Reference-free fatigue crack detection using deep long short-term memory network (DLSTM) and nonlinear ultrasonic modulation

For a metallic component, prediction of a remaining useful life (RUL) is of extreme importance to prevent a sudden failure of the structure. If a fatigue crack exists, the nonlinear interaction of a low frequency pumping wave (LF) and a high frequency probing wave (HF) generates additional responses at the frequencies equal to the sum and difference of the input frequencies. In this study, the nonlinear ultrasonic modulation is used for fatigue crack prognosis of the lifting-lug that connects a tension cable. Nonlinear ultrasonic modulation components are used to define a fatigue index. The functional relationship between fatigue index and the number of loading cycles is established based on Paris–Erdogan theories. Then, by curve fitting of the established function to the fatigue index values obtained at various loading cycles, the RUL corresponding to a fatigue index is estimated from the fitted function. The proposed RUL estimation method is associated with the following advantages: (1) the fatigue index is defined as a function of nonlinear ultrasonic modulation components, (2) the functional relationship between fatigue index and loading cycles is established for lifting-lug structure, and (3) online remaining useful life is predicted and updated using sequentially measured ultrasonic signals. To validate the proposed method, the lifting-lug specimen is used and subjected to cyclic loading. The experiment is conducted using surface-mounted piezoelectric sensors for ultrasonic signal excitation and measurement. The experimental results confirm that the proposed method can estimate the RUL successfully.

Fatigue crack detection using dual laser induced nonlinear ultrasonic modulation

Reference-free fatigue crack detection using nonlinear ultrasonic modulation under various temperature and loading conditions

In the practical applications of crack detection, traditional image processing based detection methods are faced with a lot of challenges, such as low level of intelligence, poor adaptability and so on. In this paper, an automatic pixel-level crack detection method based on deep transfer learning is proposed for engineering applications. The proposed detection method is composed of two stages: crack recognition and crack semantic segmentation, which makes it easy to meet the needs of efficient and reliable detection for large-scale collected images. The fine-tuned Vgg16 model in the first stage can accurately identify the crack images and avoid the computing cost for non-crack images in the further processing. The Unet++ model in the second stage provides a pixel-level semantic segmentation for crack images. Multifold knowledge based transfer learning and weighted loss function are proposed to improve the performance and generalization ability of the models. On 5 publicly available datasets, the proposed method achieved a mIoU of 84.62%. Tested on totally 8064 images with a resolution of 224×224, the proposed method achieved a mean detection speed of 8.1ms per image. Experiment results exhibit that the proposed method can effectively detect the pixel-level cracks. It provides an end-to-end deep learning method for large-scale crack detection for civil infrastructure. For the crack detection in complex scenes and the improvement of processing speed will be our next in-depth study.

Automatic crack detection is a main task in a crack map generation of the existing concrete infrastructure inspection. This paper presents an automatic crack detection and classification method based on genetic algorithm (GA) to optimize the parameters of image processing techniques (IPTs). The crack detection results of concrete infrastructure surface images under various complex photometric conditions still remain noise pixels. Next, a deep convolution neural network (CNN) method is applied to classify crack candidates and non-crack candidates automatically. Moreover, the proposed method is compared with the state-of-the-art methods for crack detection. The experimental results validate the reasonable accuracy in practical application.

Automatic concrete cracks detection and mapping of terrestrial laser scan data

We present a technique for microcrack modeling in the finite element framework, and numerically investigate the occurrence of nonlinear wave modulation. Typically, fatigue cracks are initiated and developed when structures are exposed to repeated loading; the crack widths of the fatigue cracks are extremely small in the early development stage. As the fatigue cracks grow by combining and coalescing, the overall size increases. Enlarged cracks undermine the safety of the structure. Therefore, fatigue crack detection is very important to ensure the integrity of structures. Although the nonlinear ultrasonic wave modulation technique has been widely used due to its high detecting sensitivity, the basic principle is not fully understood. To reveal the mechanism of nonlinear wave modulation, the movements of the crack surfaces are calculated through numerical simulation. The shape of the crack surface can determine the intensity of the wave modulation. In this study, we investigate the variation of the crack widths due to fatigue failure using microscopic imaging of real fatigue cracks, and use these images to create realistic models of the fatigue cracks.

As the aging of concrete structures becomes increasingly severe, crack detection has become a crucial aspect of maintaining their sustainable use. This paper investigates an automatic detection and analysis method for concrete cracks based on deep convolutional neural network architecture. The study utilized drones equipped with high-definition cameras to capture images of concrete structures such as bridges and roads, collecting over 10,000 original images containing cracks. Using Labelme software for point-by-point image annotation and applying data augmentation techniques like rotation and scaling, the dataset was expanded to 30,000 images to meet the requirements of deep learning model training. An improved AlexNet model was developed, replacing the fully connected layer with global average pooling to enhance robustness and reduce overfitting. The model achieved an average accuracy of 95.42% on the test set, outperforming traditional methods by 15%. The model also proved effective in noisy environments. Additionally, a semantic segmentation model based on Fully Convolutional Network (FCN) was introduced, incorporating atrous convolution and spatial pyramid pooling, achieving a pixel-level accuracy of 97.6%, surpassing benchmark models such as FCN and U-Net. The model accurately estimated crack width, with an error rate within ±2% compared to field measurements. This method improves detection efficiency and accuracy while reducing manual intervention, providing strong support for the maintenance of concrete structures.

We report a new method of automatic first break detection of P‐waves and S‐waves. Our method is based on a time‐frequency analysis of the seismic trace using minimum uncertainty (μ‐)wavelets, in particular in the minimum‐phase form. We have tested our method on both lab data with various S/N and on field data. Our method picks the first arrival time for lab data contaminated with both high and low S/N with an accuracy of 0.5 μs. We also compare our automatic detection of the first break in field data with manual detection. The difference is less than 1 ms. Our results indicate that our method is robust for automatically detecting the first arrival time for field data.

News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.

Security of Internet-of-Things (IoT) systems is important due to their widespread usage in everyday life. Much research has been performed on analyzing the security of IoT communication protocols and operating systems. However, few studies have focused on analyzing the security of IoT applications and automatic detection of vulnerabilities in them. In these studies, the code of IoT applications and operating systems are analyzed statically to detect vulnerabilities. To the best of our knowledge, there is no dynamic analysis solution suggested for vulnerability detection in such applications, although this method is more accurate than static analysis. In fact, IoT applications are executed in special-purpose hardware, which makes their dynamic analysis more difficult than ordinary applications. In this paper, we propose a technical solution that combines static and dynamic analysis methods to automatically detect vulnerability in applications of Tizen IoT operating system. We consider Native and Web Tizen applications and present an automatic vulnerability detection method for each type of application. Our focus is on detecting buffer overflow and XSS vulnerability classes in Native and Web applications, respectively. We have evaluated the effectiveness of our method using a group of native and web test programs. The results of our experiments show that our solution is able to detect the vulnerability in these programs effectively.

Karayolu yüzeyindeki asfalt çatlakları en yaygın kaplama hasarlarından biridir. Bu çatlaklar zamanında tespit edilip bakıma alınmazsa büyüyerek trafik güvenliğini ve yoğunluğunu arttıracak boyutlara ulaşmaktadır. Bu nedenle, asfalt çatlaklarının tespitinin hızlı bir şekilde gerçekleştirilmesi çok önemlidir. Geleneksel manuel çatlak tespiti, son derece zaman alıcı, çok maliyetli ve çok çaba gerektirir. Bu nedenle, araştırmacılar odaklarını otomatik asfalt çatlaklarının tespitine yoğunlaştırdılar. Ancak, son yıllarda otomatik çatlak tespiti kapsamlı olarak araştırılmasına rağmen çatlakların farklı yoğunluğa sahip olması ve kaplama ortamının karmaşıklığı nedeniyle hala zor bir görevdir. Bu çalışmada, bu zorluğun üstesinden gelmek için konvolüsyon sinir ağı temelli bir yöntem önerildi. Önerilen yöntem, sınıflandırma ve segmentasyondaki başarısı ve hafifliği bilinen MobileNetv2 ’nin temelde kullandığı konvolüsyon ve ters artıklı blok yapılarını baz alarak geliştirildi. Yapılan deneysel testler sonucunda, önerilen yöntemin performansı literatürdeki diğer yöntemlere göre daha yüksek olduğu görülmektedir. Bu da, otomatik asfalt çatlak tespitinin daha başarılı olması anlamına gelmektedir.

The accuracy of traditional fault current detection methods in distribution network is low. In order to achieve accurate detection of fault current, an automatic fault current detection method based on current transformer is proposed. By describing the saturated operation state of current transformer, the current saturation characteristics of distribution network are analyzed and measured. The least square calculation method is introduced to describe the fault current signal, so as to calculate the fault current amplitude of distribution network. By drawing the equivalent circuit diagram of fault current in distribution network, the fault current range and direction in distribution network can be detected automatically. In addition, through comparative experiments, it is proved that the proposed detection method can effectively improve the detection accuracy in practical application, and show the change of fault current more accurately.

In order to reduce the probability of website users being attacked and maintain the safety of website operation, this study proposes an automatic vulnerability detection method of websites based on associated data. We use plug-ins to scan the website in all directions, establish a scanning database, and classify and store the scanned web data. By applying optimized an a priori association rule algorithm, key features are extracted from web scan data, which are then transformed into input samples for a K-means clustering algorithm. The aim is to efficiently extract feature attributes of website vulnerability data and ultimately construct a text vectorized representation of vulnerability data. Convolutional neural networks can automatically detect website vulnerabilities by using the constructed text vector as input. Experimental verification shows that this method demonstrates comprehensive data coverage, efficient processing speed, and high-precision recognition performance. It not only significantly reduces the clustering analysis time, but also ensures the accuracy and timeliness of vulnerability detection.

In this paper, we proposed an automatic crack detection method based on deep convolutional neural network −U-Net [4]. Unlike existing machine learning based crack detection methods, we can process an image as a whole without patchifying, thanks to the encoder-decoder structure of U-Net. The segmentation result is output from the network as a whole, instead of aggregation from neighborhood patches. In addition, a new cost function based on distance transform is introduced to assign pixel-level weight according to the minimal distance to the ground truth segmentation. In experiments, we test the proposed method on two datasets of road crack images. The pixel-level segmentation accuracy is above 92% which outperforms other state-of-the-art methods significantly.