Smart Pavement Sensing, Experimental Characterization & Data-Driven Modelling
I am conducting my doctoral research at the University of Parma within the CIRCOPAV — Circular and Connected Pavements for Carbon-Neutral and Digital Roads Marie Skłodowska-Curie Doctoral Network. My individual project, SN4SI — Sensors Network for Smart Self-Sustaining Interactive Infrastructures, is part of WP1 — Smart and Connected Assets.
The work integrates the complete experimental and computational workflow, from asphalt-mixture preparation and specimen fabrication to sensing, data acquisition, signal processing, material characterization, sensor calibration, viscoelastic modelling and machine-learning-based prediction.
Asphalt Mixture & Specimen Preparation
Preparation and compaction of asphalt slabs, coring of cylindrical specimens, cutting and dimensional preparation, and preparation of specimens for mechanical and dynamic testing.
Experimental Dynamic Testing
Controlled cyclic testing with an MTS system under different temperature and loading-frequency conditions, combining force, LVDT, temperature and ADXL355 measurements.
Sensor Integration & Data Acquisition
Raspberry Pi-based acquisition using ADXL355 MEMS accelerometers, including sensor interfacing, logging, synchronization and integration with reference measurements.
Sensor Calibration & Validation
Comparison with reference measurements, amplitude and phase checks, frequency-response evaluation, repeatability, residual analysis and signal-quality assessment.
Signal Processing
Python workflows for cleaning, filtering, FFT, power spectral density, sinusoidal curve fitting, frequency extraction, amplitude estimation, phase-angle determination and automated processing.
Viscoelastic Characterization & Machine Learning
Analysis and prediction of temperature- and frequency-dependent asphalt response, including dynamic/complex modulus and phase angle, with viscoelastic and data-driven approaches.
From asphalt materials to engineering interpretation
The overall objective is to combine experimental pavement engineering, sensing technologies and data-driven modelling to improve the measurement, characterization and interpretation of asphalt-material behaviour and contribute to smarter, more connected pavement infrastructure.
Sensor & Installation Development
Practical development of the acquisition hardware and evaluation of accelerometer installation approaches for asphalt specimens and slab-based testing.
Data acquisition prototype
A Raspberry Pi-based acquisition setup was developed to collect accelerometer measurements and compare them with controlled laboratory reference measurements.
Accelerometer positioning trials
Different installation conditions and positions were investigated before selecting a surface-mounted accelerometer configuration for specimen tests.
Surface-mounted measurement
The experimental development led to mounting the accelerometer on the specimen surface for the laboratory measurement workflow.
Scuffing-test sensor integration
Accelerometer installation inside an asphalt slab was also explored for scuffing-test measurements.
