Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement

Monitoring the vibration response of Portland cement concrete (PCC) pavement under dynamic vehicle loading is critical for road maintenance and traffic analysis. This study embedded micro-electro-mechanical systems (MEMS) accelerometer sensors in PCC pavement to capture vibration signals induced by vehicles. A thresholding method is proposed to automate vehicle detection by analyzing acceleration time-domain data, achieving precision and recall rates exceeding 85%. The study also explored various sensor placement locations and different threshold values for acceleration time-domain signals. Sensor placement optimization revealed that positioning sensors at the front or rear ends of pavement slabs maximizes vibration response, enabling low-cost and efficient detection. Experimental results demonstrated that the proposed method balances simplicity and accuracy, eliminating the need for complex denoising processes. This approach provides a cost-effective solution for real-time vehicle detection and enhances pavement performance monitoring, supporting improved maintenance and traffic management strategies.

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Publication Details

Journal
Sensors
Published
2025-05-04
DOI
https://doi.org/10.3390/s25092898
Citations
6
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
5.72

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article

Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement

Ya Wei, Junmin Li, Libin Han, Shuo Pan et al.
6 citations
Sensors
Infrastructure Maintenance and Monitoring
5.72
article

Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement

Ya Wei, Junmin Li, Libin Han, Shuo Pan, Linghui Kong, Chul-Ki Chang
article en
6 citations

Abstract

Monitoring the vibration response of Portland cement concrete (PCC) pavement under dynamic vehicle loading is critical for road maintenance and traffic analysis. This study embedded micro-electro-mechanical systems (MEMS) accelerometer sensors in PCC pavement to capture vibration signals induced by vehicles. A thresholding method is proposed to automate vehicle detection by analyzing acceleration time-domain data, achieving precision and recall rates exceeding 85%. The study also explored various sensor placement locations and different threshold values for acceleration time-domain signals. Sensor placement optimization revealed that positioning sensors at the front or rear ends of pavement slabs maximizes vibration response, enabling low-cost and efficient detection. Experimental results demonstrated that the proposed method balances simplicity and accuracy, eliminating the need for complex denoising processes. This approach provides a cost-effective solution for real-time vehicle detection and enhances pavement performance monitoring, supporting improved maintenance and traffic management strategies.

SensorsVol. 25(9)
Beijing University of Technology (CN), Tsinghua University (CN)
National Key Research and Development Program of China
Sustainable cities and communities
Openalex Percentile: Top 4%
Infrastructure Maintenance and Monitoring
5.72
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Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement — Ya Wei, Junmin Li, et al. · Sensors (2025) | TGRS Research Map | TGRS