# Research on Three-Axis Vibration Characteristics and Vehicle Axle Shape Identification of Cement Pavement Under Heavy Vehicle Loads Based on EMD–Energy Decoupling Method

## TL;DR

This study uses a MEMS-based system and signal processing to analyze pavement vibrations from heavy vehicles, identifying axle impacts and improving pavement health monitoring.

## Contribution

A novel method for accurate axle identification using EMD and STE analysis, overcoming signal noise in pavement vibration monitoring.

## Key findings

- Vertical (Z-axis) vibrations show the highest amplitude and frequency components under heavy vehicle loads.
- EMD and STE analysis effectively identify axle impacts and distinguish vibration components.
- The method enables accurate axle configuration recognition and improves pavement performance assessment.

## Abstract

This study develops a MEMS-based triaxial vibration monitoring system to capture dynamic pavement responses under heavy-duty vehicle loads. Using empirical mode decomposition (EMD) and short-time energy (STE) analysis, this research study identifies distinct energy peaks associated with axle impacts, with vertical (Z-axis) vibrations showing the highest amplitude and frequency components. A robust method for accurate axle identification is proposed, overcoming signal noise interference and offering significant contributions to pavement health monitoring and traffic management systems.

What are the main findings?
Vertical (Z-axis) vibrations have the highest amplitude and frequency components, highlighting their key role in pavement response.EMD and STE analyses effectively identify axle impacts and enable accurate axle configuration recognition.

Vertical (Z-axis) vibrations have the highest amplitude and frequency components, highlighting their key role in pavement response.

EMD and STE analyses effectively identify axle impacts and enable accurate axle configuration recognition.

What is the implication of the main finding?
The findings contribute to better pavement health monitoring and performance assessment under heavy vehicle loads.Provides a reliable method for axle identification, supporting infrastructure management and optimizing traffic flow.

The findings contribute to better pavement health monitoring and performance assessment under heavy vehicle loads.

Provides a reliable method for axle identification, supporting infrastructure management and optimizing traffic flow.

The structural integrity of cement concrete pavements, paramount for ensuring traffic safety and operational efficiency, faces mounting challenges from the escalating burden of heavy-duty vehicular traffic. Precise characterisation of pavement dynamic responses under such conditions proves indispensable for implementing effective structural health monitoring and early warning system deployment. This investigation examines the triaxial dynamic response characteristics of cement concrete pavement subjected to low-speed, heavy-duty vehicular excitations, employing data acquired through in situ field measurements. A monitoring system incorporating embedded triaxial MEMS accelerometers was developed to capture vibration signals directly within the pavement structure. Raw data underwent preprocessing utilising a smoothing wavelet transform technique to attenuate noise, followed by empirical mode decomposition (EMD) and short-time energy (STE) analysis to scrutinise the time–frequency and energetic properties of triaxial vibration signals. The findings demonstrate that heavy, slow-moving vehicles generate substantial triaxial vibrations, with the vertical (Z-axis) response exhibiting the greatest amplitude and encompassing higher dominant frequency components compared to the horizontal (X and Y) axes. EMD successfully decomposed the complex signals into discrete intrinsic mode functions (IMFs), identifying high-frequency components (IMF1–IMF3) associated with transient vehicular impacts, mid-frequency components (IMF4–IMF6) presumably linked to structural and vehicle dynamics, and low-frequency components (IMF7–IMF9) representing system trends or ambient noise. The STE analysis of the selected IMFs elucidated the transient nature of axle loading, revealing pronounced, localised energy peaks. These findings furnish a comprehensive understanding of the dynamic behaviour of cement concrete pavements under heavy vehicle loads and establish a robust methodological framework for pavement performance assessment and refined axle load identification.