design of steel structure components. This research shows that it is possible to identify steel structure fabrication factors and formulate design rules through analyses of RFIs, BIM models, and interviews with steel structure component designers and manufacturers. The results also show that the design rules formulated in this study are different from the rules already present in the literature and inspired by the manufacturing industry. In perspective, this work proposes to identify fabrication factors and formulate design rules in other industrial contexts of the structural steel construction industry and in other professions of the AIC industry. This thesis also proposes to apply the methods developed in this study with other performance criteria such as transformation, flow, and value, inspired by the TFV theory developed by Koskela (2000).

Date	25 Nov 2021
Original language	French
Awarding Institution	École de technologie supérieure
Supervisor	Daniel Forgues (Supervisor) & Sylvie Doré (Co-supervisor)