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

Area of Science:

• Civil Engineering
• Transportation Engineering
• Materials Science

Background:

• Effective monitoring of Portland cement concrete (PCC) pavement is crucial for infrastructure maintenance and traffic management.
• Dynamic vehicle loading generates vibration responses in pavements that can be analyzed for detection and monitoring.
• Existing methods for vehicle detection may be complex or costly, necessitating simpler, efficient alternatives.

Purpose of the Study:

• To develop and validate a cost-effective method for automated vehicle detection on PCC pavements using embedded sensors.
• To optimize sensor placement for maximizing vibration signal capture.
• To assess the accuracy and efficiency of a proposed thresholding method for analyzing acceleration data.

Main Methods:

• Embedding micro-electro-mechanical systems (MEMS) accelerometer sensors within PCC pavement slabs.
• Recording pavement vibration signals under dynamic vehicle loading.
• Developing and applying a thresholding algorithm to acceleration time-domain data for vehicle detection.
• Investigating the impact of sensor placement locations and threshold values on detection performance.

Main Results:

• The proposed thresholding method achieved precision and recall rates exceeding 85% for automated vehicle detection.
• Optimal sensor placement was identified at the front or rear ends of pavement slabs to maximize vibration response.
• The method demonstrated a balance of simplicity and accuracy, without requiring complex signal denoising.
• Experimental validation confirmed the effectiveness of the approach for real-time detection.

Conclusions:

• Embedded MEMS accelerometers and a thresholding method offer a simple, accurate, and cost-effective solution for real-time vehicle detection on PCC pavements.
• Optimized sensor placement enhances the efficiency of this monitoring technique.
• This approach supports improved pavement performance monitoring, maintenance strategies, and traffic management.