PaveVibe is an innovative project designed to measure the quality of pavement surfaces and identify defects in a georeferenced manner using accelerometer and GPS data. By leveraging the capabilities of an ESP32 microcontroller, PaveVibe collects precise movement and location data, enabling detailed analysis of pavement conditions and the pinpointing of areas in need of maintenance.

The main objective of PaveVibe is to create a portable, efficient device that can detect and record pavement defects with precise geolocation information. This allows for a comprehensive mapping of pavement quality over large areas, providing valuable data for maintenance planning and infrastructure improvement efforts.

• Real-time Clock Synchronization: At startup, PaveVibe synchronizes the ESP32's internal RTC with GPS time. This ensures that even in the absence of GPS signals, the system retains accurate time information, which can be synchronized with smartphone apps like GPS Logger for enhanced data integrity.
• Data Integration: The repository includes a Jupyter Notebook (tratamento e merge.ipynb) designed to merge the data collected by PaveVibe from the SD card with .gpx files from GPS Logger. This process automatically handles missing columns and merges entries based on date and time, followed by interpolation and expansion of GPS data for comprehensive analysis.
• Advanced Data Filtering: Also featured is a Notebook (Filtros_SMA_Butterworth_COMP.ipynb) containing models for filtering accelerometer data using SMA (Simple Moving Average) and Butterworth filters, ensuring clean and reliable data for analysis.

To get started with PaveVibe, follow these steps:

1. Hardware Setup: Assemble your ESP32 module and sensors according to the provided circuit diagram.
2. Software Setup: Flash the ESP32 with the code found in the PaveVibe directory of this repository.
3. Data Collection: Begin collecting pavement data. Ensure GPS Logger is running on your smartphone to gather GPS data simultaneously.
4. Data Processing: Use the provided Jupyter Notebooks to merge and filter the collected data.

The PaveVibe project allows for certain configurations to be adjusted to suit specific requirements. These configurations are set in the ESP32 code, found in the /PaveVibe directory.

One of the key configurable parameters in the PaveVibe project is the sampling rate, which dictates how frequently the device collects data from the accelerometer and GPS. By default, this is set to:

const int amostrasPorSegundo = 10; // Samples per second

This diagram illustrates the setup required for PaveVibe, showing connections between the ESP32, accelerometer, and GPS module.

This image shows the internal organization of the components within PaveVibe.

This image displays the final version of the PaveVibe project in its current state.

A representation of how pavement quality data can be visualized using QGIS, offering insights into pavement conditions across different locations.

The PaveVibe project includes a 3D printable case for the device, which can be found in the 3D files directory as a .f3d file. This case is designed to protect the device and integrate all components effectively.

• /PaveVibe: Contains the ESP32 code for data collection.
• tratamento e merge.ipynb: Jupyter Notebook for merging and preprocessing collected data.
• Filtros_SMA_Butterworth_COMP.ipynb: Notebook for applying SMA and Butterworth filters to accelerometer data.
• /3D files: Contains the .f3d file for printing the case in 3D.

PaveVibe is licensed under the GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007. For more details, see the LICENSE file in this repository.

Special thanks to everyone who contributed to PaveVibe, from initial concept through to implementation and data analysis. Your hard work and dedication are greatly appreciated.