Robust attention driven densenet framework for accurate structural health and crack detection

Full-scale aircraft structural fatigue testing exposes structural weaknesses by simulating the use of the aircraft in service and monitoring the development and expansion of fatigue cracks to guide the optimisation of aircraft structural design, the determination of service life and the development of maintenance and repair programmes. If crack damage is not detected in time, the uncontrolled expansion of the crack may lead to structural damage and prolong the test cycle. Therefore, it is crucial to achieve accurate detection of early cracks in fatigue testing of aircraft structures. Digital image technology is a new technology supported by a number of technical and theoretical achievements. In practical application, it relies on the support of computer functions, by converting image information into two-dimensional digital signals, thus realising various operations. In this paper, digital image processing techniques are used to deepen the backbone feature extraction network structure and improve the prediction scale of small targets, thus improving the performance of the model in terms of feature extraction and small defect detection. Experimental results show that the method can achieve accurate detection of small cracks, providing an effective technical approach for the automatic detection of cracks in aircraft structural fatigue tests. The technique of detecting cracks in aircraft structures using digital image processing also provides a new idea for research into improving aircraft endurance testing, which can be further developed with the aid of image technology.

Crack detection and retardation of steel structure by smart CFRP embedding PPP-BOTDA fiber sensors

Crack detection is an important issue in the health monitoring and performance evaluation of concrete structures. Based on the heat transfer theory, a temperature tracer method and a monitoring system for crack detection in underwater concrete structures are presented in this paper. The system is composed of an integrated sensing and heating system, a monitoring tube, and multiple casing tubes. The inner diameter of the casing tube should be larger than the outer diameter of the monitoring tube so that there is a certain gap between the casing tube and the monitoring tube to form a cavity. Water fills the cavity along the crack face when the underwater concrete structure cracks, resulting in a change in the surrounding medium at the cracked site. Thus, the cooling rate of the heat source in the monitoring tube corresponding to the cracked site will be accelerated. Crack identification can be done based on a comparative analysis of the cooling curves of the heat source in the uncracked and cracked states. The effects of cavity geometry size and intensity of heat source on crack identification are studied using numerical simulations and crack detection tests. Both simulations and test results confirm the feasibility of the proposed crack detection method.

Crack detection is gaining much attention nowadays due to localization and identification of cracks in the civil structures accurately. The aim of crack detection on images is to automatically detect the cracks or defects considering the properties of civil structures. Among various civil structures, concrete structures are the most important one which is having both domestic and industrial applications. Detection of cracks in concrete structures will be very important in present scenario. Some of the prevailing crack detection methods are manual inspection method, acoustic and vibration-based method, electrical and magnetic methods, visual and optical methods. This survey paper jointly summarizes the various methodologies and issues of automatic crack detection. Performance metrics like accuracy, sensitivity, and specificity are used to analyze the experimental evaluation of different papers effectively. Finally, various challenges and the future research directions of the crack detection techniques are discussed.

In this research, two methods for crack detection in structures are presented and compared. The considered structure is a cantilever beam with rectangular cross section. In order to find cracks, firstly, a new technique based on wavelet analysis and finite element method (FEM) is applied. The advantage of this technique is that the crack detection process is more clear and comfortable than previous works. Then the process of crack detection is performed using FEM and combination of two types of artificial neural network (ANN) including radial basis function (RBF) and back-error propagation (BEP) neural networks. For crack identification in the proposed method, firstly, a RBF neural network is used to detect the number of cracks of structure. Then a BEP neural network is trained to detect the locations of cracks. Training of neural networks is performed using obtained data from FEM. Finally obtained results from two methods are compared with each other.

Structural damage generally initiates in the form of structural cracks. Thus, developing efficient crack detection techniques is of great importance for the structural health monitoring. In this paper, a new crack identification method is proposed, which is based on the differential pulse-width pair Brillouin optical time domain analysis (DPP-BOTDA) technology and the irregular features of Brillouin gain spectrum (BGS) in the fiber due to structural cracks. The proposed method provides a new way to detect and quantify structural cracks without knowing the strain in the structure. First, the working mechanism of DPP-BOTDA is introduced to illustrate the reason that the DPP-BOTDA, compared to traditional BOTDA technique, can significantly improve the spatial resolution of distributed strain sensing, which is critical for structural crack detection. Then, the BGSs in the fiber with the presence of structural cracks, measured by the DPP-BOTDA, are numerically simulated, from which the crack-induced irregular features of the BGS are summarized. Based these irregular features, new structural crack detection and quantification methods are proposed, which are found to be independent of structural stain. Finally, an experiment is conducted on a simple supported reinforced concrete (RC) beam. The results demonstrate that by using the BGS measured by the DPP-BOTDA, the proposed structural crack identification method successfully detects the occurrence of structural cracks and relatively accurately predicts the crack widths.

Machine learning based frequency modelling

Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed and applied to bridge crack image identification to provide an automatic method for crack detection. First, a large number of concrete crack images collected from the site were segmented and preprocessed for the establishment of a crack image dataset. Deep learning of the SSD algorithm was introduced on the training set to establish the detection model, where the model parameters were adjusted by the validation set. Sliding window technology was integrated to identify the cracks in the test set. The effects of the sliding window size and dataset size on the crack detection results were discussed. Moreover, the eight neighborhood algorithm was adopted for further crack detection correction. The results show that the configuration achieves good crack detection by the deep learning of the SSD algorithm with high precision and recall. The introduction of the eight neighborhood correction algorithm further improves the detection results by eliminating some misjudged results. Finally, the developed algorithm was placed into a portable device, with which cracks were effectively identified. The introduced method shows significantly better performance in crack detection, and the system installed on the portable device provides a way to broaden its application in the automatic crack detection of concrete bridges.

Crack detection has always been a dominant requirement for steel industries to ensure quality production and seamless infrastructure maintenance. However, application complexities and defect morphological differences make existing approaches confined. Steel-strip surface often undergoes scratch, crack and fatigue conditions during production. Manual crack detection schemes are no longer effective in current day complex environment. Amongst major steel strip crack detection approaches vision based techniques have been found potential. Filamentous crack which is caused due to fatigue or strain is fine-grained and thin and hence highly difficult to be detected by classical morphology and static threshold based schemes. In the present work steel strip surface crack(filamentous) detection system is developed which employs Varying-Morphological Segmentation (VMS) also called Neuron-Model Segmentation (NMS) in conjunction with local directive filtering and active contour propagation. The proposed method can be stated as an augmented variational framework that employs multi-directional filters for local crack-region identification followed by automated multi-directional region growing and iterative contour evolution which performs level set energy minimization to achieve accurate crack detection even under topological non-linearity and varying illumination conditions .Simulation results with standard benchmark data has confirmed that the proposed method exhibits satisfactory performance for steel strip surface cracks.

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model’s decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice.

The application of distributed fiber optic sensing (DFOS) technologies to the monitoring of civil engineering structures has been widely developed in recent years. In particular, fiber optic sensing (FOS) using the Rayleigh backscattering phenomenon of the laser frequency spectrum has been used to continuously measure strain along the fiber optic sensor with high spatial resolution. However, the practical implementation of fiber optic sensorsin the field is still a challenge. These include, but are not limited to, the bonding between fiber sensor cables and the surrounding medium, the practical way in which fiber optic sensors can be installed in concrete structures, and the analysis of crack width using the strain distribution along the fiber. Compared to classical discrete sensors, the DFOS has a great potential to detect, localize and quantify crack formation even in the early stages of microcracking. This paper discusses the potential and challenges of using distributed fiber optic sensors for crack detection in concrete structures. In this sense, different types of mechanical tests on concrete structures using different fiber optic sensors, and different bonding techniques are presented to explore the possibilities of crack detection in concrete structures and its potential for practical structural health monitoring (SHM) systems of civil structures.

High-precision detection of crack is of great significance to ensure the safety of pressure equipment in service. Electromagnetic ultrasound has the advantages of no coupling agent, no surface pretreatment, and non-contact. However, several problems such as low energy conversion efficiency and poor sound field directivity limit the application in crack detection. This paper designs a multi-layer line-focusing electromagnetic acoustic bulk wave transducer (MLF-EMAT). A simulation model containing MLF-EMAT is established based on the Lorentz force and magnetostrictive force transduction mechanism. The influence of some transducer parameters (wire width, wire thickness, coil turns, lift-off distance) on the echo response of crack has been studied. The orthogonal experimental design method is used to optimize the controllable parameters of the transducer. When the width of the permanent magnet is equivalent to the width of the coil, the larger the number of coil layers, the larger the amplitude of the echo response of crack. An experimental platform for electromagnetic ultrasonic testing is established, and several line-focusing electromagnetic acoustic transducers with multiple parameters are prepared. The comparison results show that the optimized MLF-EMAT significantly improves the signal-to-noise ratio of echo response of crack. The amplitude of echo wave is increased by about 78% compared with that before optimization, and the detection accuracy of crack notch depth is up to 0.05 mm. The MLF-EMAT proposed in this study lays the foundation for the application of crack detection or monitoring of pressure vessels and pipes under complex working conditions.

The paper presents the application of ultrasonic guided waves for fatigue crack detection in metallic structures. The study involves a simple fatigue test performed to introduce a crack into an aluminium plate. Lamb waves generated by a low-profile, surface-bonded piezoceramic transducer are sensed using a tri-axis, multi-position scanning laser vibrometer. The results demonstrate the potential of laser vibrometry for simple, rapid and robust detection of fatigue cracks in metallic structures. The method could be used in quality inspection and in-service maintenance of metallic structures in aerospace, civil and mechanical engineering industries.

The combination of UAV camera and intelligent algorithm is a promising method for non-contact bridge crack detection. In this paper, an inspection tool based on UAV Image Acquisition Technology (UAVIAT) and Improved Intelligent Target Detection Technology (IITDT) called Improved Intelligent Real-Time Crack Detection Method for Bridges (IIRTCDMB) is proposed for efficient crack detection. The contributions of this paper are (1) The Squeeze-Excitement (SE) attention module is integrated into the target detection algorithm - You Only Look Once version 7 (YOLOv7) model to improve the learning ability of the feature channel. A Focal-efficient intersection over union (Focal-EIoU) loss function is also introduced to improve the regression accuracy of the model. As a result, a new crack image detection algorithm, YOLOv7-CD, is proposed. (2) A training process based on two-stage transfer learning (TSTL) is established, and hyper-parameter optimization of YOLOv7-CD is carried out. The feasibility and excellent performance of the proposed method are verified by applying it on the Cuntan Yangtze River Bridge. The results show that the average precision (AP) of the YOLOv7-CD model is improved by 3.19% compared with the original YOLOv7 model. After TSTL and hyperparameter optimization, the AP of the YOLOv7-CD model for bridge crack detection reaches 98.01%, which is higher than that of the popular target detection models. The IIRTCDMB proposed in this paper can acquire bridge surface images more safely and efficiently, and provide inspectors with more accurate structural crack information with lower computational and hardware requirements, which can provide technical support for the assessment of structural safety conditions and the formulation of maintenance programs.

Classification of C scan images obtained using pulsed eddy current and giant magneto resistive sensor (PEC-GMR) detection is presented in this paper. PEC-GMR technique is applied to solve the detection of cracks in multi-layered structures, in particular, the aircraft wing structure with rivets. In current techniques, time-dependent, (A scan) signals are processed for feature extraction. In this paper, for rivet inspection, the rivet image data is processed as a whole. In the C-scan image analysis, principle component analysis (PCA) is applied to transform the C-scan image into the PCs (principle components) subspace to extract appropriate features. The extracted features are used as input to a k-means classifier for detection of cracks in the 2ndand 3rdlayers at rivet sites in multi-layered riveted samples.