Application of Knowledge Discovery in Databases (KDD) to environmental, economic, and social indicators used in BIM workflow to support sustainable design

Abstract

Life Cycle Sustainability Assessment (LCSA) can help to predict the impact of products and services, such as buildings, during their entire life cycle. However, it is an extensive data method. The Building Information Modelling (BIM) method can contribute towards reducing the effort involved and simplifying the data collection relating to the building elements. To this end, databases adjusted to the BIM workflow are needed to systematise and harmonise the structure of the environmental, economic and social data of said elements. This paper provides a solution to this problem by presenting an innovative Triple Bottom Line (TBL) database with environmental, economic, and social indicators of building elements to support the triple assessment adapted to the BIM workflow. An analysis employing Knowledge Discovery in Databases (KDD) was performed for the first time on this type of database to better understand the correlations between the dimensions. The key contributions include correlation detection, 83 % of which were direct, which showed that, overall, the environmental (CO2 emissions), economic (cost), and social (labour) dimensions experience similar growth trends. Strong correlations between economic and social variables were found in 68 % of the cases, followed by those of the economic and environmental (32 %), and social and environmental (18 %) variables. Findings from the correlation analysis between the three dimensions reveal their influence on the type of building system, element and material. Four scenarios were thereby identified in accordance with these correlations, to aid in sustainable decision-making. Various growth trends were detected, which can facilitate the implementation of the LCSA.