AI-Driven Parametric Building Design With Manufacturer Data
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Solution Overview
Problem
Existing computer-aided design systems in the architecture and construction industry lack integrated artificial intelligence capabilities for real-time optimization across multiple technical constraints, leading to inefficiencies in design cycles, computational errors, and imprecise cost estimations, particularly in mass timber construction.
Innovation Solution
An automated computer-aided design system that integrates generative building design and manufacturing integration, utilizing AI/machine learning algorithms for real-time decision-making, advanced design software, and direct integration with manufacturer product databases to automate design optimization, compliance verification, and documentation generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual computation and verification processes are used for material selection, cost estimation, and regulatory compliance, then design flexibility is maintained, but processing time increases significantly and accuracy decreases
Solution Approach 1:
The patent replaces manual computation and verification processes with an automated computer-aided design system that integrates artificial intelligence algorithms. The system automatically performs material selection optimization, cost estimation, and regulatory compliance verification, eliminating the need for manual mechanical computation while significantly improving both accuracy and reducing design cycle time.
Solution Approach 2:
The automated CAD system performs self-verification of design parameters, automatically checking compliance with building codes and regulations, and independently optimizing material selections based on multiple constraints. This self-service capability eliminates the need for separate manual verification steps while maintaining high accuracy standards.
2Adaptability or versatility
If disconnected computational processes are used in existing CAD systems, then system simplicity is maintained, but optimization across multiple technical constraints becomes sub-optimal
Solution Approach 1:
The patent merges previously disconnected computational processes into a single integrated automated CAD system. The system combines structural analysis, cost estimation, material optimization, and regulatory compliance verification into one unified platform that can simultaneously optimize across all these parameters, achieving superior multi-parameter optimization while managing complexity through integrated architecture.
Solution Approach 2:
The automated CAD system performs multiple functions within a single platform: generative design, structural optimization, cost estimation, material selection, compliance verification, and manufacturing specification generation. This multi-functional approach enables comprehensive optimization across all technical constraints without requiring separate specialized tools.
3Reliability
If real-time AI-based optimization is implemented across multiple technical constraints, then design quality and compliance accuracy improve, but computational resource requirements increase
Solution Approach 1:
The system performs preliminary filtering and pre-processing of design parameters before applying complex AI optimization algorithms. By pre-organizing material data, building code requirements, and constraint parameters, the system reduces the computational burden during real-time optimization while maintaining high accuracy in compliance verification and design quality.
Solution Approach 2:
The automated CAD system maintains continuous optimization processes that run seamlessly as design parameters change, rather than performing discrete computational bursts. This continuous action allows the system to efficiently track optimal solutions as constraints evolve, reducing overall computational resource requirements while maintaining high reliability in compliance verification.
Data Source
AI summary
A computer-aided system and associated method for automated building design includes receiving input parameters and generating a three-dimensional parametric computer model of a building frame and populating the three-dimensional computer building model with generic and manufacturer-specific products by accessing internal and external data sources. The populated computer model undergoes real-time simulation to analyze regulatory compliance, verify engineering requirements including load calculations, and evaluate cost considerations. Based on simulation results, the method generates output documents including requests for proposals (RFP documents) for contractors, requests for quotations (RFQs) for vendors, and regulatory compliance documentation. These documents are transmitted to respective stakeholders through a secure data exchange protocol. The method further includes receiving responses to the generated RFP documents/RFQs.