Load Distribution Modeling Using Regression-Based Load Segmentation
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Solution Overview
Problem
The development of load distribution systems, including whiffletrees, is time-consuming and prone to human error, leading to undesirable or unbalanced distribution of loads to a structure, especially as the number of load points increases, resulting in numerous unsuitable or unfeasible configurations.
Innovation Solution
A computing system employs a regression algorithm to segment load points into subgroups, using a weighted linear regression line and boundary lines to determine centers of force, allowing for the generation of a load distribution system model that can be refined by users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual design methods are used for load distribution systems, then design flexibility is maintained, but design time increases and human error occurs leading to unbalanced load distribution
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated computing system that uses regression algorithms to calculate optimal load distribution configurations. The system automatically determines member assignments and connection points based on mathematical models, eliminating human error while reducing design time through computational efficiency.
Solution Approach 2:
The computing system performs self-directed design optimization by automatically analyzing load profiles, segmenting load points, and determining optimal configurations without requiring iterative manual adjustments. The algorithm independently identifies the best distribution scheme based on the given parameters, making the design process self-correcting and efficient.
2Adaptability or versatility
If the number of load points increases, then load distribution capability improves, but design complexity increases leading to numerous unsuitable configurations
Solution Approach 1:
The patent segments the design process into distinct computational stages: receiving load profiles, segmenting load points using regression algorithms, determining centers of force, and assigning members. This systematic segmentation of the design process manages complexity by breaking down the overall problem into smaller, algorithmically solvable sub-problems, enabling the system to handle any number of load points efficiently.
Solution Approach 2:
The system dynamically adjusts design parameters based on the number and distribution of load points. The regression algorithm automatically modifies the configuration parameters (member assignments, connection points, distribution ratios) to optimize load distribution for any given set of load points, allowing the system to adapt to varying complexity levels without manual intervention.
Data Source
AI summary
A computing system performs a method for generating a model of a load distribution system. The computing system receives a respective location of each load point, a respective force to be applied at each load point, and a force direction. A load segmentation loop is performed over a plurality of iterations that includes: selecting an iteration group of load points from among the set of load points, determining a weighted linear regression line and a center of force for the iteration group, identifying a first subgroup of load points that are located on a first side of a boundary line, and identifying a second subgroup of load points that are located on a second side of the boundary line. The model of the load distribution system is generated based on the center of force, the first subgroup, and the second subgroup of one or more iterations of the loop.