Generative Housing Layouts for Code and Financial Constraints
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Solution Overview
Problem
Conventional CAD software struggles to navigate the design space for multi-family housing projects effectively, failing to balance regulatory compliance with financial performance and incorporate landowner feedback without adversely impacting design feasibility or desirability, resulting in predominantly infeasible or undesirable designs.
Innovation Solution
A design engine integrated into CAD applications that includes a design analyzer, site analyzer, design generator, and design evaluator, which generates design trends from historical data, adheres to construction regulations, and iteratively improves design options based on financial metrics and landowner feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional design methods are used, then design control and predictability are maintained, but design iterations are time-consuming and resource-intensive
Solution Approach 1:
The patent uses generative design software to create multiple digital design iterations rapidly, allowing designers to evaluate numerous options without manually redrawing each scheme. The system generates alternative layouts by copying and modifying base design parameters, significantly reducing the time required for design exploration while maintaining control through constraint settings.
2Adaptability or versatility
If multiple design iterations are created manually, then design exploration is thorough, but computational resources and time are excessively consumed
Solution Approach 1:
The generative design system dynamically adjusts design parameters and generates iterations based on performance feedback and constraint satisfaction. Rather than statically creating all possible variations, the system adaptively explores the design space by modifying parameters iteratively, reducing unnecessary computational effort while maintaining design versatility.
Solution Approach 2:
The system explores design variations by systematically changing parameters such as unit type, orientation, and configuration within defined constraints. This parameter-based approach allows thorough design exploration without manually creating every possible combination, efficiently utilizing computational resources to generate meaningful design alternatives.
3Reliability
If design constraints are strictly enforced, then design quality and code compliance are ensured, but design flexibility and creativity are limited
Solution Approach 1:
The patent segments design constraints into hierarchical categories (mandatory code requirements vs. project-specific preferences) and applies them at different levels of the generative process. This allows the system to strictly enforce critical building code constraints while maintaining flexibility in less critical areas, enabling creative exploration within compliant boundaries.
Data Source
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AI summary
A design engine automatically generates designs for multi-family housing projects that simultaneously meet local construction regulations while also meeting specific financial targets. The design engine includes a design analyzer, a site analyzer, a design generator, and a design evaluator. The design analyzer generates design trends based on a historical database of designs. The site analyzer generates design criteria based on relevant construction regulations. The design generator generates design options that reflect the design trends while also adhering to the construction regulations. The design evaluator then analyzes the design options and generates various design metrics. Based on the design metrics, the design generator generates additional design options that better meet the design criteria.