•An annotated and validated impact echo dataset was formed.•Deep learning was used to classify impact echo signals for the first time.•Both 1D and 2D deep learning models were investigated.•The ...proposed 1D model was superior to both conventional and other neural networks.
Impact echo (IE) is a common nondestructive evaluation (NDE) method to detect subsurface defects in concrete bridge decks. The conventional approach for analyzing the IE data requires user expertise to define analysis parameters that could hinder broad field implementation. In this paper, the feasibility of using deep learning models (DLMs) for autonomous subsurface defect detection in bridge decks using IE has been investigated. A set of eight laboratory-made reinforced concrete bridge specimens with artificial defects were constructed at the Federal Highway Administration Advanced Sensing Technology NDE laboratory. A total number of 2016 of IE data was collected from these specimens. A one-dimensional (1D) convolutional neural network (CNN), and a 1D recurrent neural network using bidirectional long-short term memory units, were developed and applied on the IE data. In addition, two-dimensional (2D) world renowned CNNs were applied on the 2D representatives of the IE data, i.e., spectrograms. The proposed 1D CNN was the most accurate model achieving an overall accuracy of 0.88 by classifying 0.70 of the defects and 0.95 of the sound regions correctly. The proposed 1DCNN was superior to previous machine learning models that were previously used for IE classification. The results of this study showed the feasibility and the potentials of the DLMs for subsurface defect detection.
•Eight spectrum imaging techniques reviewed in this article refer to RGB imaging, Multispectral/Hyperspectral imaging, Chlorophyll fluorescence imaging, Thermal imaging, Raman spectral imaging, X-ray ...imaging, Magnetic resonance imaging and Terahertz spectral imaging.•The application of spectrum imaging technology in the study of biochemical, physiological, morphological and yield traits of greenhouse vegetable was reviewed.•Four potential solutions are proposed: multi-source fusion and phenotype platform development, innovative data analysis methods, development of portable devices, and analysis combined with underlying mechanisms.
Greenhouse vegetables have become increasingly important in global crop production due to their ability to be cultivated out of season and ensure a year-round supply of vegetables. With the rapid advancement of “phenomics”, accurately measuring the phenotypic information of greenhouse vegetables is crucial for enhancing both their yield and quality. Over the past two decades, various technologies have been developed for phenotypic detection of fruits, vegetables, and other crops, based on the interaction between electromagnetic waves and matter. While some articles have investigated these applications, there is a lack of a systematic review specifically focused on the phenotypic detection of greenhouse vegetables. In this review, RGB imaging, Multispectral/Hyperspectral imaging, Chlorophyll fluorescence imaging, Thermal imaging, Raman imaging, X-ray imaging, Magnetic resonance imaging, and Terahertz imaging are collectively referred to as spectrum imaging technologies. We provide a comprehensive review of the origins, research progress over the past twenty years, and current challenges of spectrum imaging in the field of greenhouse vegetable research. It focuses on identifying the most suitable spectrum imaging technologies for detecting four categories of phenotypic traits: biochemical, physiological, morphological, and yield-related traits. Additionally, we highlight the issues that need optimization in the practical application of these technologies and the bottlenecks faced in different trait studies. Finally, based on existing research, we propose several potential solutions and future research directions to maximize the utility of spectrum imaging technologies in the phenotypic detection of greenhouse vegetables.
In this paper, convolutional neural network (CNN) is employed for crack damage detection in thin aluminum plates. Identifying a successful damage feature is a critical step in computationally ...efficient damage detection and characterization process; however, such feature identification process is generally time-consuming and often difficult to execute. The CNN model is a deep learning model that can be trained to represent high dimensional data for which traditional mathematical model is ill to describe. This study first formalizes the nondestructive evaluation (NDE) problem of notch-type crack damage detection in thin metal plates into an image classification problem in the machine learning domain, which is then solved with a deep CNN model trained using Lamb wave data converted images. Analytical formulas have been derived for generating Lamb wave signals subsequently used for training and validation test of the proposed crack damage detection technique for thin metal plates. Experimental test data from aluminum plate with simulated notch damage has also been used to independently validate the CNN model. The study results show its potential as a promising NDE tool for crack damage detection in thin plate structures.
Stretchable Imagers
In article number 2304048, Teppei Araki, Kou Li, Daichi Suzuki, Daichi Suzuki, Yukio Kawano, Tsuyoshi Sekitani, and co‐workers review trends and technologies regarding stretchable ...imagers that are attached to an arbitrary surface and remotely acquire signals with minimal mechanical stress. The fusion of broadband photodetectors and stretchable packaging enables internal evaluations for health conditions and abnormalities of targeted living bodies and various objects.
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•Four dominant damage mechanisms in tensile loading of GFRPs were detected using AE.•The peak frequency range of each damage mechanism was determined.•The share of each damage ...mechanism in composite failure was calculated by wavelet transform.•The AE results verified using SEM images.•The effects of nanoparticles on the LVI and TAI strength of GFRPs had been investigated.
The present paper focuses on the detection of dominant damage mechanisms in tensile loading of laminated composite specimens subjected to low-velocity impact (LVI) using acoustic emission (AE) method. Moreover, the effects of various nanoparticles (NPs) on the tension-after-impact (TAI) strength of glass fiber-reinforced polymer (GFRP) composites have been investigated. The hierarchical method was employed to cluster the acoustic emission data and detect the damage mechanisms of the composites. These damage are matrix cracking, fiber–matrix debonding, fiber breakage, and fiber pull-out, the peak frequency ranges of which were obtained to be (100–200), (250–370), (410–530), and (570–650) kHz, respectively. Subsequently, the existence of these mechanisms was verified using Scanning Electron Microscope (SEM) images, and their shares in composite failure under tensile loading were calculated by wavelet transform method. According to the results, the NPs have enhanced the impact strength of the GFRPs by up to 66% reduction in damage index. Furthermore, they have increased TAI strength by up to 39%, claims also supported by the AE analysis.
This volume describes the fundamentals and techniques necessary for Lamb-wave-based damage identification. This damage assessment method offers efficient structural failure detection which lowers ...maintenance costs and increases safety benefits.
This article presents a review of air-coupled ultrasonics employed in the characterization or nondestructive inspection of industrial materials. Developments in air-coupled transduction and ...electronics are briefly treated, although the emphasis here is on methods of characterization and inspection, and in overcoming limitations inherent in the use of such a tenuous sound coupling medium as air. The role of Lamb waves in plate characterization is covered, including the use of air-coupled acoustic beams to measure the elastic and/or viscoelastic properties of a material. Air-coupled acoustic detection, when other methods are employed to generate high-amplitude sound beams is also reviewed. Applications to civil engineering, acoustic tomography, and the characterization of both paper and wood are dealt with here. A brief summary of developments in air-coupled acoustic arrays and the application of air-coupled methods in nonlinear ultrasonics complete the review. In particular, the work of Professor Bernard Hosten and his collaborators at Bordeaux is carefully examined.
Second harmonic generation (SHG), a laser-based surface technique, was used to characterize near-surface defect evolution associated with mechanical deformation in 2024-T3 aluminum due to tensile ...loads. Electron backscatter diffraction (EBSD) measurements were also performed to determine the geometrically necessary dislocation density before and after tensile deformation. Comparison of the EBSD and SHG results found that dislocation density and SHG signal are inversely proportional; tensile deformation in the plastic regime led to an increase in dislocation density and a decrease in SHG response. A similar trend was seen in the SHG results on as-received (unpolished) Al samples; SHG response decreases with extent of plastic deformation. Furthermore, this study suggests that SHG has potential as a nondestructive evaluation technique.
Electrical impedance tomography (EIT) is a method of spatially mapping the conductivity distribution of a domain and has been studied as a potential embedded sensing or nondestructive evaluation ...(NDE) tool. An often touted advantage of EIT is that it can be used in-situ; that is, because the method only requires the application of unobtrusive electrodes, it can conceivably be used while the component or structure is in operation. This material-as-the-sensor philosophy strongly aligns with key components of the NDE 4.0 vision such as the realization of intelligent cyber–physical systems (CPS) and digital twins. To date, however, the claim of in-situ sensing via EIT has not been significantly substantiated. This is problematic because operational loads induce strains that often change the conductivity of the material. Establishing that EIT can detect damage-induced conductivity changes through the presence of unrelated strain-induced conductivity changes is therefore important. To that end, we herein study the application of EIT for detecting indentation damage in a carbon fiber/epoxy composite as the composite is loaded in a four-point bend. It was found that the bending load changes the contact impedance of the electrodes, which resulted in poor EIT images when solving the EIT inverse problem with the ℓ2-norm on the error term. Using the ℓ1-norm on the error term, solved via the primal–dual interior point method (PDIPM), significantly improved image quality. Image quality was even further improved through the use of a mixed prior for regularization, and EIT images were compared to thermography with good agreement. These results show that EIT can indeed detect damage through the presence of an applied load, but care must be taken to account for factors such as outlier data arising from electrode degradation and changing contact impedance. Use of the ℓ1-norm on the error term is therefore highly recommended for in-situ imaging via EIT.
Switch rails are weak but essential components in a high-speed rail track system, which have an urgent non-destructive testing requirement due to aging and associated fatigue damage accumulation. ...They are settled under sophisticated operation environments, which causes them to have unpredictable damages, such as abrasion, exfoliation, and cracks. Our goal is to propose a reliable system to detect structural damages of switch rails. Using ultrasonic guided waves to examine the health status of switch rails makes it possible to continuously evaluate the health status of switch rails when they are in use. Conventional damage detection methods with ultrasonic guided waves such as baseline signal subtraction, independent component analysis-based methods cannot always make reliable detection results. These methods are either lack of powerful abilities to capture the characteristics of damaged signals or time-consuming to be operated in real damage detection tasks. In this paper, a convolutional neural network-based system is proposed to solve both of the above challenges simultaneously. The proposed model employs multiple convolutional layers to extract deep features of ultrasonic guided wave signals. These features are then fed into a classifier to predict whether they are damaged signals or not. To evaluate the proposed model performance, we collected ultrasonic guided wave signals from two different switch rails. The proposed model achieved more than 91% testing accuracy and outperformed other relevant methods. It also demonstrated the proposed model had strong generalization abilities to make it capable in practical switch rail structural damage detection tasks.
