Dynamic Control Points for Infrastructure Curve Optimization
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Solution Overview
Problem
Progressive optimizers in infrastructure design often deviate from the optimization target due to the use of static control points, which can lead to suboptimal results and constrain subsequent recursive optimization levels.
Innovation Solution
The implementation of dynamic control points, which are generated by sampling and filtering static control points based on a control point sampling interval and ground conditions, allows for improved curve optimization without deviating significantly from the initial alignment or profile, enabling more efficient progressive optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static control points are used in progressive optimization, then the optimization process can be constrained to maintain alignment with ground elevations, but the control points may deviate greatly from the optimization target and lead to suboptimal results
Solution Approach 1:
The patent applies dynamics by transitioning from static control points to dynamic control points that automatically adjust their positions during the optimization process. The dynamic control points are generated by sampling the initial curve at multiple intervals and selectively using sampled points based on optimization progress, allowing the control points to adaptively maintain alignment with ground elevations while converging toward the optimal solution without manual intervention.
2Manufacturing precision
If control points are constrained early in optimization, then the optimization target can be maintained, but subsequent recursive optimization levels may be constrained away from the optimized solution
Solution Approach 1:
The patent segments the optimization process into multiple recursive levels, where each level operates with a subset of control points generated from the initial curve. By dividing the control point set into multiple filtered control points at different sampling intervals, the system allows each optimization level to focus on specific segments of the curve, enabling progressive refinement without being constrained by fixed control points from earlier levels.
Solution Approach 2:
The dynamic control points are regenerated at each recursive optimization level based on the current state of the curve and ground elevation data. This dynamic regeneration allows the control points to adapt to the optimization progress made in previous levels, maintaining flexibility and preventing the optimization process from being constrained away from the optimal solution while still maintaining precision through systematic sampling and filtering.
3Adaptability or versatility
If human users manually configure alignment on terrain, then design flexibility and adaptability are maintained, but errors and inefficiencies increase
Solution Approach 1:
The system implements self-service by automatically generating and updating control points based on the initial curve and ground elevation data without requiring manual user intervention. The dynamic control points are autonomously sampled, filtered, and adjusted during the optimization process, allowing the system to self-correct errors and maintain design accuracy while preserving adaptability through automated adaptation to terrain conditions.
Data Source
AI summary
A method for generating a dynamic control point for generating an optimized curve includes determining, at a processor(s), an initial curve. A control point sampling interval and a condition are set by the processor(s). Multiple static control points are determined. The static control points are sampled based on the control point sampling interval. A sampled static control point is selected from the plurality of static control points based on the sampled static control point satisfying the condition. The sampled static control point is stored in a set of filtered control points. The set of filtered control points is sampled. A sampled filtered control point is selected based on the control point sampling interval. A dynamic control point is generated based on the sampled filtered control point.