Non-graphical data structure for the purpose of BIM-based Life Cycle Assessment: Methodology for the Czech environment

Abstract

The article deals with the data structure required for the Life Cycle Assessment (LCA) of buildings using the Building Information Model (BIM) for implementation in the Czech construction industry. The construction industry produces a significant amount of waste while the capacity of landfills is being limited. The effective management of previously used materials that have the potential to be reused can significantly help to decrease consumption and save natural resources. LCA is a method that can be used to demonstrate the suitability of proposed materials, structures or buildings in terms of their whole life cycle and its environmental impact. For an LCA evaluation, it is crucial to obtain life cycle inventory (LCI) input data, though the structure of this data has not yet been clearly defined for the Czech environment. BIM includes, in addition to geometry, an information component which is applicable to this assessment. The aim of the article is to analyze previous approaches and define a BIM data structure for a particular material for LCI purposes. The new methodology is based on the standardization of a non-graphical information model data structure (SNIM) which was developed for the Czech construction industry. “Markup syntaxes” have been taken from the SNIM database and used to further identify the data for LCI base structure purposes. The MatLab matrix solver was used to classify these data. The advantages of the proposed methodology have been demonstrated via a case study of a monolithic concrete slab structure in which three types of concrete were evaluated. The results indicated the percentage of impact categories for each concrete type. These results are useful for expanding the BIM model with new data necessary for further LCA calculations. The extension of the BIM model via LCA calculations will lead to a reduction in the extraction of natural resources as well as a positive impact on the environment.