• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.4
• /
• pp.290-296
• /
• 2005

This paper presents the implementation of a crack tracking mobile robot. The crack tracking robot is built for tracking cracks on the pavement. To track cracks, crack must be detected by laser and camera sensors. Laser sensor projects laser on the pavement to detect the discontinuity on the surface and the camera captures the image to find the crack position. Then the robot is commanded to follow the crack. To detect crack position correctly, neural network is used to minimize the positional errors of the captured crack position obtained by transformation from 2 dimensional images to 3 dimensional images.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.9 no.6
• /
• pp.78-86
• /
• 2010

This paper aims to investigate the natural frequency of a cracked cantilever L-beams with a coupled bending and torsional vibrations. In addition, a theoretical method for detection of the crack position and size in a cantilever L-beams is presented based on natural frequencies. Based on the Euler-Bernoulli beam theory, the equation of motion is derived by using extended Hamilton's Principle. The dynamic transfer matrix method is used for calculation of a exact natural frequencies of L-beams. In order to detect the crack of L-beams, the effect of spring coefficients for bending moment and torsional force is included. In this study, the differences between the actual data and predicted positions and sizes of crack are less than 0.5% and 6.7% respectively.

• Proceedings of the IEEK Conference
• /
• 2007.07a
• /
• pp.381-382
• /
• 2007

In this paper, crack detection technique of concrete bridge is proposed robust against shadow and noise. Our technique consists of two steps. In the first step, crack candidate region is detected by preprocessing. Preprocessing techniques such as median filter, isolated point filter and morphological techniques, use utilized for detection of crack candidate regions. In the final step, crack is detected from crack candidate region by considering any connectivity between cracks. By experimental results, performance is improved 6.8% over the existing method.

• Journal of the Korean Society for Precision Engineering
• /
• v.27 no.6
• /
• pp.47-54
• /
• 2010

In this paper, the purpose is to study a method for detection of crack in clamped-clamped beams using the vibration characteristics. The natural frequency of beam is obtained by FEM and experiment. The governing differential equations of a Timoshenko beam are derived via Hamilton's principle. The two coupled governing differential equations are reduced to one fourth order ordinary differential equation in terms of the flexural displacement. The crack is assumed to be in the first mode of fracture and to be always opened during the vibrations. The differences between the actual and predicted crack positions and sizes are less than 9.8% and 28%, respectively.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2022.11a
• /
• pp.245-246
• /
• 2022

Typically, the methods of crack detection on concrete structures include some problems, such as a low accuracy and expensive. To solve these problems, we proposed a neural network-based crack search method. The proposed algorithm goes through three convolutions and is classified into crack and non-crack through the softmax layer. As a result of the performance evaluation, cracks can be detected with an accuracy of 99.4 and 99.34 % at the training model and the validation model, respectively.

• Structural Engineering and Mechanics
• /
• v.69 no.4
• /
• pp.407-416
• /
• 2019

This paper develops a wireless sensor for online fatigue crack detection and failure warning based on crack-induced nonlinear ultrasonic modulation. The wireless sensor consists of packaged piezoelectric (PZT) module, an excitation/sensing module, a data acquisition/processing module, a wireless communication module, and a power supply module. The packaged PZT and the excitation/sensing module generate ultrasonic waves on a structure and capture the response. Based on nonlinear ultrasonic modulation created by a crack, the data acquisition/processing module periodically performs fatigue crack diagnosis and provides failure warning if a component failure is imminent. The outcomes are transmitted to a base through the wireless communication module where two-levels duty cycling media access control (MAC) is implemented. The uniqueness of the paper lies in that 1) the proposed wireless sensor is developed specifically for online fatigue crack detection and failure warning, 2) failure warning as well as crack diagnosis are provided based on crack-induced nonlinear ultrasonic modulation, 3) event-driven operation of the sensor, considering rare extreme events such as earthquakes, is made possible with a power minimization strategy, and 4) the applicability of the wireless sensor to steel welded members is examined through field and laboratory tests. A fatigue crack on a steel welded specimen was successfully detected when the overall width of the crack was around $30{\mu}m$, and a failure warnings were provided when about 97.6% of the remaining useful fatigue lives were reached. Four wireless sensors were deployed on Yeongjong Grand Bridge in Souht Korea. The wireless sensor consumed 282.95 J for 3 weeks, and the processed results on the sensor were transmitted up to 20 m with over 90% success rate.

• Proceeding of EDISON Challenge
• /
• 2016.03a
• /
• pp.195-202
• /
• 2016

In this paper, crack detection method using eigen value analysis is presented. Three methods are used: theoretical analysis, finite element method with the cracked beam elements and finite element method with three dimensional continuum elements. Finite element formulation of the cracked beam element is introduced. Additional term about stress intensity factor based on fracture mechanics theory is added to flexibility matrix of original beam to model the crack. As using calculated stiffness matrix of cracked beam element and mass matrix, natural frequencies are calculated by eigen value analysis. In the case of using continuum elements, the natural frequencies could be calculated by using EDISON CASAD solver. Several cases of crack are simulated to obtain natural frequencies corresponding the crack. The surface of natural frequency is plotted as changing with crack location and depth. Inverse analysis method is used to find crack location and depth from the natural frequencies of experimental data, which are referred by another papers. Predicted results are similar with the true crack location and depth.

• Computers and Concrete
• /
• v.21 no.6
• /
• pp.727-739
• /
• 2018

Crack is the most common typical feature of concrete deterioration, so routine monitoring and health assessment become essential for identifying failures and to set up an appropriate rehabilitation strategy in order to extend the service life of concrete structures. At present, image segmentation algorithms have been applied to crack analysis based on inspection images of concrete structures. The results of crack segmentation offering crack information, including length, width, and area is helpful to assist inspectors in surface inspection of concrete structures. This study proposed an algorithm of image segmentation enhancement, named morphological segmentation based on edge detection-II (MSED-II), to concrete crack segmentation. Several concrete pavement and building surfaces were imaged as the study materials. In addition, morphological operations followed by cross-curvature evaluation (CCE), an image segmentation technique of linear patterns, were also tested to evaluate their performance in concrete crack segmentation. The result indicates that MSED-II compared to CCE can lead to better quality of concrete crack segmentation. The least area, length, and width measurement errors of the concrete cracks are 5.68%, 0.23%, and 0.00%, respectively, that proves MSED-II effective for automatic measurement of concrete cracks.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.36 no.6
• /
• pp.451-459
• /
• 2016

Crack detection on panels during manufacturing process is an important step for ensuring the product quality. The accuracy and efficiency of traditional crack detection methods, which are performed by eye inspection, are dependent on human inspectors. Therefore, implementation of an on-line and precise crack detection is required during the panel pressing process. In this paper, a regular CCTV camera system is utilized to obtain images of panel products and an image process based crack detection technique is developed. This technique uses a comparison between the base image and a test image using an amplitude mapping of the local image. Experiments are performed in the laboratory and in the actual manufacturing lines to evaluate the performance of the developed technique. Experimental results indicate that the proposed technique could be used to effectively detect a crack on panels with high speed.

• Journal of the Korean Society of Safety
• /
• v.34 no.6
• /
• pp.50-57
• /
• 2019

In this study, various types of deep learning models that have been proposed recently are classified according to data input / output types and analyzed to find the deep learning model suitable for constructing a crack detection model. First the deep learning models are classified into image classification model, object segmentation model, object detection model, and instance segmentation model. ResNet-101, DeepLab V2, Faster R-CNN, and Mask R-CNN were selected as representative deep learning model of each type. For the comparison, ResNet-101 was implemented for all the types of deep learning model as a backbone network which serves as a main feature extractor. The four types of deep learning models were trained with 500 crack images taken from real concrete structures and collected from the Internet. The four types of deep learning models showed high accuracy above 94% during the training. Comparative evaluation was conducted using 40 images taken from real concrete structures. The performance of each type of deep learning model was measured using precision and recall. In the experimental result, Mask R-CNN, an instance segmentation deep learning model showed the highest precision and recall on crack detection. Qualitative analysis also shows that Mask R-CNN could detect crack shapes most similarly to the real crack shapes.