3D Rut Morphology Generation via Feature Extraction
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Solution Overview
Problem
Existing methods for digital modeling of ruts in asphalt pavements are inefficient due to redundant and invalid information in mass detection data, and they fail to accurately capture the three-dimensional morphology of ruts.
Innovation Solution
An automatic generation method for three-dimensional morphology of ruts, which involves constructing a standard rut cross-section representation model, classifying ruts into nine types based on curve shapes, selecting feature points according to geometric fluctuation features, connecting these points to fit a cross-sectional curve based on deformation evolution laws, and fusing multiple reconstructed cross-sections to generate a three-dimensional model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mass detection data is used for digital modeling of ruts, then comprehensive information is obtained, but redundant and invalid information occupies large storage space
Solution Approach 1:
The patent extracts key geometric features (seven characteristic points: two edge points, two raised points, three valley points) from mass detection data to represent rut cross-sections. This extraction process removes redundant and invalid information while preserving essential geometric characteristics, thereby reducing storage space requirements while maintaining modeling accuracy.
Solution Approach 2:
The patent applies local quality by selectively processing different parts of the detection data. Instead of uniformly processing all data points, it identifies and extracts specific characteristic points that locally represent the most important geometric features of rut cross-sections, optimizing the balance between data reduction and feature preservation.
2Shape
If cross-sectional data is used for rut modeling, then two-dimensional features are captured, but three-dimensional morphology cannot be reconstructed
Solution Approach 1:
The patent transitions from two-dimensional cross-sectional data to three-dimensional rut morphology by sequentially connecting multiple cross-sections along the longitudinal direction. The seven characteristic points in each cross-section are extended into three-dimensional space, enabling reconstruction of the rut's longitudinal development and creating a complete 3D digital model from 2D measurements.
Solution Approach 2:
The patent segments the rut structure into multiple cross-sectional slices along the longitudinal direction, with each slice represented by seven characteristic points. By processing and connecting these segmented cross-sections sequentially, the method reconstructs the overall three-dimensional morphology while managing modeling complexity through systematic segmentation.
3Productivity
If parametric modeling is used for ruts, then modeling efficiency is improved, but geometric fluctuation features are neglected
Solution Approach 1:
The patent uses parameter changes by defining seven characteristic points with specific geometric parameters (coordinates, elevations) that capture the essential features of rut cross-sections. This parametric representation improves modeling efficiency while maintaining geometric accuracy, as the seven points are strategically selected to represent key features without requiring excessive data processing.
Solution Approach 2:
The patent applies local quality by ensuring that the seven characteristic points locally represent the most important geometric features of each cross-section (edges, raised portions, valleys). This selective representation maintains geometric feature accuracy while improving modeling efficiency compared to using all detection data points.
Data Source
AI summary
An automatic generation method for three-dimensional morphology of ruts in asphalt pavements comprises: constructing a standard rut cross-section representation model, classifying ruts, acquiring detection data of a rutted section of an asphalt pavement, selecting different rut cross-sections at equal intervals in a longitudinal direction of the pavement, denoising and smoothening the rut cross-sections, selecting feature points according to geometric fluctuation features of the rut cross-sections, connecting the feature points and fitting a cross-sectional curve based on an evolution law of rut deformation, and fusing multiple reconstructed rut cross-sections to realize automatic generation and visualization of three-dimensional morphology of a rut.