AN RBF BASED HIGH-DIMENSIONAL MODEL REDUCTION APPROACH IN BUILDING OPTIMIZATION

Abstract

Building optimization has gained importance with the recent push to create the most economical and efficient buildings possible. As the effects of optimization are a function of the building size, it is crucial to understand and further develop optimization techniques for large-scale building structures. Practical structural optimization of buildings requires the use of a structural analysis software package and an iterative optimization procedure. As a result, finite element (FE) software shall be linked with an optimization solver. It is an expensive process which requires extensive computer coding. Alternative methods are available, including metamodeling methods, which are used to create simple and approximate functions based on complex FE simulations. In this study, the approximate functions are generated using a high-dimensional model representation (HDMR) framework. The HDMR framework is a model reduction approach and is found to be very accurate for different functions. The component functions of HDMR are expressed using augmented radial basis functions (RBFs). To further improve the numerical efficiency of the metamodels and reduce the total required number of structural analyses, a few different HDMR sampling approaches are investigated, including one static approach and two iterative strategies. An existing nonlinear programming (NLP) solver is employed in the design process. To illustrate the proposed approach, a three-dimensional building structure is selected as a numerical example. The numerical optimization is conducted to reduce the torsional response of the building. The proposed optimization method works very well and the results from different HDMR techniques are compared.