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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2898 |
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| author | Congyi Chang Linghui Kong Libin Han Junmin Li Shuo Pan Ya Wei |
| author_facet | Congyi Chang Linghui Kong Libin Han Junmin Li Shuo Pan Ya Wei |
| author_sort | Congyi Chang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-456481e782fd470a918fedfbe103eb31 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-456481e782fd470a918fedfbe103eb312025-08-20T02:31:08ZengMDPI AGSensors1424-82202025-05-01259289810.3390/s25092898Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC PavementCongyi Chang0Linghui Kong1Libin Han2Junmin Li3Shuo Pan4Ya Wei5Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing 100084, ChinaKey Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing 100084, ChinaKunming International Aviation Hub Engineering Construction Headquarters, Yunnan Airport Group Co., Ltd., Kunming 650200, ChinaKunming International Aviation Hub Engineering Construction Headquarters, Yunnan Airport Group Co., Ltd., Kunming 650200, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, ChinaKey Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing 100084, ChinaMonitoring 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.https://www.mdpi.com/1424-8220/25/9/2898multiple pavement slabsthresholding methodsensor placement locationsdetection precision and recallmaximum vibration response |
| spellingShingle | Congyi Chang Linghui Kong Libin Han Junmin Li Shuo Pan Ya Wei Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement Sensors multiple pavement slabs thresholding method sensor placement locations detection precision and recall maximum vibration response |
| title | Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement |
| title_full | Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement |
| title_fullStr | Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement |
| title_full_unstemmed | Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement |
| title_short | Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement |
| title_sort | vibration analysis and vehicle detection by mems acceleration sensors embedded in pcc pavement |
| topic | multiple pavement slabs thresholding method sensor placement locations detection precision and recall maximum vibration response |
| url | https://www.mdpi.com/1424-8220/25/9/2898 |
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