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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 周承復 | zh_TW |
| dc.contributor.advisor | Cheng-Fu Chou | en |
| dc.contributor.author | 江雨艾 | zh_TW |
| dc.contributor.author | Yu-Ai Jiang | en |
| dc.date.accessioned | 2024-08-08T16:16:16Z | - |
| dc.date.available | 2024-08-09 | - |
| dc.date.copyright | 2024-08-08 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-02-23 | - |
| dc.identifier.citation | [1] “Global Energy Review: CO2 Emissions in 2021”.IEA (2022).
[2] Lisa Heschong, Kevin Van Den Wymelenberg, Approved Method: IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE), Illuminating Engineering Society of North America, 2012. [3] Xian-De Lin, Zi-Ping Lin, Yao-Hsien Tsai , Green Building Evaluation Manual-Basic Version, 2023 Edition, Taiwan, 2023. [4] S. Building and Construction Authority, “Code on envelope thermal performance for buildings”. [5] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, “Image-to-Image Translation with Conditional Adversarial Nets,” Computer Vision and Pattern Recognition, 2017. [6] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition,” Computer Vision and Pattern Recognition, 2015. [7] Federico Bucci, Marco Mulazzani, Luigi Moretti: Works and Writings, Princeton Architectural Press, 2002. [8] M. Luz Castro Pena *, Adri´an Carballal, Nereida Rodríguez-Fern´andez, Iria Santos, Juan Romero, “Artificial Intelligence Applied to Conceptual Design: A Review of Its Use in Architecture,” Automation in Construction, Volume 124, April 2021. [9] Funes, Pablo & Pollack, Jordan, “Computer Evolution of Buildable Objects for Evolutionary Design by Computers,” 1997. [10] von Mammen, Sebastian & Jacob, Christian, “Evolutionary swarm design of architectural idea models.,” GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008, 2008. [11] Konis, K., Gamas, A., Kensek, K, “Passive Performance and Building Form: An Optimization Framework for Early-Stage Design Support,” Solar Energy, 2016. [12] Chen Chen, Ricardo Jose Chacón Vega, Tiong Lee Kong,, “Using Genetic Algorithm to Automate the Generation of an Open-plan Office Layout,” International Journal of Architectural Computing, 2021. [13] Imdat As, Siddharth Pal, Prithwish Basu, “Artificial intelligence in architecture: Generating conceptual design via deep learning,” International Journal of Architectural Computing, 2018. [14] Xinyue Ye, Jiaxin Du, Yu Ye, “MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks,” Urban Analytics and City Science, 2022. [15] J. Grason, “An approach to computerized space planning using graph theory,” Proceedings of the 8th Design Automation Workshop, 1971. [16] Huang, Weixin & Zheng, Hao, “Architectural Drawings Recognition and Generation through Machine Learning,” conference ACADIA, 2018. [17] S. Chaillou, AI + Architecture | Towards a New Approach, Harvard GSD, 2019. [18] Wenming Wu, Xiao-Ming Fu, Rui Tang, Yuhan Wang, Yu-Hao Qi, Ligang Liu, “Data-driven Interior Plan Generation for Residential Buildings,” ACM Transactions on Graphics (SIGGRAPH Asia), 2019. [19] Ruizhen Hu, Zeyu Huang, Yuhan Tang, Oliver Van Kaick, Hao Zhang, and Hui Huang, “Graph2Plan: learning floor plan generation from layout graphs,” ACM Trans. Graph. 39, 4, Article 118, Aug 2020. [20] Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang, Hang Chu, Chin-Yi Cheng, Yasutaka Furukawa, “House-GAN++: Generative Adversarial Layout Refinement Networks towards Intelligent Computational Agent for Professional Architects,” Computer Vision and Pattern Recognition Conference (CVPR), 2021. [21] Feixiang He, Yanlong Huang, He Wang, “iPLAN: Interactive and Procedural Layout Planning,” Conference Computer Vision and Pattern Conference, 2022 . [22] Shen, Jiaqi & Liu, Chuan & Ren, Yue & Zheng, Hao, “Machine Learning Assisted Urban Filling,” Conference CAADRIA, 2020. [23] R. Tian, “Suggestive Site Planning with Conditional GAN and Urban GIS Data,” CDRF, 2019. [24] Y Liu, Z Zhang, Q Deng, “Exploration on Diversity Generation of Campus Layout Based on GAN,” The International Conference on Computational Design and Robotic Fabrication, 2022. [25] J.M. Gagne, M. Andersen, “Multi-objective façade optimization for daylighting,” Proc. SimBuild 4, 2010. [26] Lu, Xinzheng & Liao, Wenjie & Huang, Yuli & Zheng, Zhe & Lin, Yuanqing., “Automated structural design of shear wall residential buildings using generative adversarial networks,” Automation in Construction, 2020. [27] Emilie Nault, Peter Moonen, Emmanuel Rey, Marilyne Andersen,, “Predictive models for assessing the passive solar and daylight potential of neighborhood designs: A comparative proof-of-concept study,” Building and Environment, 2017. [28] Valerio R.M. Lo Verso, Gueorgui Mihaylov, Anna Pellegrino, Franco Pellerey,, “Estimation of the daylight amount and the energy demand for lighting for the early design stages: Definition of a set of mathematical models,” Energy and Buildings, 2017. [29] M. Ayoub, “A multivariate regression to predict daylighting and energy consumption of residential buildings within hybrid settlements in hot-desert climates.,” Indoor and Built Environment., 2019. [30] Clara-Larissa Lorenz, , Michael Packianather, A. Benjamin Spaeth and Clarice Bleil De Souza, “Artificial Neural Network-Based Modelling for Daylight,” simaud, 2017. [31] Qiushi He, Ziwei Li , Wen Gao, Hongzhong Chen, Xiaoying Wu, Xiaoxi Cheng, Borong Lin, “Predictive models for daylight performance of general floor plans based on CNN and GAN: A proof-of-concept study,” Building Environment, Dec 2021. [32] L. Xian-De, Thermal Environment of Human Habitat, 詹氏書局, 2009. [33] A. K. Tnage, “Uni-President Enterprise Taipei Branch”.wikipedia. [34] KRIS YAO | ARTECH, “Hua Nan Bank Headquarters,” ArchDaily, 2014. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93798 | - |
| dc.description.abstract | 敷地計畫在建築平面設計中是重要的階段，需要考慮包括考慮場地環境和永續議題等。過去研究雖然已經探討了使用基因算法和機器學習的自動平面圖生成，但往往忽略了基地條件，導致方案不能回應實際設計。此外儘管基因演算法很常用於平面生成，但其計算成本高且費時。本文處理前期建築配置的自動生成，主要強調將基地條件的納入初期建築生成設計，尤其著重物理環境造成的影響，而這個階段的策略也將對後續設計產生重大影響。
本文提出了一種新的自動生成配置圖的流程，將法規、採光與能耗等因素納入考慮，並且以基地條件作為約束與設計的新可能性。作者提出最佳化前期平面圖生成的流程，將參數化和機器學習方法整合，而法規、日照和能源消耗等面向作為設計目標，提供設計師更為永續的解決方案。作者使用參數化模型生成平面圖，並且嘗試不同的生成式機器學習模型。接著使用pix2pix和Resnet-50評估採光與能源消耗結果，最後採用本地搜索演算法來建議最佳的設計方案。 主要貢獻包括： 1.在自動建築平面生成流程中，整合基地條件。 2.通過增加設計要素和擴展數據集來增強評估階段，以提高精度和適用性。 3.利用機器學習生成的結果以及採光和能源消耗的評估數值來尋找最佳化的平面圖。 這篇論文提供自動化建築平面設計更完整與有效的永續設計方法，以助於建築師和研究人員更快速產生質量更高的初步設計方案。 | zh_TW |
| dc.description.abstract | Site planning stands as a pivotal stage within architectural floor plan design, including considerations of site contexts and sustainable issues. While previous research has explored floor plan generation using genetic algorithms and machine learning, it often neglected site context, resulting in undesired solutions. Moreover, though genetic algorithms are common, they can be computationally intensive. This thesis ad-dresses machine learning-based early-stage architectural plan generation, emphasizing the inclusion of site conditions, particular for influences from sunlight. The goal is to find a sustainable suggestion for footprint, which will greatly impact subsequent design.
This thesis presents a novel approach to automated site planning that incorporates regulations, daylight and energy considerations. The author introduces a pipeline for optimal footprint generation through parametric and machine learning methods, ad-dressing the lack of integration between design phases and offering a more site-responsive and eco-friendly design approach. The author generates footprint with parametric models, experiments generative machine learning models, and evaluates the results using pix2pix and Resnet-50. A local search is then applied to suggest optimal solutions. Key contributions include: 1.Pioneering the integration of site conditions into the automatic architecture plan generation pipeline. 2.Enhancing the evaluation pipeline with additional design factors and a larger dataset for improved accuracy and applicability. 3.Utilizing machine learning outcomes to optimize plans while providing evaluation feedback on daylight and energy consumption. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-08T16:16:16Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-08T16:16:16Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENTS I
摘要 II ABSTRACT III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VII CHAPTER 1 INTRODUCTION 1 CHAPTER 2 RELATED WORK 4 2.1 Parametric Architecture Design 4 2.2 Generative Architecture Plan 5 2.3 Building Daylight and Energy Evaluation 10 CHAPTER 3 METHODOLOGY 14 3.1 Design Framework 14 3.1.1 Labeling 15 3.1.2 Dataset 16 3.2 Stage I – Generation 17 3.2.1 Parametric Model 18 3.2.2 Machine Learning Model Experimentation 20 3.3 Stage II – Evaluation 23 3.3.1 Metrics – sDA, ASE, EnvLoad, ETTV 24 3.3.2 Daylight Visualization Model and Calculation Model 27 3.4 Stage III - Optimization 30 3.4.1 Local Search – Gradient Descent 31 CHAPTER 4 RESULTS AND DISCUSSION 33 4.1 Stage I – Generation 33 4.2 Stage II – Evaluation 36 4.3 Stage III – Optimization 43 CHAPTER 5 CONCLUSION 50 REFERENCE 51 | - |
| dc.language.iso | en | - |
| dc.subject | 參數化模型 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 最佳化 | zh_TW |
| dc.subject | 建築日照和能源評估 | zh_TW |
| dc.subject | 前期建築平面生成 | zh_TW |
| dc.subject | parametric models | en |
| dc.subject | early-stage design | en |
| dc.subject | machine learning | en |
| dc.subject | daylight and energy evaluation | en |
| dc.subject | footprint optimization | en |
| dc.subject | architectural plan generation | en |
| dc.title | 生成建築配置圖中使用機器學習整合採光與能耗的方法 | zh_TW |
| dc.title | A Holistic Approach Integrating Daylight and Energy in Machine Learning-Based Architectural Footprint Generation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 彭立沛 | zh_TW |
| dc.contributor.coadvisor | Li-Pei Peng | en |
| dc.contributor.oralexamcommittee | 吳曉光;蔡耀賢;施宣光 | zh_TW |
| dc.contributor.oralexamcommittee | Hsiao-Kuang Wu;Yaw-Shyan Tsay;Shen-Guan Shih | en |
| dc.subject.keyword | 前期建築平面生成,建築日照和能源評估,最佳化,機器學習,參數化模型, | zh_TW |
| dc.subject.keyword | early-stage design,architectural plan generation,footprint optimization,daylight and energy evaluation,machine learning,parametric models, | en |
| dc.relation.page | 53 | - |
| dc.identifier.doi | 10.6342/NTU202400058 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-02-23 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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