Multi-Objective Optimization of Daylight in Building Skin Guided by Lingnan Traditional Wisdom
- DOI
- 10.2991/978-94-6463-815-8_38How to use a DOI?
- Keywords
- Multi-Objective Optimization; Daylight; Parametric Design
- Abstract
While multi-objective optimization (MOO) gains traction in building performance, traditional strategies—like historic daylight-ventilation-shading designs—are sidelined as mere decorations, with untapped environmental potential and limited computational integration.
This study bridges this gap via a Lingnan heritage-MOO envelope framework targeting UDI, GA, and illuminance CV. Parametric modeling converts traditional geometric features into computable rules for designer-guided optimization. Key variables (gradient patterns, porosity, spatial configurations) are simulated in Grasshopper with NSGA-II, generating Pareto solutions analyzed by k-means clustering.
Case results show optimized envelopes improve UDI, GA, and uniformity while retaining traditional aesthetics. The research presents a “heritage-performance” integration approach, offering early-stage methods to merge high-performance design with architectural tradition.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Haixiu Liang AU - Dongyuan Guo PY - 2025 DA - 2025/08/13 TI - Multi-Objective Optimization of Daylight in Building Skin Guided by Lingnan Traditional Wisdom BT - Proceedings of the 2025 4th International Conference on Art Design and Digital Technology (ADDT 2025) PB - Atlantis Press SP - 354 EP - 361 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-815-8_38 DO - 10.2991/978-94-6463-815-8_38 ID - Liang2025 ER -