Multi-objective optimization for LEED: New construction using genetic algorithms

In the U.S. , the building sector is responsible for 73% of electricity usage, 38% of CO2 emissions, and 13. 6% of potable water. These data indicate that the construction industry negatively impacts the global environment and natural resources. The concept of “sustainability” was introduced to set guidelines for the construction industry to limit its negative environmental impact. To promote sustainability in the construction industry, many organizations have introduced guidelines and rating systems for buildings. One of these rating systems is Leadership in Energy and Environmental Design (LEED) which is the most globally acknowledged system. Although LEED excels in reducing the negative environmental impacts and the energy consumption of buildings, the high costs in the early phases associated with the implementation and pursuit of LEED certification are pushing away some project owners from entering the process. Therefore, to balance these objectives in sustainable projects, an approach which optimizes multiple objectives is needed. In this study, a multi-objective optimization framework, which uses Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is proposed to find the optimal solution in terms of life-cycle cost and sustainability for a new construction project pursuing LEED v4 BD+C certification. A BIM project of a 3-floor educational building was selected as a case study in the research. The study case is used to verify the efficiency and soundness of the proposed model. The results show that the method does indeed lead to optimal solutions.