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.

Life cycle assessment (LCA) is a scientific method for evaluating the environmental impact of products. Standards provide a general framework for conducting an LCA study and calculation rules specifically for buildings. The challenge is to design energy-efficient buildings that have a low environmental impact, reasonable costs, and high thermal comfort as these are usually conflicting aspects. Efficient mathematical optimisation algorithms can be applied to such engineering problems. In this paper, a framework for automated optimisation is described, and it is applied to a multi-story residential building case study in two locations, Portugal and Hungary. The objectives are to minimise the life cycle environmental impacts and costs. The results indicate that optimum solutions are found at a higher cost but lower global warming potential for Portugal than for Hungary. Optimum solutions have walls with a thermal transmittance in the intervals of 0.29–0.39 and 0.06–0.19 W/m²K for Portugal and Hungary, respectively. Multi-objective optimisation algorithms can be successfully applied to find solutions with low environmental impact and an eco-efficient thermal envelope.

Building life cycle assessment is getting more and more attention within the topic of environmental impact caused by the built environment. Although more and more research focus on the embodied impact of buildings, the investigation of the operational energy use still needs attention. The majority of the building stock still does not comply with the nearly zero energy requirements. Also, in case of retrofitting, when most of the embodied impact is already spent on the existing structures (and so immutable), the importance of the operational energy rises. There are several methods to calculate the energy performance of buildings covering the range from simplified seasonal methods to detailed hourly energy simulations. Not only the accuracy of the calculations, but the computational time can be significantly different within the methods. The latter is especially important in case of optimization, when there is limited time to perform one calculation. Our research shows that the use of different calculation techniques can lead to different optima for environmental impacts in case of retrofitting. In this paper we compare these calculation methods with focus on computational time, accuracy and applicability to environmental optimization of buildings. We present the results in a case study of the retrofitting of a middle-sized apartment house in Hungary.