Generative Adversarial Network for Residential Building Plan Generation
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Solution Overview
Problem
Current residential building design processes using CAD tools are manual, slow, error-prone, and costly, lacking efficiency and accuracy, especially in batch processing and large-scale designs.
Innovation Solution
The method employs a generative adversarial network model trained on standard residential building plans to automatically generate designs, incorporating geographical region data, functional zone analysis, and denoising techniques to produce accurate and diverse plans, reducing human error and design time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual CAD design is used, then design flexibility and customization are maintained, but design speed and productivity are slow
Solution Approach 1:
The system enables self-service by automatically generating residential building plans through the GAN model without requiring manual CAD operations. The model independently processes input parameters (construction area, unit area, layout requirements) and produces complete design outputs, eliminating the need for designers to manually draw each plan while maintaining design quality through learned patterns from training data
Solution Approach 2:
The patent replaces the mechanical CAD drawing system with an AI-based generative model. Instead of using computer-aided design software that requires manual manipulation of graphical elements, the system uses a trained neural network that automatically generates design plans by processing numerical and spatial parameters, substituting the mechanical interaction with intelligent computation
2Reliability
If manual design by multiple people is used, then diverse design perspectives are achieved, but error rate increases
Solution Approach 1:
The system changes the fundamental parameter of design generation from human-dependent variables (designer skill, fatigue, attention) to model-dependent variables (training data quality, input parameters). By controlling input parameters such as construction area, unit area, and layout requirements, the system produces consistent, error-free outputs while maintaining adaptability through parameter adjustment rather than relying on varying designer expertise
3Adaptability or versatility
If multiple modifications are made manually, then design adjustments are achieved, but time cost and error rate increase significantly
Solution Approach 1:
The system performs preliminary action by generating complete, modification-ready design plans in the first place through the GAN model. The generated plans include all necessary design elements (unit layouts, functional zones, geometric information) in a structured format that can be directly adjusted by modifying input parameters rather than making sequential manual changes, significantly reducing the time cost of design iterations
4Adaptability or versatility
If high-variety residential building designs are produced manually, then user demand diversity is satisfied, but design time and cost increase
Solution Approach 1:
The GAN model achieves universality by being trained on diverse residential building plan data covering various geographical regions, building types, and design requirements. A single model can generate multiple varieties of designs by processing different input parameters (construction area, unit area, layout configurations), enabling batch processing of diverse design requests without requiring separate manual design processes for each variant
Data Source
AI summary
A method and device for automatically generating residential building plan, comprises: obtaining the standard plans of the residential buildings in each geographical region, analyzing the standard plans to obtain information on the construction area, residential unit area, unit layout, unit plan outline and functional composition and dimension corresponding to each of the standard plans as original training data; conducting training with original training data according to each geographical region in order to establish the residential building plan generative adversarial network model; receiving constraints and objective such as the target construction area, target residential unit area, target unit plan outline and target unit layout of the target residential building, generating residential unit plan model with functional zones based on the generative adversarial network model; denoising and trimming the residential unit plan model to obtain defined geometric information of residential building plan.