Environmental benchmarking of building typologies through BIM-based combinatorial case studies

Abstract Integrated life-cycle assessment (LCA) tools have emerged as decision-making support for BIM practitioners during the design stage of sustainable projects. However, differences between methodologies applied for determining the environmental impact of buildings produce significant variations in the results obtained, making them difficult to be compared. In this study, a methodology is defined for generating environmental benchmarks for building typologies through a combination of BIM-based LCA tools and machine learning techniques. When applied to an 11-story residential building typology with 92 dwellings by varying the constructive solutions of facades, partitions, roof and thermal insulation materials, results fall within a range from 360 to 430 kgCO2eq/m2. The Random Forest (RF) algorithm is successfully applied for identifying the most decisive variables in the analysis (partitions and facades), and shows signs of being useful for predicting the environmental impact of future constructions and to be applied to the analysis of greater scale urban zones.