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Sustainable construction is critical to the architecture-engineering-construction (AEC) industry. The consideration and selection of optimal building components is necessary in order to fulfill multiple objectives of sustainable construction, which are often conflicting. Today, information and communication technologies (ICT) such as building information modeling (BIM) are widely used in the construction industry for decision making during design and construction. However, due to the complexity of building and construction, supporting multiple-objective decision making is not trivial. Currently, BIM has limited capability to support such a process. This paper introduces Simulation of Environmental Impact of Construction (SimulEICon), which is an extension of BIM designed specifically to aid in the decision-making process during the design stage of a construction project. SimulEICon is an add-on to the Autodesk Revit Architecture software, and it uses Microsoft Access for database-related operations. In addition, the genetic algorithm (GA), NSGA-II, is used for optimization in order to find solutions that best conform to project objectives. Moreover, SimulEICon has the capability of finding optimal solutions for all components at a building level or only for specific components and visualizing the solutions in a three- or four-dimensional model to support the decision-making process. Furthermore, the results can be constrained to conform to certain limitations. A case study of a real building, the Future House USA, is used with SimulEICon to demonstrate its use and results. A demonstration of SimulEICon is shown by comparing a set of possible solutions obtained by SimulEICon to those implemented in the building; it is expected that some of the solutions from SimulEICon would match those in the building at a component level, or even at a building level. Moreover, additional solutions to those present in the building show that SimulEICon is capable of presenting a wider range of possible solutions from which the user might choose the most appropriate one for the project goals.
Inyim et al. (Mon,) studied this question.