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Related Experiment Video

Updated: Sep 3, 2025

Design and Optimization Strategies of a High-Performance Vented Box
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Phenotype Variability Mimicking as a Process for the Test and Optimization of Dynamic Facade Systems.

Ana Cocho-Bermejo1, Maria Vogiatzaki1

  • 1Faculty of Science and Engineering, Anglia Ruskin University, Chelmsford CM1 1SQ, UK.

Biomimetics (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

This study introduces a dynamic façade system using genetic algorithms and artificial neural networks to optimize thermal efficiency. The system adapts to weather and occupant needs for improved building performance.

Keywords:
artificial intelligenceartificial neural networkscomplex systemsevolutionary computationgenetic algorithmsinsulation optimizationintelligent facadepareto frontierphenotype

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Area of Science:

  • Building Science
  • Computational Intelligence
  • Materials Science

Background:

  • Dynamic façade systems are crucial for optimizing building energy performance.
  • Adaptive building envelopes require sophisticated design and control strategies.
  • ETFE (Ethylene tetrafluoroethylene) cushions offer potential for innovative façade solutions.

Purpose of the Study:

  • To design a dynamic multi-layered façade system using computational intelligence.
  • To enable real-time adaptation of the façade to environmental conditions and occupant requirements.
  • To optimize the thermal efficiency of ETFE cushion-based façade systems.

Main Methods:

  • Deployment of a genetic algorithm (GA) for performance optimization.
  • Utilization of an artificial neural network (ANN) for real-time adaptation.
  • Modeling façade cushions as artificial neurons within a digital framework.
  • Simulating phenotypical adaptations based on environmental data and GA-optimized gene configurations.

Main Results:

  • The genetic algorithm successfully optimized façade cushion performances.
  • The artificial neural network enabled learning from environmental data models.
  • The computational model demonstrated phenotypical adaptations for thermal efficiency.
  • The proposed façade system showed maximized thermal efficiency across various scenarios.

Conclusions:

  • The integration of genetic algorithms and artificial neural networks is effective for designing adaptive façade systems.
  • The developed computational model successfully optimizes thermal performance in dynamic conditions.
  • This approach offers a promising solution for energy-efficient and responsive building envelopes.