Structural Grid Generation for Arbitrarily Shaped Building Design
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Solution Overview
Problem
Conventional CAD applications are inefficient in exploring the overall design space for structural systems of buildings, leading to suboptimal finalized designs due to time-consuming finite element analysis and conservative decision-making, especially in complex buildings.
Innovation Solution
A computer-implemented method that breaks down the design optimization problem into simpler constituent problems, using generative design techniques to systematically explore and optimize structural system designs for buildings, incorporating a structural design application that includes gravity design, grid generation, and iterative optimization applications to enhance design efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional CAD applications are used to generate structural system designs, then the design process can be completed with standard tools, but the exploration of design space is inefficient and time-consuming
Solution Approach 1:
The patent segments the structural system design into discrete components (columns, beams, slabs) with independent design variables. Each component can be optimized separately while maintaining overall structural integrity, enabling efficient exploration of design space through systematic variation of individual elements rather than analyzing entire structures at once.
Solution Approach 2:
The patent performs preliminary actions by pre-defining design constraints, objective functions, and initial design configurations before the optimization process begins. This includes establishing building codes, load requirements, and material properties in advance, which allows the optimization algorithm to focus computational resources on finding optimal solutions rather than validating basic structural requirements during each analysis iteration.
2Adaptability or versatility
If multiple baseline designs are generated and refined using conventional CAD, then design options can be explored, but the process becomes extremely time-consuming and only a handful of designs can be evaluated
Solution Approach 1:
The optimization system performs self-service by automatically generating, evaluating, and comparing multiple design options without requiring manual intervention for each design iteration. The system autonomously varies design parameters, executes finite element analyses, and selects optimal configurations based on predefined objective functions, eliminating the need for manual review and refinement of each baseline design.
Solution Approach 2:
The patent systematically varies multiple design parameters simultaneously (column spacing, beam dimensions, slab thickness, material properties) to generate a diverse set of baseline designs. This parameter variation approach allows exponential increase in design options while maintaining manageable computation time through automated evaluation, as each parameter change is processed algorithmically rather than requiring manual redesign.
3Loss of time
If conservative design decisions are made to reduce design time, then the finalized designs can be generated faster, but the designs may not be properly optimized for design objectives
Solution Approach 1:
The optimization system implements feedback mechanisms where the results of finite element analyses are automatically fed back into the optimization algorithm. This feedback loop allows the system to evaluate whether each design option meets performance criteria and adjust subsequent design iterations accordingly, ensuring that time-constrained designs still achieve optimal results by learning from previous analysis outcomes.
Solution Approach 2:
The patent replaces manual mechanical design processes with automated computational optimization algorithms. Instead of relying on engineer experience and iterative manual adjustments, the system uses mathematical optimization techniques to automatically find designs that minimize time while maximizing optimization of design objectives such as structural efficiency, material usage, and cost.
Data Source
AI summary
In various embodiments, a grid-based design application automatically generates a design for a structural system of a building. In operation, the grid-based design application generates a structural grid based on a region within a computer-aided design of the building. Subsequently, the grid-based design application applies the structural grid to the region to generate a gridded region. The grid-based design application computes a set of spanning directions based on the gridded region. The grid-based design application then generates at least a portion of the design for the structural system based on the set of spanning directions and the gridded region.