A novel approach to life cycle assessment for early-stage design of low-carbon buildings
| dc.contributor.author | Torabi, Mahsa S. | |
| dc.contributor.supervisor | Evins, Ralph | |
| dc.contributor.supervisor | Bristow, David | |
| dc.date.accessioned | 2024-10-16T20:30:10Z | |
| dc.date.available | 2024-10-16T20:30:10Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Civil Engineering | |
| dc.degree.level | Doctor of Philosophy PhD | |
| dc.description.abstract | Building design processes are dynamic and complex. The context of a building project is manifold and depends on the context, climatic conditions and personal design preferences. Many stakeholders may be involved in deciding between a number of possible designs defined by a set of influential design parameters. Building LCA is the state-of-the-art way to provide estimates of the building carbon content and environmental performance of various design alternatives. However, setting up a simulation model can be labour intensive and evaluating it can be computationally unfeasible. As a result, building simulations often occur at the end of the design process instead of being an influential factor in making early design decisions. Given this, the growing availability of machine learning algorithms as a potential method of exploring analytical problems has lead to the development of surrogate models in recent years. The idea of surrogate models is to learn from physics-based models, here a building LCA model, by emulating the simulation outputs given the simulation inputs. The key advantage is their computational efficiency in terms of accuracy and time. They can produce performance estimates for any desired building design within seconds, while in physics based modeling hours maybe needed to run the analysis. This shows the great potential of surrogate modelling to innovate the field. Instead of only being able to assess a few specific designs, entire regions of the design space can be explored, or instant feedback on the sustainability metrics of building can be given to architects during design sessions. This PhD thesis aims to advance the young field of building LCA surrogate models. It contributes by: (a) developing a parametric model capable of whole design space exploration, to solve the issue of lack of building LCA data and (b) deriving surrogate models that can process dataset of building carbon results and estimate the associated impact on building performance. The result of this study can assist architects, engineers, researchers and policy makers both by provided results and also the proposed methodology to integrated LCA in strategic and early-stage decision making in the design process. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/20603 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | Life cycle assessment | |
| dc.subject | Green building | |
| dc.subject | Carbon footprint | |
| dc.subject | Parametric LCA | |
| dc.subject | Surrogate model | |
| dc.title | A novel approach to life cycle assessment for early-stage design of low-carbon buildings | |
| dc.type | Thesis |