Low-carbon building evaluation index system based on hierarchical analysis method

Abstract

Low-carbon building is an unavoidable development trend in the construction industry, especially in the critical moment of global warming, it is necessary to make a comprehensive evaluation of low-carbon buildings. At this stage, low carbon building has become an important direction in the construction field, and whether the low carbon building reaches the corresponding standards and the advantages played by the low carbon field need to be assessed with perfect evaluation indexes. Based on this, this paper constructs a low-carbon building evaluation system from the whole life cycle of the building using the hierarchical analysis method (AHP) and BP neural network method. Firstly, the definition and influencing factors of low-carbon buildings are analyzed, secondly, the evaluation index system of low-carbon buildings is constructed, and then the evaluation index system of low-carbon buildings is verified by using the hierarchical analysis method, and the results show that the evaluation results based on the hierarchical analysis and the BP neural network method are more accurate than those of the traditional hierarchical analysis method. The results show that the evaluation results based on hierarchical analysis and BP neural network are more accurate than the traditional hierarchical analysis method. It shows that the BP neural network method can effectively reduce the influence of subjective factors in the hierarchical analysis method and improve the objectivity of the evaluation results. On this basis, this paper proposes countermeasures to promote the development of low-carbon buildings, in order to provide a certain reference for the long-term development of low-carbon buildings.