Milling Particle Gradation Prediction Model for Asphalt Recycling
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Solution Overview
Problem
The existing methods for in-situ cold recycling of asphalt pavements require repetitive testing to determine operational parameters, leading to lengthy construction times and potential waste of asphalt material, as they often necessitate multiple road segment tests to achieve the required recycled particle gradation.
Innovation Solution
A method and device for obtaining a milling particle gradation prediction model by sieving test particles, calculating characteristic parameters of the milling rotor's cutting graph, and establishing a functional relation between sieve residual mass ratios and operational parameters, allowing for the prediction of recycled particle gradation under different construction conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If repetitive testing is conducted to determine operational parameters for in-situ cold recycling, then the required recycled particle gradation can be achieved, but construction time increases and asphalt material is wasted
Solution Approach 1:
The patent establishes a prediction model for milling particle gradation before actual construction begins. The model uses operational parameters (rotational speed, forward speed, milling depth) to predict the resulting particle gradation, allowing engineers to determine optimal parameters in advance through calculation rather than repetitive field testing. This preliminary action eliminates the need for multiple test passes during construction.
Solution Approach 2:
The patent creates a virtual model that replicates the complex relationship between milling parameters and particle gradation. Instead of physically testing different parameter combinations on the road, the prediction model copies the milling process outcomes through mathematical relationships derived from cutting graph characteristic parameters, enabling parameter optimization without material waste.
2Manufacturing precision
If repetitive testing is conducted to determine operational parameters for in-situ cold recycling, then the required recycled particle gradation can be achieved, but asphalt material is wasted
Solution Approach 1:
The prediction model allows operational parameters to be determined before construction starts. By calculating the characteristic parameters of the cutting graph and establishing functional relationships with sieve residual mass ratios, the optimal milling parameters can be identified in advance, preventing material waste from ineffective test passes.
Solution Approach 2:
The patent uses a virtual prediction model to replicate the milling process outcomes without physically consuming asphalt material. The model copies the relationship between cutter tooth arrangement, operational parameters, and particle gradation, enabling parameter optimization through calculation rather than material-intensive testing.
3Manufacturing precision
If multiple road segment tests are performed to achieve required recycled particle gradation, then accurate gradation can be obtained, but manpower resources are consumed
Solution Approach 1:
The patent replaces physical testing with a virtual prediction model that copies the milling process outcomes. The model uses the characteristic parameters of the cutting graph (derived from cutter tooth arrangement and operational parameters) to predict particle gradation, eliminating the need for manual field testing and reducing manpower requirements.
Solution Approach 2:
The patent substitutes mechanical field testing with a computational prediction system. Instead of physically performing multiple mill passes and conducting sieve analyses to determine particle gradation, the system uses mathematical relationships and characteristic parameter calculations to predict outcomes, replacing labor-intensive mechanical processes with automated computation.
Data Source
AI summary
The present disclosure provides a method for obtaining a milling particle gradation prediction model, a prediction method and a device. The method includes: sieving a plurality of groups of test particles obtained by a plurality of milling tests to obtain a sieve residual mass ratio corresponding to each of a plurality of sieves after performing milling tests on an asphalt layer; calculating a characteristic parameter of a cutting graph of the milling rotor according to arrangement of cutter teeth of the milling rotor, a rotational speed, a forward speed, and a milling depth of the milling rotor; establishing, by regression analysis, a functional relation between the sieve residual mass ratio corresponding to each sieve and the characteristic parameter according to the rotational speed, the forward speed and the milling depth; and normalizing the functional relation to obtain the milling particle gradation prediction model.