Details

Autor(en) / Beteiligte

• Fan, Zhaoxiang
• Liu, Mengxuan
• Tang, Shuoning
• Zong, Xuan

Titel

Multi-objective optimization for gymnasium layout in early design stage: Based on genetic algorithm and neural network

Ist Teil von

• Building and environment, 2024-06, Vol.258, Article 111577

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The layout of gymnasium directly affects environment performance. The current methods are insufficient to provide quantified decision support for gymnasium layouts in the early design stages (EDS). This study proposes a framework for optimizing the layout of gymnasiums using a multi-objective optimization (MOO) method based on genetic algorithms (GA) and neural networks. The study tested the framework using a community sports arena as an example, and the results indicate: the final optimized solutions achieved a maximum reduction of 11.1 % in cooling energy consumption (CE) and 3.3 % in solar radiation (SR) compared to the earlier generations, along with a 0.9 % improvement in thermal comfort percentage (TCP). This framework promotes the development of algorithm-driven methods for stadium layout design, while the prediction model based on RBF neural networks can simultaneously provide effective performance predictions for similar design outcomes. •A multi-objective optimization approach is proposed to optimize gymnasium layout.•Pareto front solution dataset and prediction model were established.•A performance based on radial basis function networks was built.•Visualized optimization results assist decision-making in the early design stages.