Deflection Estimation for Columnar Structures with Missing Data
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Solution Overview
Problem
Existing techniques for estimating deflection values of columnar structures are inaccurate when missing portions occur in the solid data set, as the calculated deflection values deviate significantly based on the position and size of the missing portion.
Innovation Solution
A system comprising computation units to calculate deflection and missing portion extent, assess accuracy for various missing portion patterns, and estimate deflection accuracy using Root Mean Squared Errors (RMSE) to correct deflection values based on the extent and pattern of missing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deflection estimation is performed using existing techniques on solid data sets with missing portions, then processing can be completed, but the deflection values greatly deviate from the true values depending on the position and size of the missing portion
Solution Approach 1:
The system performs preliminary classification of missing portion patterns and pre-calculates accuracy assessment indicators for various missing portion scenarios. By preparing accuracy evaluation data in advance for different missing portion patterns, the system can quickly assess and correct deflection values without performing complex recalculations when missing portions are detected.
Solution Approach 2:
The system changes the approach from directly calculating deflection values to calculating accuracy assessment indicators as an intermediate parameter. By introducing this intermediate parameter that evaluates the impact of missing portions, the system can adjust and correct the final deflection values based on the assessed accuracy level, thereby improving measurement precision.
2Ease of operation
If the extent of missing portion increases in the solid data set, then data collection becomes easier, but the accuracy of deflection calculation decreases significantly
Solution Approach 1:
The system introduces feedback by calculating accuracy assessment indicators that evaluate the impact of missing portions on deflection calculations. This feedback mechanism allows the system to determine the reliability of calculated deflection values and apply appropriate corrections or adjustments based on the assessed accuracy level, ensuring reliable results even when data collection is performed under various conditions.
3Measurement precision
If comprehensive solid data sets are collected to ensure accurate deflection calculation, then measurement precision improves, but processing load and data volume increase
Solution Approach 1:
The system extracts and classifies only the essential information about missing portions (pattern types and extent) from the complete solid data sets. By separating the critical accuracy assessment parameters from the full data set, the system can evaluate and correct deflection values using only the necessary information, significantly reducing processing load while maintaining measurement precision.
Data Source
AI summary
Even when a missing portion occurs in a solid data set on a columnar structure, an estimator for a deflection value and an accuracy of the deflection value are correctly estimated according to an extent of the missing portion and the like. A measurement accuracy estimation unit (15) is included that: calculates a deflection of a columnar structure and an extent of a missing portion, from a solid data set on the columnar structure; calculates an accuracy assessment indicator for the deflection that is acquirable when a plurality of missing portion patterns occur on a virtual basis, based on a plurality of solid data sets in each of which the calculated extent of the missing portion is smaller than a preset threshold value, the accuracy assessment indicator being calculated for each missing portion pattern; and calculates an accuracy of the deflection calculated from the solid data set, based on the calculated accuracy assessment indicator for each missing portion pattern, and based on the calculated extent of the missing portion in the solid data set.


