Method, device and equipment for testing working performance of asphalt mixture and storage medium

By using a multimodal sensor array and dynamic sliding time window analysis, combined with visual gloss, the construction performance window of asphalt mixtures can be accurately determined, solving the problem of difficulty in judging the construction performance of new materials and achieving high-precision construction control.

CN121679002BActive Publication Date: 2026-05-26XIYUEFA INT ENVIRONMENTAL PROTECTION NEW MATERIALS CO LTD +2
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
XIYUEFA INT ENVIRONMENTAL PROTECTION NEW MATERIALS CO LTD
Filing Date
2026-02-09
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies cannot accurately determine the construction performance window of new materials such as modified asphalt and porous asphalt, making it difficult to guarantee construction quality.

Method used

A multimodal sensor array is used to collect torque, temperature, and visual signals. Torque data is analyzed through a dynamic sliding time window, and the height of the mixing paddle is adaptively adjusted in combination with the visual gloss attenuation coefficient to accurately determine the upper and lower critical temperature points of the construction performance window.

Benefits of technology

This improves the accuracy of testing the workability of asphalt mixtures, ensures construction quality, and meets the construction control requirements of new materials.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application discloses a method, apparatus, equipment, and storage medium for testing the workability of asphalt mixtures, relating to the field of mixture performance testing technology, and aiming to solve the problem of low testing accuracy of asphalt mixture workability. The method includes: firstly, setting a test temperature range based on the material formulation and construction environment; then, using a multimodal sensor array to collect torque, temperature, and visual signals, and adaptively adjusting the mixing paddle height based on abnormal signal types to ensure test stability; after the signals return to normal, acquiring multi-source time-series data including torque, temperature, and visual data, using a dynamically sliding time window with adaptive window size, calculating the torque median baseline, and generating a target confidence interval with dynamically adjusted boundaries; finally, by comprehensively judging the trend of the gloss decay coefficient in the visual data and the torque median or torque median baseline, determining the lower and upper critical temperature points, thereby determining the workability window.
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