Cocho-Bermejo Ana, Vogiatzaki Maria
Faculty of Science and Engineering, Anglia Ruskin University, Chelmsford CM1 1SQ, UK.
Biomimetics (Basel). 2022 Jun 26;7(3):85. doi: 10.3390/biomimetics7030085.
A genetic algorithm and an artificial neural network are deployed for the design of a dynamic multi-layered façade system that adapts in real-time to different weather and occupants' needs scenarios. The outputs are a set of different performances of the façade insulation cushions, optimized by the previous run of the genetic algorithm. A façade system of ETFE cushions is considered for them to learn from environmental data models. Each façade cushion is set up as an artificial neuron that is linked to the behavior and temperature of the others. The proposed outputs are a set of different performances of the façade system that are optimized through running the genetic algorithm. Façade neurons are configured as genes of the system that is abstractly represented on a digital model. The computational model manages cushion patterns' performances through several phenotypical adaptations, suggesting that the proposed facade system maximizes its thermal efficiency in different scenarios.
部署了遗传算法和人工神经网络来设计动态多层立面系统，该系统可实时适应不同的天气和居住者需求场景。输出结果是立面隔热垫的一组不同性能，这些性能通过遗传算法的上一轮运行进行了优化。考虑采用ETFE垫的立面系统，以便从环境数据模型中学习。每个立面垫都被设置为一个人工神经元，它与其他立面垫的行为和温度相关联。所提出的输出结果是通过运行遗传算法优化后的立面系统的一组不同性能。立面神经元被配置为系统的基因，在数字模型上进行抽象表示。计算模型通过几种表型适应来管理垫子图案的性能，这表明所提出的立面系统在不同场景下能最大限度地提高其热效率。
