Generative Design Engine for Architecture
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Solution Overview
Problem
Traditional architectural design methods lack the ability to quantitatively assess design objectives and provide hard data for stakeholders, leading to potential inadequacies in meeting design criteria and insufficient analysis of design options.
Innovation Solution
A computer-implemented method using a design engine that generates a spectrum of design options based on constraints and occupant preferences, analyzing each option to produce metrics that quantify how well the design meets criteria, and iteratively improves these options through evolutionary processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional qualitative design management is used, then design process simplicity is maintained, but design objective fulfillment cannot be determined
Solution Approach 1:
The patent replaces the traditional qualitative, intuition-based design evaluation system with a computational algorithmic system. The design evaluation module uses automated algorithms to quantitatively assess design options against multiple objectives, substituting human judgment with systematic computational analysis that provides deterministic measurement of design fulfillment.
Solution Approach 2:
The patent introduces a design evaluation module as an intermediary between the design generation process and stakeholder decision-making. This module acts as a mediator that translates complex design geometries into quantifiable metrics and scores, enabling objective comparison of design options without requiring stakeholders to directly analyze complex architectural details.
2Loss of information
If traditional design methods are used, then design process simplicity is maintained, but hard data for stakeholder analysis is not generated
Solution Approach 1:
The patent implements continuous generation of design options with automated evaluation throughout the design process. Rather than producing a single design outcome, the system continuously generates and evaluates multiple design options, maintaining an ongoing flow of quantitative data that stakeholders can analyze to inform decision-making.
Solution Approach 2:
The design evaluation module operates autonomously to generate comprehensive analysis data without requiring manual intervention. The system automatically computes metrics, scores, and performance indicators for each design option, providing self-service data generation that eliminates the need for separate manual analysis processes.
3Adaptability or versatility
If multiple design objectives are managed qualitatively, then design flexibility is maintained, but trade-offs among objectives cannot be analyzed
Solution Approach 1:
The patent transforms design objectives from qualitative concepts into quantifiable parameters with associated weights and targets. The design evaluation module uses these parameterized objectives to systematically analyze trade-offs, allowing stakeholders to adjust objective priorities and observe how different weightings affect overall design scores and recommendations.
4Measurement precision
If deterministic assessment of design objectives is implemented, then design objective fulfillment is accurately measured, but design evaluation complexity increases
Solution Approach 1:
The patent segments the design evaluation process into distinct modular components: geometry generation, constraint validation, objective assessment, and scoring. Each module handles a specific aspect of evaluation independently, making the overall complex system manageable through functional decomposition and enabling targeted improvements to specific evaluation aspects.
Data Source
AI summary
A design engine includes a geometry module and a metric module that interoperate to generate optimal design options. The geometry module initially generates a spectrum of design options for a structure based on project constraints and design criteria set forth by potential occupants of the structure. The metric module then analyzes each design option and generates, for any given design option, a set of metrics that indicates how well the given design option meets the design criteria. The geometry module then generates additional design options in an evolutionary manner to improve the metrics generated for subsequent design options.