Data-driven solutions for building environmental impact assessment

Abstract

Life cycle assessment (LCA) as a decision support tool for evaluating the environmental load of products has been widely used in many fields. However, applying LCA in the building industry is expensive and time consuming. This is due to the complexity of building structure along with a large amount of high-dimensional heterogeneous building data. So far building environmental impact assessment (BEIA) is an important yet under-addressed issue. This paper gives a brief survey of BEIA and investigates potential advantages of using data mining techniques to discover the relationships between building materials and environment impacts. We formulate three important BEIA issues as a series of data mining problems, and propose corresponding solution schemes. Specifically, first, a feature selection approach is proposed based on the practical demand and construction characteristics to perform assessment analysis. Second, a unified framework for solving constraint-based clustering ensemble selection is proposed to extend the environmental impact assessment range from the building level to the regional level. Finally, a multiple disparate clustering method is presented to help sustainable new buildings design. We expect our proposal would shed light on data-driven approaches for environment impact assessment.