Optimising Embodied Energy and Thermal Performance of Thermal Insulation in Building Envelopes via an Automated Building Information Modelling (BIM) Tool
Author(s): |
Zixuan Chen
Ahmed W. A. Hammad Imriyas Kamardeen Ali Akbarnezhad |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 24 November 2020, n. 12, v. 10 |
Page(s): | 218 |
DOI: | 10.3390/buildings10120218 |
Abstract: |
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. |
Copyright: | © 2020 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
4.98 MB
- About this
data sheet - Reference-ID
10526429 - Published on:
09/12/2020 - Last updated on:
02/06/2021