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Room-level conditional graph neural networks for shear wall layout generation in high-rise buildings
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DOI：10.1016/j.autcon.2026.107189.png)
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Abstract
En 中文
• Room-level graphs encode spatial semantics and boundary feasibility. • FiLM conditioning enables discrete density-controlled wall generation. • Dual-stream training improves compliance with sparse paired labels.
Keywords:
Structural design automation
Shear wall layout
Graph neural networks
Conditional generation
Room-level representation
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