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Multi-objective optimization for LEED-new construction using BIM and genetic algorithms
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AI
DOI：10.1016/j.autcon.2023.104807.png)
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Abstract
En 中文
Although LEED excels in reducing the negative environmental impacts and the energy consumption of buildings, the high costs in the early phases of the implementation and pursuit of LEED certification are pushing away some project owners from entering the process. How can the objectives of (1) getting the points necessary to achieve the desired LEED certification and (2) the life-cycle cost of sustainable projects be balanced? In this study, Non -dominated Sorting Genetic Algorithm-II (NSGA-II), a multi-objective optimization tool, is proposed to find the optimal solution measured in terms of life-cycle cost and sustainability for a new construction project pursuing LEED v4 BD + C New Construction and Major Renovation certification. A BIM project of a 3-floor educational building was selected as a case study to verify the efficiency and soundness of the proposed model. The results show that the method does indeed lead to optimal solutions. The proposed model is expected to benefit con-struction owners, designers, and contractors alike, Expanding the database of components and extending the model to cover LEED v4 projects other than BD + C New Construction and Major Renovation could improve the reach and impact of the proposed model.
Keywords:
Multi-objective optimization
Building information modeling
BIM
NSGA-II
Revit
LEED
Automation
Sustainability
MATLAB
Dynamo
Journal
IF:
11.5
Papers:
6.2K
Citations:
4.2W
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