Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Building and Real Estate | en_US |
| dc.contributor.advisor | Sun, Yi (BRE) | en_US |
| dc.creator | Zhang, Qian | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13161 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Research on morphological optimization algorithms for urban villages under compact city perspective | en_US |
| dcterms.abstract | Rapid urbanization continues to shape the demographic landscape globally, with projections indicating that by 2050, approximately 68% of the world’s population will reside in urban areas. In China, the expansion of urban regions such as the Pearl River Delta has led to the encapsulation of traditional villages within city boundaries, creating unique challenges in these urban villages. | en_US |
| dcterms.abstract | This study specifically addresses the complex renovation needs of Guangzhou’s Chebei Village by employing a novel methodology that integrates generative design with machine learning. Our approach tackles three primary challenges: insufficient natural lighting and deficient public space, disorganized spatial functions, which are prevalent in urban villages. By leveraging Grasshopper and its plugin, we implement generative design algorithms on the Grasshopper platform to simulate multiple demolition scenarios aimed at enhancing daylight accessibility and optimizing spatial layouts. | en_US |
| dcterms.abstract | Concurrently, machine learning algorithms analyze these scenarios to inform decisions on building retention based on daylight impact factors, enhancing both the functionality and livability of the urban space. This method not only streamlines the design process but also ensures that renovations are aligned with sustainable urban development goals. | en_US |
| dcterms.abstract | Through a rigorous field study and literature review, our research outlines a renovation process for urban village renewal that is scientifically grounded and replicable. The integration of generative design and machine learning represents a significant advancement in the architectural and engineering (AEC) industry, providing a robust framework for addressing the multi-dimensional challenges of urban renewal. Our study not only demonstrates the efficacy of this integrated approach but also highlights its potential to revolutionize urban planning practices by enhancing environmental quality and social functionality within densely populated urban villages. | en_US |
| dcterms.extent | 130 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Urban renewal -- China -- Guangzhou | en_US |
| dcterms.LCSH | City planning -- Data processing | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7612.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 15.26 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/13161