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1. Estimating Guided Wave Velocity Variation With Neural Networks

   https://doi.org/10.1115/QNDE2021-75080  

   Leibovici, Ori; Yang, Kang; Harley, Joel B. (July 2021, Proc. of the Annual Review of Progress in Quantitative Nondestructive Evaluation)

   Abstract While guided wave structural health monitoring (SHM) is widely researched for ensuring safety, estimating performance deterioration, and detecting damage in structures, it experiences setbacks in accuracy due to varying environmental, sensor, and material factors. To combat these challenges, environmentally variable guided wave data is often stretched with temperature compensation methods, such as the scale transform and optimal signal stretch, to match a baseline signal and enable accurate damage detection. Yet, these methods fail for large environmental changes. This paper addresses this challenge by demonstrating a machine learning method to predict stretch factors. This is accomplished with feed-forward neural networks that approximate the complex velocity change function. We demonstrate that our machine learning approach outperforms the prior art on simulated Lamb wave data and is robust with extreme velocity variations. While our machine learning models do not conduct temperature compensation, their accurate stretch factor predictions serve as a proof of concept that a better model is plausible. 

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2. Memetic Optimizer for Structural Damage Identification Using Electromechanical Admittance

   https://doi.org/10.1115/DETC2022-91039  

   Zhang, Yang; Zhou, Kai; Tang, Jiong (August 2022, Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers & Information in Engineering Conference)

   Abstract Electromechanical impedance-based (EMI) techniques using piezoelectric transducers are promising for structural damage identification. They can be implemented in high frequency range with small characteristic wavelengths, leading to high detection sensitivity. The impedance measured is the outcome of harmonic and stationary excitation, which makes it easier to conduct inverse analysis for damage localization and quantification. Nevertheless, the EMI data measurement points are usually limited, thus oftentimes resulting in an under-determined problem. To address this issue, damage identification process can be converted into a multi-objective optimization formulation which naturally yields multiple solutions. While this setup fits the nature of damage identification that a number of possibilities may exist under given observations/measurements, existing algorithms may suffer from premature convergence and entrapment in local extremes. Consequently, the solutions found may not cover the true damage scenario. To tackle these challenges, in this research, a series of local search strategies are tailored to enhance the global searching ability and incorporated into particle swarm-based optimization. The Q-table is utilized to help the algorithm select proper local search strategy based on the maximum Q-table values. Case studies are carried out for verification, and the results show that the proposed memetic algorithm achieves good performance in damage identification. 

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3. Clamping Force Effects on the Performance of Mechanically Attached Piezoelectric Transducers for Impedance-Based NDE

   https://doi.org/10.1007/978-3-030-12676-6_33  

   Tenney, C. (April 2019, IMAC XXXVII A Conference and Exposition on Structural Dynamics)

   Impedance-based non-destructive evaluation (NDE) constitutes a generalization of structural health monitoring (SHM), where comparisons between known-healthy reference structures and potentially-defective structures are used to identify damage. The quantity considered by impedance-based NDE is the electrical impedance of a piezoelectric element bonded to the part under test, which is linked to the dynamic vibrational response of the part under test through electromechanical coupling. In this work, the piezoelectric element is not bonded directly to the part under test, but rather to a c-shaped clamp, which is then mechanically attached to the part under test. Under this attachment condition, the effect of clamping force on the sensitivity of the impedance-based evaluation is investigated for machined steel blocks with varying levels of damage severity. The highest clamping force tested (600 lb, 2670 N) was the only condition exhibiting increasing damage metric values with increasing damage severity in the parts under test, suggesting that higher clamping force increases sensitivity to damage. 

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4. Ultrathin MXene film interaction with electromagnetic radiation in the microwave range

   https://doi.org/10.1063/5.0176575  

   Rakhmanov, Roman; Shuck, Christopher Eugene; Al_Hourani, Jamal; Ippolito, Stefano; Gogotsi, Yury; Friedman, Gary (November 2023, Applied Physics Letters)

   The quick progress in communication technologies demands superior electromagnetic interference (EMI) shielding materials. However, achieving a high shielding effectiveness (SE) with thin films, which is needed for microscale, flexible, and wearable devices, through absorption of EM radiation remains a challenge. 2D titanium carbide MXene, Ti3C2Tx, has been shown to efficiently reflect electromagnetic waves. In this paper, we investigated the electromagnetic shielding of ultrathin printed Ti3C2Tx films and recorded absorption up to 50% for 4 nm-thick films. This behavior is explained by impedance matching. Analysis of the sheet impedance in the X-band frequency range allows us to correlate the EMI shielding mechanism with the electrical conductivity measured within the same range. The average bulk in-plane conductivity for 4 to 40 nm-thick films reaches 106 S/m, while the average relaxation time is estimated at around 2.3 ps. Our figures of merit are similar to those reported for ultrathin metal films, such as gold, showing that an abundant MXene material can replace noble metals. We demonstrate that the MXene conductivity mechanism does not change from direct current to THz. The conventional method of reporting EMI SE is correlated with absolute values of transmitted, reflected, and absorbed power, which allows us to interpret previous results on MXene EMI shielding. Considering the easy deposition of thin MXenes films from solution onto a variety of surfaces, our findings offer an attractive alternative for shielding microscale devices and personal electronics. 

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5. Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning

   https://doi.org/10.3390/app13031935  

   Ardani, Samira; Eftekhar Azam, Saeed; Linzell, Daniel G. (February 2023, Applied Sciences)

   This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is an efficient method for knowledge transfer and mapping from source to target domains. In addition, Proper Orthogonal Modes (POMs), which help classify behavior and health, provide a promising tool for damage identification in structural systems. Previous investigations show that damage intensity and location are highly correlated with POM variations for structures under unknown loads. To train damage identification algorithms based on POMs and ML, one generally needs to use multiple simulations to generate damage scenarios. The developed process is applied to a simply supported truss span in a multi-span railway bridge. TL is first used to obtain relationships between POMs for two modeled bridges: one being a source model (i.e., labeled) and the other being the target modeled bridge (i.e., unlabeled). This technique is then implemented to develop POMs for a damaged, unknown target using TL that links source and target POMs. It is shown that the trained knowledge from one bridge was effectively generalized to other, somewhat similar, bridges in the population. 

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