Method for detecting friction performance of highway pavement based on intelligent sensor

By combining a multimodal intelligent sensor array with a deep convolutional neural network, the problems of feature loss and insufficient fusion in traditional road surface friction performance detection are solved, enabling accurate detection and risk assessment of highway road surface friction performance, and generating detailed friction coefficient distribution maps and maintenance recommendations.

CN122133079AActive Publication Date: 2026-06-02INNER MONGOLIA HIGHWAY ENG CONSULTANTS SUPERVISION CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
INNER MONGOLIA HIGHWAY ENG CONSULTANTS SUPERVISION CO LTD
Filing Date
2026-04-28
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

In traditional methods for detecting the friction performance of highway pavements, the vibration signal processing algorithm is not optimized by combining tribological characteristics and projection characteristics, resulting in the loss of key friction features. Multi-source pavement data are not fused, leading to biases in the friction coefficient estimation results. It is difficult to generate a complete spatial distribution map of the friction coefficient, and subsequent risk level classification and maintenance recommendations lack accurate data support.

Method used

A multimodal intelligent sensor array is used to collect road surface data. Vibration feature vectors are extracted by an improved sparse coding algorithm. A comprehensive state representation is generated by combining three-dimensional topography reconstruction and temporal feature fusion. The data is then input into a multi-source feature fusion network. A deep convolutional neural network is used to decode the friction coefficient and its spatial distribution map. Based on decision logic, a risk level assessment and maintenance recommendations are generated.

Benefits of technology

It achieves a complete characterization of road surface friction performance, generates an accurate spatial distribution map of friction coefficient, provides precise risk level assessment and targeted maintenance recommendations, and improves the accuracy and comprehensiveness of the detection.

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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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