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semantic_scholare-Journal of Nondestructive Testing2026-08-01

From Color Shift to Crack Monitoring: Mechanochromic Coatings as a new sensing technique

Perla El boueiz, N. Bertola, Rijeesh Kizhakidathazhath, Jan P. F. Lagerwall

Over 30% of European bridges are over 50 years old, raising serious safety concerns and maintenance challenges. Despite this urgency, Structural Health Monitoring (SHM) systems remain underutilized due to high costs, complex installation, and limited scalability. Building upon the pioneering work of Camo et al. (2023), this study introduces a novel monitoring technique that uses Cholesteric Liquid Crystal Elastomers (CLCEs), mechanochromic polymers that change color under mechanical strain. Applied as a paint on the concrete surface, CLCEs function as passive, 2D spatially-distributed sensors that visually indicate crack formation through localized color shifts. This enables continuous, distributed monitoring without the need for power or embedded electronics. The project combines CLCE coatings with camera-based monitoring and machine learning to detect and quantify structural damage. This data-driven system supports predictive maintenance and aims to deliver a low-cost, scalable solution for real bridges. To validate feasibility and optimize the system, initial experimental campaigns were carried out. First, the applicability of CLCE coatings on concrete substrates was confirmed through mechanical testing. The coatings showed clear mechanochromic responses (color shifts) to crack initiation, validating their potential for SHM applications. Second, a systematic thickness optimization study was performed to enhance sensitivity, durability, and material efficiency. Using a custom metallic frame, coatings ranging from 10 μm to 30 μm were applied, and an optimal thickness range was identified, balancing visibility sensitivity, coating mechanical integrity, and resource use. Third, a methodology was developed to quantitatively correlate crack openings in concrete specimens with the coating response area, enabling not only detection but also quantification of damage in laboratory setups. The results were validated using digital image correlation, paving the way for its use in SHM applications. This work contributes to the development of a novel monitoring technique that is scalable, cost-efficient, and environmentally responsible. By validating the coating’s application, optimizing its parameters, and enabling crack quantification, the project moves closer to real-world deployment on aging infrastructure. Next steps include full-scale bridge trials and integration into maintenance strategies under real conditions.

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semantic_scholare-Journal of Nondestructive Testing2026-08-01

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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

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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

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Fatigue in metallic materials leads to progressive degradation driven by a sequence of microstructural mechanisms occurring over the life cycle. While fracture is typically the most obvious and critical damage state, it only appears at the end of life. However, when no fracture i…

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,…