| id |
caadria2024_373 |
| authors |
Fan, Zhaoxiang, Tang, Shuoning and Liu, Mengxuan |
| year |
2024 |
| title |
Integrating Genetic Algorithms and RBF Neural Networks in the Early Design Stage of Gymnasium for Multi-Objective Optimization Framework |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.505
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 505–514 |
| summary |
The early design phase of the gymnasium's enclosing interfaces directly affects the indoor daylighting and thermal environmental performance. The optimization framework proposed in this study aims to simultaneously balance and optimize conflicting objectives, including the maximum daylight factor (DF), minimum daylight glare index (DGP), and minimum solar radiation (RS) for gymnasium. This approach aims to maximize daylighting performance in hot summer regions while avoiding glare, reducing energy consumption, and ultimately enhancing both daylight comfort and energy efficiency during the sports facility design process. Using the SPEA-2 genetic algorithm, the study explored the Pareto front solutions for three different skylight patterns and established a predictive model for design results based on a Radial Basis Function (RBF) neural network. Compared to traditional Multi-Objective Optimization (MOO) frameworks, this optimization method improves computational efficiency and provides more intelligent decision support for the early-stage design of gymnasiums. |
| keywords |
Multi-Objective Optimization (MOO), Building Performance Simulation (BPS), Parametric Design (PD), Predictive Model. |
| series |
CAADRIA |
| email |
liumengxuan93@163.com |
| full text |
 file.pdf (1,902,337 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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