Highway pavement friction performance detection method based on smart sensors

CN122133079BActive Publication Date: 2026-06-30INNER MONGOLIA HIGHWAY ENG CONSULTANTS SUPERVISION CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
INNER MONGOLIA HIGHWAY ENG CONSULTANTS SUPERVISION CO LTD
Filing Date
2026-04-28
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

In traditional methods for detecting the friction performance of highway pavements, sparse coding algorithms do not incorporate the projection characteristics of tribological feature subspaces, resulting in low accuracy in vibration feature extraction, ineffective feature fusion of multi-source data, biased friction coefficient estimation results, difficulty in generating a complete spatial distribution map of friction coefficients, and a lack of accurate data support for subsequent risk level classification and maintenance recommendations.

Method used

A multimodal intelligent sensor array is used to collect road surface data. Vibration feature vectors are extracted through an improved sparse coding algorithm. Combined with three-dimensional topography reconstruction and temporal feature fusion, texture feature maps and environmental state vectors are generated. These are input into a multi-source feature fusion network and a deep convolutional neural network is used to evaluate friction performance, generate friction coefficient estimates and their spatial distribution maps, and generate risk level assessment results and maintenance recommendations based on decision logic.

Benefits of technology

It achieves high-precision detection of road surface friction performance, generates a complete spatial distribution map of friction coefficient, provides accurate risk level assessment and targeted maintenance suggestions, and improves the accuracy and comprehensiveness of the test results.

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Abstract

This invention relates to the field of highway pavement inspection technology, specifically a method for detecting the friction performance of highway pavements based on intelligent sensors. The method includes: collecting vibration spectra, surface texture depth sequences, and environmental meteorological readings using a multimodal intelligent sensor array deployed on the highway pavement to form a raw pavement dataset. A sparse coding algorithm optimized based on the projection characteristics of tribological feature subspaces is used to extract vibration feature vectors. These vectors are then reconstructed in three dimensions and fused with temporal features to obtain texture feature maps and pavement environmental state vectors. Multiple features are input into a multi-source fusion network to generate a comprehensive state representation. Finally, a deep convolutional neural network model decodes and outputs estimated friction coefficients and spatial distribution maps. Based on this, risk level assessments and maintenance recommendations are generated and uploaded to a management platform. This method accurately preserves friction-related features, enables collaborative analysis of multi-source data, fully reflects the spatial distribution of pavement friction, and improves the accuracy and completeness of the detection results.
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