부산대학교 도서관

This paper proposes a framework that integrates Building Information Modeling (BIM) with Genetic Algorithm Optimization and Monte-Carlo Simulation. The developed framework helps in implementing a stochastic Life-Cycle Cost (LCC) model for building to select the optimum building materials alternatives and discover the most influential building system in each cost element starting from initial cost to end on life cost. Genetic Algorithms Optimization technique is utilized to select the optimum alternatives of building systems taking in consideration sustainability aspects. The Monte-Carlo Simulation model is used as a fitness function for the optimization model. The environmental aspect of building is achieved by considering the maximum number of points that can be awarded under the Leadership in Energy and Environmental Design (LEED) rating system. Sensitivity analysis is performed on the optimum solutions that are chosen by optimization a model to examine the effect of different building systems on LCC and its components through computing the rank order of building systems and the target output. A case study is presented to demonstrate the practical features of the proposed framework. [ABSTRACT FROM AUTHOR]