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Ju-Won Kim
Department of Safety Engineering, Dongguk University-Gyeongju, Gyeongju 38066, Korea

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Journal article
Published: 17 September 2020 in Sensors
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The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it. First, a Q-switched Nd:YAG pulsed laser and an acoustic emission sensor were used as exciting and sensing ultrasonic signals, respectively. Then, a 3D full-field ultrasonic data set was created using an ultrasonic wave propagation imaging (UWPI) process, after which several signal processing techniques were applied to generate the processed data. By using a deep convolutional neural network (DCNN) with a VGG-like architecture based regression model, the estimated error was calculated to compare the performance of a DCNN on different processed data set. The proposed approach was also compared with a K-nearest neighbor, support vector regression, and deep artificial neural network for regression to demonstrate its robustness. Consequently, it was found that the proposed approach shows potential for the incorporation of laser-generated ultrasound and DL algorithms. In addition, the signal processing technique has been shown to have an important impact on the DL performance for automatic looseness estimation.

ACS Style

Dai Quoc Tran; Ju-Won Kim; Kassahun Demissie Tola; Wonkyu Kim; Seunghee Park. Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data. Sensors 2020, 20, 5329 .

AMA Style

Dai Quoc Tran, Ju-Won Kim, Kassahun Demissie Tola, Wonkyu Kim, Seunghee Park. Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data. Sensors. 2020; 20 (18):5329.

Chicago/Turabian Style

Dai Quoc Tran; Ju-Won Kim; Kassahun Demissie Tola; Wonkyu Kim; Seunghee Park. 2020. "Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data." Sensors 20, no. 18: 5329.

Research article
Published: 21 June 2020 in Structural Control and Health Monitoring
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This paper proposes a bolt looseness detection method based on the ultrasonic wavefield energy in the Lamb wave reflected from a target bolt. The full wavefield inside the area of the specimen containing the target bolt was first visualized. The ultrasonic wave propagation imaging (UWPI) approach was used for visualizing the interaction of the wavefield with the elements on the specimen such as the bolts and the boundaries of the specimen. The interaction between the ultrasonic wave and the airgap between the bolt head and the plate was interpreted based on the microcontact theory. This theory, when applied to the interface of two contacting surfaces such as those joined by a bolt, generally states that by increasing the fastening torque, the true contact area will increase and the wave will propagate across the interface with less energy loss. To apply the theory, the reflected wave from the target bolt was isolated from the incident wave and was further assessed to extract a feature indicating the looseness or tightness. The variation of the energy in the wave reflected from the target bolt with varying magnitudes of torque was determined by performing a continuous wavelet transform‐based energy computation. The reflected wave energies of the different torque values were first compared by considering single time instants starting with the moment the wave was first observed in the UWPI reflecting from the target bolt. Then, energy accumulation was used to obtain the overall energy differences that resulted solely from the torque differences.

ACS Style

Kassahun Demissie Tola; Changgil Lee; Jooyoung Park; Ju‐Won Kim; Seunghee Park. Bolt looseness detection based on ultrasonic wavefield energy analysis using an Nd:YAG pulsed laser scanning system. Structural Control and Health Monitoring 2020, 27, 1 .

AMA Style

Kassahun Demissie Tola, Changgil Lee, Jooyoung Park, Ju‐Won Kim, Seunghee Park. Bolt looseness detection based on ultrasonic wavefield energy analysis using an Nd:YAG pulsed laser scanning system. Structural Control and Health Monitoring. 2020; 27 (9):1.

Chicago/Turabian Style

Kassahun Demissie Tola; Changgil Lee; Jooyoung Park; Ju‐Won Kim; Seunghee Park. 2020. "Bolt looseness detection based on ultrasonic wavefield energy analysis using an Nd:YAG pulsed laser scanning system." Structural Control and Health Monitoring 27, no. 9: 1.

Research article
Published: 09 October 2019 in Journal of Sensors
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In the real world, the main cables of suspension bridges are commonly inspected by conducting a periodic visual inspection of the exterior cover of the cable. Although there is a need to conduct a nondestructive evaluation (NDE) of the damage of the main cable, a suitable NDE technique has not yet been developed due to the large diameter and low accessibility of the cable. This study investigates a magnetic sensing cross-sectional loss quantification method that can detect internal and external damage to the main cables. This main cable NDE method applies an extremely low-frequency alternating current (ELF-AC) magnetization method and search coil sensor-based total flux measurement. A total flux sensor head consists of a magnetization yoke and a search coil sensor. To magnetize the main cable, a magnetic field was generated by applying a triangular ELF-AC voltage to the electromagnet yoke. The sensing part measures the magnetic flux that passes through the search coil, and the B-H loop was then obtained using the relationship between the ELF-AC voltage that has been input and the total flux that was measured. Also, the cross-sectional loss can be quantified using a variation of magnetic features from the B-H loop. To verify the feasibility of using the proposed NDE technique, a series of experiments were performed using a main cable specimen with a gradual increase in the cross-sectional loss. Finally, the relationship between the cross-sectional loss and extracted magnetic feature was determined and used to quantify the cross-sectional loss via the proposed method.

ACS Style

Ju-Won Kim; Junkyeong Kim; Seunghee Park. Cross-Sectional Loss Quantification for Main Cable NDE Based on the B-H Loop Measurement Using a Total Flux Sensor. Journal of Sensors 2019, 2019, 1 -10.

AMA Style

Ju-Won Kim, Junkyeong Kim, Seunghee Park. Cross-Sectional Loss Quantification for Main Cable NDE Based on the B-H Loop Measurement Using a Total Flux Sensor. Journal of Sensors. 2019; 2019 ():1-10.

Chicago/Turabian Style

Ju-Won Kim; Junkyeong Kim; Seunghee Park. 2019. "Cross-Sectional Loss Quantification for Main Cable NDE Based on the B-H Loop Measurement Using a Total Flux Sensor." Journal of Sensors 2019, no. : 1-10.

Journal article
Published: 07 September 2019 in Materials
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Wire ropes used in various applications such as elevators and cranes to safely carry heavy weights are vulnerable to breakage or cross-sectional loss caused by the external environment. Such damage can pose a serious risk to the safety of the entire structure because damage under tensile force rapidly expands due to concentration of stress. In this study, the magnetic flux leakage (MFL) method was applied to diagnose cuts, corrosion, and compression damage in wire ropes. Magnetic flux signals were measured by scanning damaged wire rope specimens using a multi-channel sensor head and a compact data acquisition system. A series of signal-processing procedures, including the Hilbert transform-based enveloping process, was applied to reduce noise and improve the resolution of signals. The possibility of diagnosing several types of damage was verified using enveloped magnetic flux signals. The characteristics of the MFL signals according to each damage type were then analyzed by comparing the extracted damage indices for each damage type. For automated damage type classification, a support vector machine (SVM)-based classifier was trained using the extracted damage indices. Finally, damage types were automatically classified as cutting and other damages using the trained SVM classifier.

ACS Style

Ju-Won Kim; Kassahun Demissie Tola; Dai Quoc Tran; Seunghee Park. MFL-Based Local Damage Diagnosis and SVM-Based Damage Type Classification for Wire Rope NDE. Materials 2019, 12, 2894 .

AMA Style

Ju-Won Kim, Kassahun Demissie Tola, Dai Quoc Tran, Seunghee Park. MFL-Based Local Damage Diagnosis and SVM-Based Damage Type Classification for Wire Rope NDE. Materials. 2019; 12 (18):2894.

Chicago/Turabian Style

Ju-Won Kim; Kassahun Demissie Tola; Dai Quoc Tran; Seunghee Park. 2019. "MFL-Based Local Damage Diagnosis and SVM-Based Damage Type Classification for Wire Rope NDE." Materials 12, no. 18: 2894.

Journal article
Published: 02 January 2018 in Sensors
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In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

ACS Style

Ju-Won Kim; Seunghee Park. Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation. Sensors 2018, 18, 109 .

AMA Style

Ju-Won Kim, Seunghee Park. Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation. Sensors. 2018; 18 (2):109.

Chicago/Turabian Style

Ju-Won Kim; Seunghee Park. 2018. "Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation." Sensors 18, no. 2: 109.

Journal article
Published: 30 August 2017 in Sensors
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The tensile force of pre-stressed concrete (PSC) girders is the most important factor for managing the stability of PSC bridges. The tensile force is induced using pre-stressing (PS) tendons of a PSC girder. Because the PS tendons are located inside of the PSC girder, the tensile force cannot be measured after construction using conventional NDT (non-destructive testing) methods. To monitor the induced tensile force of a PSC girder, an embedded EM (elasto-magnetic) sensor was proposed in this study. The PS tendons are made of carbon steel, a ferromagnetic material. The magnetic properties of the ferromagnetic specimen are changed according to the induced magnetic field, temperature, and induced stress. Thus, the tensile force of PS tendons can be estimated by measuring their magnetic properties. The EM sensor can measure the magnetic properties of ferromagnetic materials in the form of a B (magnetic density)-H (magnetic force) loop. To measure the B-H loop of a PS tendon in a PSC girder, the EM sensor should be embedded into the PSC girder. The proposed embedded EM sensor can be embedded into a PSC girder as a sheath joint by designing screw threads to connect with the sheath. To confirm the proposed embedded EM sensors, the experimental study was performed using a down-scaled PSC girder model. Two specimens were constructed with embedded EM sensors, and three sensors were installed in each specimen. The embedded EM sensor could measure the B-H loop of PS tendons even if it was located inside concrete, and the area of the B-H loop was proportionally decreased according to the increase in tensile force. According to the results, the proposed method can be used to estimate the tensile force of unrevealed PS tendons.

ACS Style

Junkyeong Kim; Ju-Won Kim; Chaggil Lee; Seunghee Park. Development of Embedded EM Sensors for Estimating Tensile Forces of PSC Girder Bridges. Sensors 2017, 17, 1989 .

AMA Style

Junkyeong Kim, Ju-Won Kim, Chaggil Lee, Seunghee Park. Development of Embedded EM Sensors for Estimating Tensile Forces of PSC Girder Bridges. Sensors. 2017; 17 (9):1989.

Chicago/Turabian Style

Junkyeong Kim; Ju-Won Kim; Chaggil Lee; Seunghee Park. 2017. "Development of Embedded EM Sensors for Estimating Tensile Forces of PSC Girder Bridges." Sensors 17, no. 9: 1989.

Journal article
Published: 17 August 2013 in KSCE Journal of Civil Engineering
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The demand for the construction of high-rise buildings and wide-span bridges using High Strength Concrete (HSC) has increased. To prevent unexpected collapse during and after the construction of HSC structures, it is essential to confirm the strength development of HSC during the curing process. In this study, a novel method to estimate the strength development of HSC based on Electromechanical Impedance (EMI) measurements using a wireless sensor node is proposed. The EMI of HSC structures with 100 MPa strength was tracked to monitor the strength development. In addition, two types of signal processing, resonant frequency shift and cross-correlation coefficient were applied in sequence to examine the trend of the impedance variations more quantitatively. The results confirmed that the proposed technique can be applied successfully to more reliable and economical monitoring of the strength development during the curing process of HSC structures.

ACS Style

Ju-Won Kim; Changgil Lee; Seunghee Park; Kyung-Taek Koh. Real-time strength development monitoring for concrete structures using wired and wireless electro-mechanical impedance techniques. KSCE Journal of Civil Engineering 2013, 17, 1432 -1436.

AMA Style

Ju-Won Kim, Changgil Lee, Seunghee Park, Kyung-Taek Koh. Real-time strength development monitoring for concrete structures using wired and wireless electro-mechanical impedance techniques. KSCE Journal of Civil Engineering. 2013; 17 (6):1432-1436.

Chicago/Turabian Style

Ju-Won Kim; Changgil Lee; Seunghee Park; Kyung-Taek Koh. 2013. "Real-time strength development monitoring for concrete structures using wired and wireless electro-mechanical impedance techniques." KSCE Journal of Civil Engineering 17, no. 6: 1432-1436.