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.

Modeling and Optimization of Supplier Selection Process for Decreasing Total Cost of Projects

There are significant factors affecting the decisions about supplier selection for procurement in steel construction firms: parts prices and transportation costs, payment periods, parts lengths and considering penalty terms coming from firm’s clients. Therefore, in order to select optimal firms, each proposal must be considered on equal terms. As a result, ‘Net Present Value’ should be calculated in evaluating payment period, lead time, parts lengths and also transportation costs payment period. This evaluation may bring about decrease in costs of parts based on older procurements. For an optimal solution, a mathematical model which includes, demand of units, net present value of products, delay cost due to retardation of Project is required. Moreover, the possibility of making predictions for future scenarios may assist in mapping out a route for the decision makers in steel construction firms. On this account, this study is designed to develop a mathematical model for the supplier selection system using integer programming.