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<p>Rural environmental adaptive design faces significant challenges due to complex environmental conditions, limited infrastructure, and the need for sustainable development solutions. This research proposes an innovative automated decision support platform that integrates Variational Autoencoders with Adversarial Neural Architecture Search (ANAS) to address these challenges. The platform comprises five core modules: data acquisition, feature extraction, design generation, architecture search, and decision output. The VAE component learns meaningful representations of successful design patterns and generates novel solutions through probabilistic modeling, while ANAS automatically discovers optimal neural architectures for specific design tasks. Experimental validation demonstrates superior performance compared to traditional methods, achieving 22.4% improvement in design diversity over GAN baseline (58.6% over traditional CAD), 22.2% enhancement in novelty metrics over GAN (60.1% over CAD), and 16.4% increase in environmental adaptability ratings over GAN (45.3% over CAD). The platform reduces design time by 65–75% while maintaining high-quality outputs and achieving professional acceptance rates exceeding 89%. This research contributes to the advancement of intelligent design automation and provides a scalable framework for sustainable rural development applications.</p>

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An automated decision support platform for rural environmental adaptive design based on VAE-ANAS integration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

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  • Nana Tang,
  • Furong Zhi,
  • Jiehua Sun

摘要

Rural environmental adaptive design faces significant challenges due to complex environmental conditions, limited infrastructure, and the need for sustainable development solutions. This research proposes an innovative automated decision support platform that integrates Variational Autoencoders with Adversarial Neural Architecture Search (ANAS) to address these challenges. The platform comprises five core modules: data acquisition, feature extraction, design generation, architecture search, and decision output. The VAE component learns meaningful representations of successful design patterns and generates novel solutions through probabilistic modeling, while ANAS automatically discovers optimal neural architectures for specific design tasks. Experimental validation demonstrates superior performance compared to traditional methods, achieving 22.4% improvement in design diversity over GAN baseline (58.6% over traditional CAD), 22.2% enhancement in novelty metrics over GAN (60.1% over CAD), and 16.4% increase in environmental adaptability ratings over GAN (45.3% over CAD). The platform reduces design time by 65–75% while maintaining high-quality outputs and achieving professional acceptance rates exceeding 89%. This research contributes to the advancement of intelligent design automation and provides a scalable framework for sustainable rural development applications.