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semantic_scholare-Journal of Nondestructive Testing2026-08-01

Phase-Based Motion Magnification in Optical-Flow-Based Fatigue Crack Assessment

Adam Machynia, Z. Dworakowski, K. Dziedziech, M. Dziendzikowski, K. Holak

TL;DR: The results show that motion magnification substantially enhances crack visibility when amplitude maps are derived from the optical-flow magnitude at low excitation amplitudes, extending the regime in which the crack-breathing pattern is discernible.

This paper investigates the role of phase-based motion magnification in a computer-vision-based methodology for marker-free fatigue crack assessment introduced in earlier work. The approach uses dense optical flow and frequency-domain analysis to capture periodic crack-breathing motion. The resulting spatial “amplitude maps” highlight regions dominated by crack-induced cyclic displacement and enable crack visualisation. We present a focused case study examining how motion magnification influences the quality and interpretability of these amplitude maps and under which conditions its use is justified. Experiments are conducted on a cantilever beam with a fatigue crack subjected to harmonic excitation at a known frequency. High-speed camera recordings are acquired across a range of excitation amplitudes, and motion magnification is applied as an optional pre-processing step. For each configuration, dense optical flow is computed and amplitude maps at the excitation frequency are formed using either the optical-flow magnitude or directional components. Maps obtained with and without magnification are compared in terms of crack-related pattern clarity and background artefacts. The results show that motion magnification substantially enhances crack visibility when amplitude maps are derived from the optical-flow magnitude at low excitation amplitudes, extending the regime in which the crack-breathing pattern is discernible. In contrast, for directional optical-flow components, which already provide clear crack visualisation, magnification yields little or no benefit and may degrade map quality at larger displacements. The study demonstrates that motion magnification is a task- and regime-dependent option rather than a default stage in amplitude-map-based crack-assessment pipelines.

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semantic_scholare-Journal of Nondestructive Testing2026-08-01

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TL;DR: This study demonstrates that transfer learning enables efficient adaptation to real-world conditions, offering a cost-effective and scalable solution for data-driven SHM.

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semantic_scholare-Journal of Nondestructive Testing2026-08-01

Integrated Structural Health Monitoring of Flax Fiber Reinforced Composites Using Nonlinear Resonance Acoustics, Acoustic Emission and Data-Driven Damage Identification

Othmane Achouham, C. Mechri, R. El Guerjouma, S. Allagui, Zeineb Kesentini, A. El Mahi

TL;DR: This work demonstrates that the combined use of nonlinear acoustics, acoustic emission, and machine learning constitutes a robust and highly sensitive SHM framework for composite structures.

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semantic_scholare-Journal of Nondestructive Testing2026-08-01

Time-Series Forecasting of Structural Temperature in Heritage Buildings Using Regression and Deep Learning Approaches

Waqas Qayyum, N. Cavalagli, E. García-Macías, F. Ubertini

Accurate prediction of the structural temperature field is crucial for the static and dynamic monitoring of engineering structures, with particular significance for heritage buildings where material preservation is paramount. The complex, time-lagged, and non-linear relationship…

semantic_scholare-Journal of Nondestructive Testing2026-08-01

Electromagnetic Assessment of Fatigue Degradation in Ferromagnetic Steel in View of Statistics and Monitoring

Christian Boller, Iman Ahadi Akhlaghi

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semantic_scholare-Journal of Nondestructive Testing2026-08-01

Integrating Ambient Vibration Monitoring and Machine Learning for Condition Assessment of Heritage Masonry Bridges: A Venetian Case Study

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Preserving the structural integrity of heritage masonry arch bridges presents unique challenges, particularly within historically dense environments like Venice where non-invasive methods are paramount. Ambient vibration monitoring (AVM) offers a well-established starting point,…