Insulation systems for the floor, roof, and external walls play a prominent role in providing a thermal barrier for the building envelope. Design decisions made for the insulation material type and thickness can alleviate potential impacts on the embodied energy and improve the building thermal performance. This design problem is often addressed using a building information modelling (BIM)-integrated optimisation approach. However, one major weakness that lies in the current studies is that BIM is merely used as the source for design parameters input. This study proposes a BIM-based envelope insulation optimisation design tool using a common software Revit and its extension Dynamo to find the trade-off between the total embodied energy of the insulation system and the thermal performance of the envelope by considering the material type and thickness. In addition, the tool also permits data visualisation in a BIM environment, and automates subsequent material library mapping and instantiates the optimal insulation designs. The framework is tested on a case study based in Sydney, Australia. By analysing sample designs from the Pareto front, it is found that slight improvement in the thermal performance (1.3399 to 1.2112 GJ/m²) would cause the embodied energy to increase by more than 50 times.

Windows account for a significant proportion of the total energy lost in buildings. The interaction of window type, Window-to-Wall Ratio (WWR) scheduled and window placement height influence natural lighting and heat transfer through windows. This is a pressing issue for nontropical regions considering their high emissions and distinct climatic characteristics. A limitation exists in the adoption of common simulation-based optimisation approaches in the literature, which are hardly accessible to practitioners. This article develops a numerical-based window design optimisation model using a common Building Information Modelling (BIM) platform adopted throughout the industry, focusing on nontropical regions of Australia. Three objective functions are proposed; the first objective is to maximise the available daylight, and the other two emphasize undesirable heat transfer through windows in summer and winter. The developed model is tested on a case study located in Sydney, Australia, and a set of Pareto-optimum solutions is obtained. Through the use of the proposed model, energy savings of up to 8.57% are achieved.