Base Asphalt Selection via Grey Relational Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for selecting base asphalt for rubber asphalt lack systematic analysis of chemical components' influence on performance, leading to inaccurate and unreasonable selection processes.
Innovation Solution
A selection method based on grey relational analysis is employed to determine the affecting factors of base asphalt chemical components on rubber asphalt performance, using parameters like saturate, aromatic, resin, and asphaltene mass percentages, and performance indicators such as softening point, penetration, ductility, and viscosity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comparative analysis of base asphalt and modified asphalt on molecular weight distribution, thermal decomposition temperature, and functional group change is adopted, then indirect study of the influence of changes in asphalt components on the performance of modified asphalt is achieved, but statistical methods are not used to analyze major affecting factors
Solution Approach 1:
The patent transforms the selection process from qualitative comparative analysis to quantitative statistical analysis by changing the parameter evaluation method. Grey relational analysis is applied to convert multiple chemical component parameters (saturate, aromatic, resin, asphaltene contents) and performance parameters into a unified evaluation system, enabling accurate identification of major affecting factors through mathematical modeling rather than traditional indirect comparison methods
Solution Approach 2:
The patent replaces the traditional mechanical/comparative analysis system with a statistical information processing system. By using grey relational analysis and correlation coefficients, the method substitutes direct experimental comparison with mathematical statistical evaluation, allowing systematic identification of which chemical components most significantly influence rubber asphalt performance without requiring complex experimental setups
2Measurement precision
If chemical components of base asphalt are analyzed using GPC, TLC/FID, and FTIR, then indirect characterization of chemical composition and structure is achieved, but the relation between components and performance remains complicated and difficult to classify
Solution Approach 1:
The patent creates a universal evaluation framework that works across different base asphalt types (crude asphalt, oxidized asphalt, modified asphalt) by applying grey relational analysis to standardized sets of chemical and performance parameters. This multi-functional approach allows the same statistical methodology to classify and evaluate diverse asphalt types systematically, replacing the need for separate analysis methods for each asphalt category
Solution Approach 2:
The patent transforms complex chemical composition data from multiple characterization methods into a standardized parameter set suitable for statistical analysis. By converting GPC, TLC/FID, and FTIR results into quantifiable chemical component percentages (saturate, aromatic, resin, asphaltene) and correlating them with performance parameters through grey relational analysis, the method enables systematic classification while maintaining the precision benefits of advanced characterization techniques
3Ease of operation
If traditional selection methods are used, then the selection process is simple, but the selection accuracy and reasonability of base asphalt for rubber asphalt are insufficient
Solution Approach 1:
The patent establishes a feedback mechanism where performance test results (penetration, ductility, softening point, viscosity, segregation resistance) are fed back to evaluate and rank chemical component parameters. Through grey relational analysis, the method determines which chemical components most strongly correlate with desired performance outcomes, creating an iterative selection process that continuously improves accuracy by learning from performance data while maintaining operational simplicity through automated statistical ranking
Data Source
AI summary
The present disclosure provides a selection method of base asphalt for rubber asphalt based on grey relational analysis, which belongs to the technical field of selection methods of base asphalt. The selection method includes the following steps: determining factors affecting the performance of rubber asphalt and rubber asphalt performance evaluation indicators; ranking the factors affecting the performance of rubber asphalt according to respective affecting degrees thereof on each of the rubber asphalt performance evaluation indicators by using a grey relational method; and determining affecting factors of chemical components of base asphalt to the performance of rubber asphalt, and selecting base asphalt according to the affecting factors. The present disclosure uses the grey relational analysis method to systematically study the influences of chemical components of base asphalt on the performance of rubber asphalt.