• Smart Structures and Systems
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• v.18 no.6
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• pp.1217-1231
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• 2016

In this research, a slotted patch antenna sensor is designed for wireless strain and crack sensing. An off-the-shelf RFID (radiofrequency identification) chip is adopted in the antenna sensor design for signal modulation. The operation power of the RFID chip is captured from wireless reader interrogation signal, so the sensor operation is completely battery-free (passive) and wireless. For strain and crack sensing of a structure, the antenna sensor is bonded on the structure surface like a regular strain gage. Since the antenna resonance frequency is directly related with antenna dimension, which deforms when strain occurs on the structural surface, the deformation/strain can be correlated with antenna resonance frequency shift measured by an RFID reader. The slotted patch antenna sensor performance is first evaluated through mechanics-electromagnetics coupled simulation. Extensive experiments are then conducted to validate the antenna sensor performance, including tensile and compressive strain sensing, wireless interrogation range, and fatigue crack sensing.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.69-74
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• 2018

Fiber-Reinforced Cementitious Composites (FRCCs) have electrical conductivity by inserting reinforced conductive fibers into a cementitious matrix. Such characteristic allows us to utilize FRCCs for crack monitoring of a structure by measuring electrical responses without sensor installation. However, the electrical responses are often sensitively altered by temperature variation as well as crack initiation. The temperature variation may disturb crack detection on the measured electrical responses. Moreover, as sensing probes for measuring electrical reponses increase, undesired contact noises are often augmented. In this paper, a self-sensing impedance circuit is specially designed for reducing the number of sensing probes. The crack initiation and temperature variation effects on the self-sensing impedance responses of FRCCs are experimentally investigated using the self-sensing impedance circuit. The experiment results reveal that the electrical impedance response are more sensitively changed due to temperature variation than crack initiation.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• Smart Structures and Systems
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• v.29 no.1
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• Smart Structures and Systems
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• v.22 no.1
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• pp.1-11
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• 2018

This study presents early crack detection of steel fiber-reinforced concrete (SFRC) under varying temperature and humidity conditions using an instantaneous electrical impedance acquisition system. SFRC has the self-sensing capability of electrical impedance without sensor installation thanks to the conductivity of embedded steel fibers, making it possible to effectively monitor cracks initiated in SFRC. However, the electrical impedance is often sensitively changed by environmental effects such as temperature and humidity variations. Thus, the extraction of only crack-induced feature from the measured impedance responses is a crucial issue for the purpose of structural health monitoring. In this study, the instantaneous electrical impedance acquisition system incorporated with SFRC is developed. Then, temperature, humidity and crack initiation effects on the impedance responses are experimentally investigated. Based on the impedance signal pattern observation, it is turned out that the temperature effect is more predominant than the crack initiation and humidity effects. Various crack steps are generated through bending tests, and the corresponding impedance damage indices are extracted by compensating the dominant temperature effect. The test results reveal that propagated cracks as well as early cracks are successfully detected under temperature and humidity variations.

• Smart Structures and Systems
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• v.4 no.5
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• pp.565-582
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• 2008

This paper presents a feasibility study of flexible piezoelectric paint for use in wide-band low-profile surface-mount or embeddable ultrasonic sensor for in situ structural health monitoring. Piezoelectric paint is a piezoelectric composite with 0-3 connectivity. Because of its ease of application, piezoelectric paint can be readily fabricated into sensing element with complex pattern. This study examines the characteristics of piezoelectric paint in acoustic emission signal and ultrasonic guided wave sensing. A series of ultrasonic tests including pitch catch and pencil break tests were performed to validate the ultrasonic wave sensing capability of piezoelectric paint. The results of finite element simulation of ultrasonic wave propagation, and acoustic emission generated by a pencil lead break on an aluminum plate are also presented in this paper along with corresponding experimental data. Based on the preliminary experimental results, the piezoelectric paint appears to offer a promising sensing material for use in real-time monitoring of crack initiation and propagation in both metallic and composite structures.

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

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.4
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• pp.1263-1274
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• 1996

In this paper, as elastic-plastic fracture toughness test under mixed mode loading was proposed using a single edge-cracked specimen subjected to bending moment(M), shearing force(F), and twisting moment(T). The J-integral of a crack in the specimen is expressed in the form J=$J_I$+ $J_II$$J_III$, where $J_I$, $J_II$ and $J_III$ are the components of mode I, mode II and mode III deformation, respectively. $J_I$, $J_II$ and $J_III$ can be estimated from M-$\theta$ ($\theta$;crack opening angle), F-U(U; crack shear displacement) and T-$\alpha$ ($\alpha$;crack twisting angle). In order to obtain the the M<-TEX>$\theta$, F-U and T-$\alpha$ diagram inreal time, a new deformaiton gage for mixed mode loading was proposed using the optical position sensing device(PSD). The elastic-plastic fracture toughness test was carried out with an aluminum alloy. The loading apparatus was designed and manufactured for this experiment. For the loading condition of the crack initatio in the mixed mode, the MMT -3(mode I+ mode II+ mode III) has the lowest values out of the all specimens. This implies that MMT-3 is possible of the crackinitation at lower load, if the specimen acts on together with the torque under the same loading condition. An elastic-plastic fracture toughness test using the PSD brings a successful experimentation in measuring the crack deformation(mode I+ mode II+ mode III).

• Composites Research
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• v.27 no.6
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• pp.219-224
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• 2014

Nancomposites manufacture has been developed rapidly, because of reinforcing effects of CNT in terms of mechanical, electrical and thermal properties. In this study, 10 wt% CNT paste was fabricated with good dispersion state and easy processability. Damage sensing and reinforcing effect of CNT paste were investigated in nanocomposites. 10 wt% CNT paste exhibited better tensile and flexural properties than those of general 1 wt% CNT nanocomposites. To observe the healing effect of CNT paste, a crack was made artificially with 30wt% CF30wt%/PP composites, and the CNT paste was filled inside the crack. The damage sensing of CNT paste in CF30wt%/PP composites was investigated by electrical resistance measurement and mechanical tests. CNT paste exhibited good reinforcing effect in mechanical properties of CF30wt%/PP composites, and this reinforcing effect was getting better with larger cracks. The reason was because CNT paste had good interfacial adhesion with CF30wt%/PP composites to resist crack propagation. In electrical resistance measurement, there was a jump in electrical resistance signal at the adhesion interface. The jumping signal could be used to predict fracture of CF/PP composites. CNT nanocomposites for damage sensing had crack reducing effect and damage detection using electrical resistance method.

• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.591-598
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• 2002

Leakage magnetic flux is occurred in the cracked area of magnetized specimens, and also it changes the magnetic domain area of the magneto-optical film positioned on the specimen. It causes the change of the optical permeability of the magnetic domain on the crack area. So crack images can be obtained easily using this principle. On the other hand, utilizing a laser in this method makes possible to perform a remote sensing by detecting the light intensity contrast between cracked area and normal area. This paper shows the application of non-destructive inspection system taking advantage of magneto-optical method for micro-cracks and presents examples applied to the several types of specimens having fatigue cracks and fabricated cracks using this method. Also the authors prove the possibility of this method as a remote sensing system under the oscillation load considering application to real fields.