Crack Detection In Structures Research Articles

Crack detection of masonry structure based on thermal and visible image fusion and semantic segmentation

The integration of visible and thermal images has demonstrated the potential ability to enhance crack segmentation accuracy. However, due to the intricate texture of masonry structures and the challenges posed in precisely aligning these cross-modality images, it is necessary to explore pixel-level alignment and develop a comprehensive dataset to enable deep-learning based methods. Therefore, a dataset, Crack900, including five image types, is developed together with a proposed two-step registration to achieve highly accurate pixel-level alignment. In addition, both Train from Scratch and Transfer Learning (TL) strategies are applied on eleven models to investigate the impact of different fused image types. Our findings reveal that the concatenation strategy markedly improves segmentation accuracy, and the performance of TL depends on the compatibility of channel numbers and domain difference between pre-trained and target models. These findings pave the way for further development of cross-modality in masonry crack segmentation methodologies for structural health monitorin.

On using the DFOS for damage identification in civil engineering structures

AbstractThe 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.

Detection of cracks in aircraft structures using digital image processing technology

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.

A novel criterion for crack identification in beam-like structures using distributed piezoelectric sensor and controlled moving load

The present study addresses the problem of crack identification in beam-like structures using a distributed piezoelectric sensor and controlled moving load. First, governing equations for the vibration of a cracked Timoshenko beam integrated with a piezoelectric layer and subjected to a moving load are established and their solution in the frequency domain is derived and acknowledged as electro-mechanical response of the beam. Then, variation of the electro-mechanical frequency response is examined versus crack and moving load parameters with the aim to propose a novel indicator, called herein spectral assurance criterion, for crack detection in beam-like structures. Finally, a procedure is developed for crack identification from moving load-induced frequency response measured by the distributed piezoelectric sensor based on the proposed spectral assurance criterion. The numerical analysis demonstrates that (a) the piezoelectric layer can be used as a distributed sensor for reliably measuring the mechanical response of beam subjected to moving load; (b) the speed-controlled moving harmonic force is a useful tool for exciting a beam-like structure for its health monitoring purpose and (c) the proposed spectral assurance criterion is actually a novel and efficient indicator for crack identification in beam-like structures.

Weakly-supervised structural surface crack detection algorithm based on class activation map and superpixel segmentation

This paper proposes a weakly-supervised structural surface crack detection algorithm that can detect the crack area in an image with low data labeling cost. The algorithm consists of a convolutional neural networks Vgg16-Crack for classification, an improved and optimized class activation map (CAM) algorithm for accurately reflecting the position and distribution of cracks in the image, and a method that combines superpixel segmentation algorithm simple linear iterative clustering (SLIC) with CAM for more accurate semantic segmentation of cracks. In addition, this paper uses Bayesian optimization algorithm to obtain the optimal parameter combination that maximizes the performance of the model. The test results show that the algorithm only requires image-level labeling, which can effectively reduce the labor and material consumption brought by pixel-level labeling while ensuring accuracy.

Detection of Fatigue Cracks for Concrete Structures by Using Carbon Ink-Based Conductive Skin and Electrical Resistance Tomography.

Concrete is among the most widely used structural materials in buildings and bridges all over the world. During their service life, concrete structures may inevitably display cracks due to long-term fatigue loads, leading to the degradation of structural integrity. Thus, it is very important to detect cracks and their growth in concrete structures using an automated structural health monitoring system. In this paper, experimental research on crack detection and imaging of concrete structures by using sensing skin and electrical resistance tomography (ERT) is presented. Carbon ink is screen-printed on the surface of concrete as a conductive material to form sensing skins. With these sensing skins, when cracks occur on or near the surface, it breaks the continuity of the sensing skins and significantly reduces conductivity in cracking areas. Then, after exciting small currents in sensing skins and measuring related voltage data, an inverse analysis based on total variation (TV) regularization is adopted to reconstruct tomographic images showing conductivity changes in sensing skins, to detect the occurrence and growth of cracks. The effectiveness of conductive sensing skins and our related crack detection method is validated in experimental studies on a concrete beam subjected to fatigue tests.

A novel criterion for crack detection in beam structures by frequency response functions

The frequency response function (FRF) is a fruitful attribute that includes almost all dynamical characteristics of a structure such as natural frequencies and modes, damping coefficients, or resonance and antiresonance conceptions. However, the complex feature of FRF has not been thoroughly employed for structural damage detection. In the present study, a novel indicator extracted from FRFs of beam structures is developed for crack identification. The damage indicator originated from the well-known mode assurance criterion (MAC) and therefore it is termed spectral assurance criterion (SAC). First, a coherence coefficient calculated from FRFs of intact and damaged beams and called herein spectral damage index (SDI) is analyzed for examining sensitivity of FRFs to crack. Then, SAC calculated for different FRFs of the same damaged structure is employed for crack detection by the so-called contour method. Results obtained in numerical examples of the crack detection problem show that SAC is really a novel and efficient criterion for crack identification in beams from measured FRFs.

Enhancing Structural Crack Detection through a Multiscale Multilevel Mask Deep Convolutional Neural Network and Line Similarity Index

This paper proposes a novel and practical crack-detection method for infrastructure. The proposed method exhibits three key components. First, a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) is proposed to accurately estimate crack candidates comprising linear and curvilinear features. Second, the proposed neural network is trained using only public image-sets. The main principle of this approach is that cracks have unique and distinct features, and therefore, public image-sets provide sufficient information to estimate crack candidates for a neural network. Third, a line similarity index (LSI), which is calculated using the Hough transform and coordinate transformation with principal component analysis, is incorporated to eliminate non-crack candidates from crack candidates based on two key characteristics: the variation in crack features with respect to the representative line and the number of crack features that crossed the representative line. Addressing these two crack-related characteristics improves accuracy and robustness by effectively eliminating non-crack features. Field tests performed inside a building and in an underground power tunnel demonstrated the effectiveness of the proposed method. The MSML Mask DCNN outperformed other neural networks, accurately recognizing local crack candidates characterized by linear and curvilinear features even though only public image-sets were used for training. The proposed LSI also effectively eliminated non-crack candidates estimated by the MSML Mask DCNN. The proposed method is practical for real-world applications, where several non-crack objects and noises are typically present.

Crack Detection of Concrete Images Using Dilatation and Crack Detection Algorithms

Crack detection in structures is an important and time-consuming element of monitoring the health of structures and ensuring structural safety. The traditional visual inspection of structures can be unsafe and may produce inconsistent results. Thus, there is a need for a method to easily and accurately identify and analyze cracks. In this study, algorithms for automatically detecting the size and location of cracks in concrete images were developed. Cracks were automatically detected in a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy, where 1000 crack images and 1000 non-crack images were used, respectively. In the crack image, 100% of the cracks were detected, but 95.3% of the results were very good, even if the results that were not bad in terms of quality were excluded. In addition, the accuracy of detecting non-crack images was also very good (96.9%). Thus, it is expected that the crack detection algorithm presented in this study will be able to detect the location and size of cracks in concrete. Moreover, these algorithms will help in observing the soundness of structures and ensuring their safety.

Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection

The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works.

Modification and Evaluation of Attention-Based Deep Neural Network for Structural Crack Detection.

Cracks are one of the safety-evaluation indicators for structures, providing a maintenance basis for the health and safety of structures in service. Most structural inspections rely on visual observation, while bridges rely on traditional methods such as bridge inspection vehicles, which are inefficient and pose safety risks. To alleviate the problem of low efficiency and the high cost of structural health monitoring, deep learning, as a new technology, is increasingly being applied to crack detection and recognition. Focusing on this, the current paper proposes an improved model based on the attention mechanism and the U-Net network for crack-identification research. First, the training results of the two original models, U-Net and lrassp, were compared in the experiment. The results showed that U-Net performed better than lrassp according to various indicators. Therefore, we improved the U-Net network with the attention mechanism. After experimenting with the improved network, we found that the proposed ECA-UNet network increased the Intersection over Union (IOU) and recall indicators compared to the original U-Net network by 0.016 and 0.131, respectively. In practical large-scale structural crack recognition, the proposed model had better recognition performance than the other two models, with almost no errors in identifying noise under the premise of accurately identifying cracks, demonstrating a stronger capacity for crack recognition.

A novel DISTINCT method for characterizing breathing features of nonlinear damage in structures

The detection of breathing cracks in in-service structures is an intractable issue due to the distinctive dynamic behavior, typically the opening and closing of the crack subjected to cyclic loading. The existing methods to identify breathing cracks generally rely on the hypothesis of the crack-induced nonlinear features, mostly represented by super-harmonics. Nevertheless, an in-service structure even free of cracking somehow has nonlinear components in material, geometry, boundary etc., such that it tends to present inherent nonlinear features, i.e., nonlinearities prior to cracking, which could impair the effectiveness of super harmonics-like methods to depict breathing cracks. In such a circumstance, a sophisticated method that can characterize breathing cracks-induced nonlinearity by accommodating inherent nonlinear of the structure is desired. Herein, a novel method, termed DISTINCT (Difference in Split Temporal signal with Inherent Nonlinear Components Tolerated) method, is proposed. This method is formulated by splitting a whole response of a structure bearing breathing cracks into positive and negative response components. Each response component largely reflects structural dynamic behavior with regard to the crack-opening state or crack-closing state. From positive or negative response components, the dynamic property such as the frequency spectrum arising from the Fast Fourier Transform, corresponding to crack-opening state or crack-closing state, can be derived. To this end, the differences between the two frequency spectra relatively purely manifests the characteristic of breathing cracks including their presence and severity. The feasibility of the DISTINCT method is numerically verified via the identification of breathing crack in a cantilever beam and a shell, with emphasis on its stronger noise resistance than the traditional super-harmonics-based method. Furthermore, the method’s effectiveness is experimentally validated via the inspection of breathing cracks in a scaled arch dam model subject to excitations induced by artificial seismic accelerations. The results show that the proposed DISTINCT method can quantify the severities of four levels of damage comprising breathing cracks by tolerating inherent nonlinearity of the structure. The advantages of the DISTINCT lie in its tolerance to structural inherent nonlinearities and requiring no numerical or physical benchmarks nor any prior knowledge of the material properties and boundary conditions in assessing breathing cracks of structures.

Multi-objective acoustic sensor placement optimization for crack detection of compressor blade based on reinforcement learning

Nowadays, acoustic sensors have been widely applied for structural health monitoring and crack detection of compressor blades. As the detection accuracy is mainly affected by signal quality, the optimal sensor placement (OSP) is significant for crack detection. To search the OSP for reliable signals, the multi-objective acoustic sensor placement optimization method is proposed based on information field fitting and optimization with reinforcement learning. First, the improved ridge and least squares support vector regression (R-LS-SVR) method is presented to fit information field of acoustic signal. Besides, the multi-objective function is constructed based on the compressor operation conditions, sound quality and crack damage observation. In addition, the Pareto front is obtained to realize multi-objective optimization (MOO) based on non-dominated sorting genetic algorithms (NSGA-II). Furtherly, the reinforcement learning-based pattern search method (RL-PSM) is proposed to quickly search the OSP of acoustic sensor. The compressor experiments are implemented to test the proposed method, and it can detect crack with the accuracy of 99.17%, which is superior to other locations. Comparing with other fitting and optimization methods, the advantage of the proposed method is validated for OSP optimization and crack detection.

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

A method of steel structure surface crack identification based on artificial intelligence technology is proposed to solve the problem that steel cracks can not be detected and forewarned in time when they appear in the railway industrial environment. The appearance of steel cracks greatly weakens the stability of steel structures, and will seriously endanger the safety of the railway industry if it is not detected and repaired in time. However, the common steel crack detection methods cannot achieve real-time monitoring of steel structures. In order to monitor the surface of steel structure in real-time and explore the recognition effect and model the advantages of common classification neural network models for surface cracks of railway industrial steel, this study evaluates the network model with multiple indicators and parameters under two experimental conditions. In this study, the steel surface cracks in the railway industrial environment are taken as samples, and the steel cracks are identified through the neural network model. For large-volume datasets, the recognition accuracy of the three network models has reached 97%, of which the YOLOv5 model has the best comprehensive recognition ability, and the C-Alex model has the best performance and convergence speed in small-volume datasets. This study explores the application prospects of models under different scenarios, proving that the three models can effectively detect steel surface cracks in real-time, and at the same time, it will pave the way for the development and application of artificial intelligence multi-model fusion technology in the field of the railway industry.

Transfer and Unsupervised Learning: An Integrated Approach to Concrete Crack Image Analysis

The detection of cracks in concrete structures is crucial for the assessment of their structural integrity and safety. To this end, detection with deep neural convolutional networks has been extensively researched in recent years. Despite their success, these methods are limited in classifying concrete as cracked or non-cracked and disregard other characteristics, such as the severity of the cracks. Furthermore, the classification process can be affected by various sources of interference and noise in the images. In this paper, an integrated methodology for analysing concrete crack images is proposed using transfer and unsupervised learning. The method extracts image features using pre-trained networks and groups them based on similarity using hierarchical clustering. Three pre-trained networks are used for this purpose, with Inception v3 performing the best. The clustering results show the ability to divide images into different clusters based on image characteristics. In this way, various clusters are identified, such as clusters containing images of obstruction, background debris, edges, surface roughness, as well as cracked and uncracked concrete. In addition, dimensionality reduction is used to further separate and visualise the data, making it easier to analyse clustering results and identify misclassified images. This revealed several mislabelled images in the dataset used in this study. Additionally, a correlation was found between the principal components and the severity of cracks and surface imperfections. The results of this study demonstrate the potential of unsupervised learning for analysing concrete crack image data to distinguish between noisy images and the severity of cracks, which can provide valuable information for building more accurate predictive models.

Differential two-wave mixing interferometer for crack detection in metallic structures based on laser-induced ultrasound

To meet the need of high-sensitivity crack detection in metallic structures with a contactless method, a differential two-wave mixing interferometer based on two nonlinear Bi12SiO20 photorefractive crystals is proposed for laser-induced ultrasound signal measurement. The two Bi12SiO20 crystals are applied with the electric fields of opposite polarities to obtain the dual-path differential signals corresponding to the laser-induced ultrasound under detection, in which the noise is not affected by the polarity of the applied electric field. The signal noise can be effectively suppressed and the sensitivity is doubled by the differential processing of the dual-path signals, by which the high-sensitivity detection of laser-induced ultrasound can be achieved to characterize the crack in metallic structures. The further optimization of system parameters is performed to obtain the optimal detection sensitivity. Both the numerical analysis and experiments have been carried out to demonstrate the feasibility of the proposed method. The proposed system provides a powerful and reliable tool for contactless detection of laser-induced ultrasound and characterization of metal cracks.

Metaheuristic-based crack detection in beam-type structures using peridynamics theory: A comparative study

This study is a first attempt to provide an effective tool for crack prediction in beam-type structures using the peridynamics (PD) theory and metaheuristic-based optimization. The norm of the difference between the response of the tested beam and that of the trial model (analyzed using PD), is minimized to find the position and depth of crack. Model calibration is discussed thoroughly. Five well-known metaheuristics as well as an upgraded charged system search algorithm are considered. Results reveal that the proposed procedure can effectively localize and detect the crack severity, even in noise contaminated cases.

Application of machine learning for crack detection on concrete structures using CNN architecture

Cracks in concrete structures are caused due to contraction and expansion irregularities, from potential damages caused in the buildings. These irregularities and damages are assessed by the engineers manually or through identification and prediction models with machine learning techniques to evaluate the impact and significance of the structural health in buildings. This research aims at applying machine learning based on VGG16-Net model for the detection of cracks in concrete structures. The proposed model is of CNN (convolutional neural network) + VGG neural network based architecture. The study uses the gradient boosting algorithm for image segmentation. The datasets are obtained from “Kaggle” resource and the library used is ‘Hugging Face Transformers’. To evaluate the developed models’ performance metrics such as “accuracy, precision, recall and f1-score” are used. The ‘accuracy’ score obtained is compared against the ‘ViT’ (Google transformer) accuracy rate, for comparison. The proposed model achieved 98% validation accuracy rate with 0.3% loss. Thus the developed research contributes an innovative and a novel ML model that predicts and identifies the cracks in concrete structures with less loss and higher accuracy with CNN architecture than ViT (vision transformer) models. Current study also provides more input upon CNN being more accurate than ViT models for future researchers for comparative analyses.

Crack detection in arbitrary beam cross-sections using a new mass-spring system

This paper presents a new system of spring masses for crack detection in structures. For modeling cracks in arbitrary cross-sections, the Gaussian quadrature method is used to obtain the integral of the energy release rate over the cracked cross-section. In the proposed method, the system of spring masses is used to obtain the time histories of displacements, which are then combined into a relative time history by weighted summation. For detecting cracks, three springs and three equal masses with equal spacing from each other are placed on the beam, starting to move consecutively from one end. At each station, the time history of the system under excitation by an external load applied to the middle mass is extracted. The extracted data in each station shows the relative stiffness of that station compared to adjacent stations. The differences in system responses are used to estimate the position of cracks. The performance of the proposed method was evaluated using the Z24 bridge and a three-span concrete girder bridge in which a multiple-crack scenario was applied. These examples were used for evaluating the proposed system for the effect of parameters such as the weight and spacing of masses. The novelty of the presented method is to model cracks in arbitrary beam cross-sections and identify cracks in real bridge beams using a new effective mass-spring system that uses only three sensor data.

Crack Growth Monitoring with Structure-Bonded Thin and Flexible Coils.

Structural health monitoring with thin and flexible eddy-current coils is proposed for in situ detection and monitoring of fatigue cracks in metallic aircraft structures, providing a promising means of crack sizing. This approach is seen as an efficient replacement to periodic inspections, as it brings economic and safety benefits. As such, printed-circuit-board eddy-current coils are viable for in situ crack monitoring for multi-layer, electrically conductive structures. They are minimally invasive and could be attached to or embedded into the evaluated structure. This work focuses on the monitoring of fatigue crack growth from a fastener hole with structure-bonded, thin, and flexible spiral coils. Numerical simulations were used for optimization of the driving frequency and selection of crack-sensitive coil parameters. The article also demonstrates the fatigue crack detection capabilities using spiral coils attached to a 7075-T6 aluminum coupon.