Mohammadiziazi, Rezvan
(2022)
Modeling Energy and Material Use of Buildings at Urban Scale.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
In the past decade, scientific efforts to address the urgency of energy consumption and greenhouse gas (GHG) emissions from the building sector have increased. Buildings in the U.S. account for 39% of energy use and 38% of GHG emissions, contributing to adverse environmental and climate change impacts. Commercial buildings are responsible for approximately half of the total energy consumption. Given that more than 80% of the U.S. population lives in cities and urban areas, the role of urban buildings in energy consumption and emissions has become more crucial. Research about simulating energy consumption, modeling material use, and assessing the environmental impacts of buildings has increased; however, there are still issues that need to be addressed especially at the urban scale. The goal of this dissertation was to advance the sustainability of buildings by investigating the energy consumption and the embedded materials of existing building stocks. The energy use of buildings in the presence of climate change throughout the 21st century was estimated by integrating machine learning and climate change science. Most regions in the U.S. will experience increase in energy use. Further, to understand the trend of building energy use and evaluate the impacts of energy efficiency strategies at the urban scale, an urban building energy model was developed. This model also introduced a novel photogrammetry and imaging framework. The outcomes revealed that energy use was correlated to building use type and the implementation of efficiency strategies reduced energy use effectively. The gaps and barriers in analyzing the material stock of buildings were identified by the critical review of the state of the art in this field to understand how building material stock analysis can contribute to and improve the circular economy of buildings. Finally, quantifying the accumulated materials and renovation flow of a building stock showed that brick and concrete had the highest share of accumulated materials and renovation flow. Moreover, there were significant variations in material distribution of different building components. The knowledge about the type, quantity, and time of availability of materials upon renovation and demolition was crucial for closing the resource loop and reducing waste.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
6 September 2022 |
| Date Type: |
Publication |
| Defense Date: |
28 June 2022 |
| Approval Date: |
6 September 2022 |
| Submission Date: |
24 July 2022 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
221 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Civil and Environmental Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Urban building energy model, Building energy simulation, Machine learning, Building energy use, climate change, Circular economy, Material stock analysis, Material flow analysis, Buildings |
| Date Deposited: |
06 Sep 2022 16:23 |
| Last Modified: |
06 Sep 2022 16:23 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/43361 |
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