Conference Agenda

Session

SS20 - 2: Vision-Based Techniques for Vibration Assessment and Structural Health Monitoring - 2

Time:

Wednesday, 08/July/2026:

2:00pm - 3:40pm

Location: Argos

Session Abstract

Organisers:

Liangliang Cheng, University of Groningen, Netherlands

Michael Döhler, Inria, France

Janko Slavič, University of Ljubljana, Slovenia

Zhen Sun, Southeast University, China

Vincent Baltazart, Université Gustave Eiffel, France

Boualem Merainani, Université Gustave Eiffel, France

In recent years, computer vision and optical sensing have emerged as powerful, cost-effective, and non-contact technologies for vibration monitoring and structural health monitoring (SHM). Unlike traditional sensors that provide point-wise data, vision-based methods capture global, full-field measurements of structural response. Techniques such as digital image correlation, optical flow, motion magnification, and UAV-based photogrammetry enable accurate motion extraction, dynamic characterization, and early-stage damage detection, even under operational conditions. This special session aims to showcase the latest developments and future directions in vision-based vibration assessment and SHM. Contributions are invited on novel methods, hybrid approaches combining video with conventional sensing, and applications to real-world infrastructure such as bridges, buildings, and wind turbines. Topics of interest include (but are not limited to):

Motion extraction and vision-based modal analysis,

Damage detection and diagnosis using optical methods,

Motion magnification and computational imaging techniques,

UAV-based inspection and monitoring of hard-to-reach assets,

Robust and efficient processing for real-world environments,

Data fusion with conventional sensors,

Benchmarking, validation campaigns, and comparisons with traditional sensing approaches.

Presentations

2:00pm - 2:20pm

Thermoelasticity-based full-field modal analysis and fatigue damage identification

K. Zaletelj, J. Šonc, L. Capponi, J. Slavič

University of Lubljana, Slovenia

Visual spectrum cameras have become increasingly popular for non-contact full-field structural dynamics measurements, enabling displacement and deformation identification through techniques such as Digital Image Correlation. However, obtaining strain information from kinematic measurements requires spatial differentiation, which significantly amplifies noise and necessitates known analytical relationships between displacement and stress—particularly challenging for complex geometries.
As an alternative, the thermoelastic principle exploits the reversible relationship between cyclic mechanical stress and surface temperature variations under adiabatic conditions. High-speed infrared cameras can directly measure stress-induced temperature oscillations, typically in the millikelvin range, providing full-field stress information without spatial derivatives.
For strain-based experimental modal analysis, hybrid identification methods combining thermoelastic measurements with high-dynamic-range reference sensors successfully extracted full-field strain mode shapes from Euler-Bernoulli beams.
Critical findings established that minimum surface stress amplitudes of approximately 100 kPa for aluminum and 500 kPa for steel, enable accurate modal identification. These thresholds provide essential design criteria for experiment feasibility assessment.
Thermoelasticity was extended to rotating structures by integrating computer vision techniques with infrared imaging. Infrared-compatible ArUco markers enabled motion tracking and compensation, successfully identifying out-of-plane structural dynamics in rotating beams despite in-plane rigid body motion. Sub-pixel transformation accuracy was achieved, with the frames-per-degree ratio emerging as a key performance metric across varying operational conditions.
For vibration-fatigue assessment, a novel thermoelasticity-based multiaxial criterion was developed. Since thermal cameras inherently measure the first stress invariant (sum of normal stresses), this criterion provides a natural multiaxial-to-uniaxial conversion for fatigue analysis. Experimental validation on Y-shaped specimens demonstrated accurate identification of critical damage locations when normal stresses dominate, proving highly effective for bending-dominated loads where normal stresses govern crack initiation. The methodology enables close-to-real-time, full-field fatigue damage assessment, opening new possibilities for structural health monitoring applications.

2:20pm - 2:40pm

Data-driven delamination detection in CFRP panels using DIC under shear loading

L. Esposito¹, E. Monaco¹, V. Memmolo¹, F. Franco¹, G. Petrone¹, L. Grassia²

¹Industrial Engineering Department, University of Naples Federico II, Via Claudio 21, 80125 Napoli, Italy; ²Department of Engineering, University of Campania “Luigi Vanvitelli”, via Roma 29, 81031 Aversa, Italy

The ongoing demand for lightweight yet high-performance structures in the aerospace industry has led to the widespread use of carbon fibre reinforced polymers (CFRPs). However, their susceptibility to barely visible impact damage and internal delamination poses a significant challenge to maintaining structural integrity throughout the product's entire service life. Therefore, reliable structural health monitoring (SHM) systems capable of early defect detection and localization are crucial for ensuring safety and reducing maintenance costs in aircraft applications.

This study proposes an experimental methodology for detecting and localizing delaminations in quasi-isotropic CFRP plates under shear loading. Four square panels were manufactured: one in pristine condition, and three with an intentionally introduced delamination. A thin square PTFE (Teflon) film was inserted between two plies prior to curing to simulate the defect. This was to represent a manufacturing-induced delamination or an in-service defect. The panels were loaded in a custom-designed fixture that converts uniaxial tensile loading into in-plane diagonal tension. This results in an initial pure shear state, followed by local buckling along the compressive diagonal at higher loads. Full-field digital image correlation (DIC) was employed to monitor the out-of-plane deformation field and its evolution with load.

A data-driven detection metric was formulated based on spatially down-sampled out-of-plane displacement maps. This enabled a quantitative comparison to be made between pristine and defective panels under the same loading conditions. Displacement features were extracted using a coarse 10×10 spatial mesh and then processed to find a thresholding-based damage detection approach.

2:40pm - 3:00pm

Preliminary results for the evaluation of the influence of noise in Computer-Vision Sub-Pixel Algorithms for Displacement Monitoring

F. Allegrezza¹, F. Micozzi¹, M. Morici², A. Zona², A. Dall'Asta¹

¹School of Science and Technology, University of Camerino, Italy; ²School of Architecture and Design, University of Camerino, Italy

This study explores and compares several classical computer-vision sub-pixel algorithms for real-time extraction of displacements, with the aim of enabling comprehensive and cost-effective structural monitoring systems that are easy to install and operate. A preliminary analysis is carried out to evaluate the robustness of each algorithm against different types of noise, which can strongly influence measurement accuracy. Algorithm performance is quantified using statistical error metrics, such as mean error, standard deviation, and maximum error amplitude. The evaluation proceeds in three phases. First, synthetic videos are generated to reproduce controlled noise scenarios, allowing a systematic assessment of the influence of lens blur, motion blur, and camera sensor electronic noise. Afterwards, in the second phase experimental measurements made in controlled laboratory conditions are illustrated to validate the results obtained through synthetic videos for a given hardware configuration. Finally, a third phase uses experimental measurements made in an external environment (bridge deck under vehicular traffic), to provide an analysis of the effect of noise in complex real-world conditions.