In recent years the application of Life Cycle Assessment (LCA) for assessing and improving the environmental performance of buildings has increased. At the same time, the automated optimization of building designs is gaining attraction for both design and research purposes. In this regard, a number of issues persist when aiming to optimize building's environmental impacts along the design process. Firstly, as LCA applies a life cycle perspective, many aspects have to be considered (e.g. energy demand in operation as well as consumption of resources and energy for production and end of life treatment) and a variety of specific calculations is needed (e.g. building energy performance simulation, material quantity take-off). Secondly, sophisticated software packages are available and being used for each of these calculations (e.g. software for building modelling, dynamic energy simulation, quantity surveying). Though many of these software packages are currently standalone applications that rely on human interaction, there is an increasing trend to provide an application programming interface (API) that enables customization and automation. Thirdly, the mentioned processes and calculations are influencing each other in various ways and several scenarios have to be assessed. Thus, a comprehensive and modular approach is required that promotes interconnectivity of the different software solutions and automation of the assessment. In this paper we propose a modular cross-platform framework for LCA of buildings aiming to support flexibility and scalability of building LCA. We present a conceptual framework, example data exchange requirements and highlight potential implementation strategies.
|Fachzeitschrift||IOP Conference Series: Earth and Environmental Science|
|Publikationsstatus||Veröffentlicht - Sep 2019|
Fields of Expertise
- Sustainable Systems
Kiss, B., Röck, M., Passer, A., & Szalay, Z. (2019). A cross-platform modular framework for building Life Cycle Assessment. IOP Conference Series: Earth and Environmental Science, 323, . https://doi.org/10.1088/1755-1315/323/1/012103