Machine Learning Structure Design Automation
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Solution Overview
Problem
The existing structure design process is inefficient and complex, involving multiple professionals and reinventing itself for each new structure, with limited reuse of designs and data, and lacking effective methods for rapid optimization and convergence on optimal designs.
Innovation Solution
The implementation of computer-implemented systems and methods that utilize machine learning to assist in structure design choices, coordinate user efforts, learn from past optimizations, and compare designs to achieve rapid convergence on optimized states, while preventing invalid design choices and optimizing for weighted preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional structure design process is used with multiple professionals, then design quality and comprehensiveness are improved, but process complexity and time consumption increase significantly
Solution Approach 1:
The design process is segmented into distinct phases: data collection, machine learning model training, design generation, optimization, and validation. Each phase is handled by specialized components (data processing module, ML model, design engine, optimization algorithm), allowing complex design tasks to be divided into manageable, parallelizable units that can be executed efficiently by both human professionals and automated systems.
Solution Approach 2:
A machine learning model serves as an intermediary between design requirements and generated designs. The ML model processes input data (program, location, constraints) and produces optimized design recommendations, mediating the complex interactions between multiple professionals and enabling more coordinated, efficient design processes without sacrificing comprehensiveness.
2Adaptability or versatility
If traditional design process reinvents itself for each new structure, then design specificity and adaptability are improved, but time consumption and inefficiency increase
Solution Approach 1:
The system performs preliminary actions by collecting and storing design data, constraints, and optimization criteria in a database before the actual design process begins. Historical design information and best practices are pre-processed and made available for rapid retrieval during new design projects, enabling the system to start from a knowledgeable baseline rather than reinventing fundamentals for each structure.
Solution Approach 2:
The machine learning model dynamically adjusts design parameters based on input data characteristics and optimization objectives. By changing parameters such as design complexity, level of detail, and optimization criteria according to the specific project requirements, the system maintains adaptability while reducing time through automated parameter optimization rather than manual iteration.
3Productivity
If limited reuse of designs and data is practiced, then design originality and creativity are improved, but productivity and efficiency decrease
Solution Approach 1:
The system creates and utilizes digital copies of design data, constraints, and optimization results stored in a database. Historical designs and their associated metadata are copied and made accessible for reference during new projects, enabling rapid reuse of validated design patterns and avoiding redundant work while maintaining the ability to customize for specific requirements.
Solution Approach 2:
The database and machine learning model serve universal functions across multiple design projects. The same infrastructure for data collection, storage, and optimization can be applied to different types of structures and design scenarios, providing multi-functional support that improves productivity without requiring separate systems for each design task.
4Manufacturing precision
If manual design optimization is performed, then design customization and precision are improved, but time consumption and labor requirements increase
Solution Approach 1:
Manual optimization processes are replaced with automated machine learning algorithms and optimization algorithms that computationally search for optimal designs. The system substitutes human iterative adjustment with automated evaluation of design alternatives against multiple criteria, achieving comparable or superior precision while dramatically reducing time consumption through parallel computation and intelligent search strategies.
Data Source
AI summary
Computational systems and methods are disclosed that learn about and assist with appropriate structure-based design choices. The systems and methods autonomously explore design states, and suggest to a user one or more optimize design states. Constraints are provided to limit exploration to valid design states. Systems and methods are disclosed that assist groups of users with coordinating their efforts in producing a cohesive design. Systems and methods are disclosed for learning from past optimizations in order to provide more rapid convergence on an optimized design, avoid local maxima and other hurdles to optimization, avoid undesired optimizations, and so on.