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Concrete strength and factors affecting its development during early concrete curing are important research topics. Avoiding uncertain incidents during construction and in service life of structures requires an appropriate monitoring system. Therefore, numerous techniques are used to monitor the health of a structure. This paper presents a nondestructive testing technique for monitoring the strength development of concrete at early curing ages. Dispersed carbon nanotubes (CNTs) were used with cementitious materials and piezoelectric (PZT) material, a PZT ceramic, owing to their properties of intra electromechanical effects and sensitivity to measure the electromechanical impedance (EMI) signatures and relevant properties related to concrete strength, such as the elastic modulus, displacement, acceleration, strength, and loading effects. Concrete compressive strength, hydration temperature, mixture ratio, and variation in data obtained from the impedance signatures using fuzzy logic were utilized in the comparative result prediction method for concrete strength. These results were calculated using a fuzzy logic-based model considering the maturity method, universal testing machine (UTM) data, and analyzed EMI data. In the study, for data acquisition, a hybrid PZT–CNT sensor and a temperature sensor (Smart Rock) were embedded in the concrete to obtain the hydration temperature history to utilize the concrete maturity method and provide data on the EMI signatures. The dynamic changes in the medium caused during the phase in the concrete strengthening process were analyzed to predict the strength development process of concrete at early curing ages. Because different parameters are considered while calculating the concrete strength, which is related to its mechanical properties, the proposed novel method considers that changes in the boundary condition occurring in the concrete paste modify the resonant frequency response of the structure. Thus, relating and analyzing this feature can help predict the concrete strength. A comprehensive comparison of the results calculated using the proposed module, maturity method, and cylindrical specimens tested using the UTM proved that it is a cost-effective and fast technique to estimate concrete strength to ensure a safe construction of reinforced cement concrete infrastructures.
Najeebullah Tareen; Junkyeong Kim; Won-Kyu Kim; Seunghee Park. Fuzzy Logic-Based and Nondestructive Concrete Strength Evaluation Using Modified Carbon Nanotubes as a Hybrid PZT–CNT Sensor. Materials 2021, 14, 2953 .
AMA StyleNajeebullah Tareen, Junkyeong Kim, Won-Kyu Kim, Seunghee Park. Fuzzy Logic-Based and Nondestructive Concrete Strength Evaluation Using Modified Carbon Nanotubes as a Hybrid PZT–CNT Sensor. Materials. 2021; 14 (11):2953.
Chicago/Turabian StyleNajeebullah Tareen; Junkyeong Kim; Won-Kyu Kim; Seunghee Park. 2021. "Fuzzy Logic-Based and Nondestructive Concrete Strength Evaluation Using Modified Carbon Nanotubes as a Hybrid PZT–CNT Sensor." Materials 14, no. 11: 2953.
As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.
Sehwan Park; Jinpyung Kim; KyoYoung Jeon; Junkyeong Kim; Seunghee Park. Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3. Remote Sensing 2021, 13, 2011 .
AMA StyleSehwan Park, Jinpyung Kim, KyoYoung Jeon, Junkyeong Kim, Seunghee Park. Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3. Remote Sensing. 2021; 13 (10):2011.
Chicago/Turabian StyleSehwan Park; Jinpyung Kim; KyoYoung Jeon; Junkyeong Kim; Seunghee Park. 2021. "Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3." Remote Sensing 13, no. 10: 2011.
Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.
Michael Bekele Maru; Donghwan Lee; Kassahun Demissie Tola; Seunghee Park. Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections. Sensors 2020, 21, 201 .
AMA StyleMichael Bekele Maru, Donghwan Lee, Kassahun Demissie Tola, Seunghee Park. Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections. Sensors. 2020; 21 (1):201.
Chicago/Turabian StyleMichael Bekele Maru; Donghwan Lee; Kassahun Demissie Tola; Seunghee Park. 2020. "Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections." Sensors 21, no. 1: 201.
Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, the Republic of Korea, in April 2020. Then, the proposed two-patch-level deep-learning models were implemented. A patch-level 1 network was trained using the UNet++ architecture. The output prediction of this network was used as a position input for the second network, which used UNet. It took the reference position from the first network as its input and refined the results. Finally, the final performance of our proposed method was compared with a state-of-the-art image-segmentation algorithm to prove its robustness. Comparative research on the loss functions was also performed. Our proposed approach demonstrated its effectiveness in extracting burnt areas from UAV images and can contribute to estimating maps showing the areas damaged by forest fires.
Dai Tran; Minsoo Park; Daekyo Jung; Seunghee Park. Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System. Remote Sensing 2020, 12, 4169 .
AMA StyleDai Tran, Minsoo Park, Daekyo Jung, Seunghee Park. Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System. Remote Sensing. 2020; 12 (24):4169.
Chicago/Turabian StyleDai Tran; Minsoo Park; Daekyo Jung; Seunghee Park. 2020. "Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System." Remote Sensing 12, no. 24: 4169.
As civil engineering structures become larger, non-contact inspection technology is required to measure the overall shape and size of structures and evaluate safety. Structures are easily exposed to the external environment and may not be able to perform their original functions depending on the continuous load for a long time. Therefore, in this study, we propose a method for estimating the vertical displacement of structures using light detection and ranging, which enables non-contact measurement. The point cloud acquired through laser scanning was rearranged into a three-dimensional space, and internal nodes were created by continuously dividing the space. The generated node has its own location information, and the vertical displacement value was calculated by searching for the node where the deformation occurred. The performance of the proposed displacement estimation technique was verified through static loading experiments, and the octree space partitioning method is expected to be applied and utilized in structural health monitoring.
Gichun Cha; Sung-Han Sim; Seunghee Park; Taekeun Oh. LiDAR-Based Bridge Displacement Estimation Using 3D Spatial Optimization. Sensors 2020, 20, 7117 .
AMA StyleGichun Cha, Sung-Han Sim, Seunghee Park, Taekeun Oh. LiDAR-Based Bridge Displacement Estimation Using 3D Spatial Optimization. Sensors. 2020; 20 (24):7117.
Chicago/Turabian StyleGichun Cha; Sung-Han Sim; Seunghee Park; Taekeun Oh. 2020. "LiDAR-Based Bridge Displacement Estimation Using 3D Spatial Optimization." Sensors 20, no. 24: 7117.
Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most models focus only on heat-associated deaths. In this study, a random forest model was developed for the weekly prediction of heat-related damages on the basis of four years (2015–2018) of statistical, meteorological, and floating population data from South Korea. The model was evaluated through comparisons with other traditional regression models in terms of mean absolute error, root mean squared error, root mean squared logarithmic error, and coefficient of determination (R2). In a comparative analysis with observed values, the proposed model showed an R2 value of 0.804. The results show that the proposed model outperforms existing models. They also show that the floating population variable collected from mobile global positioning systems contributes more to predictions than the aggregate population variable.
Minsoo Park; Daekyo Jung; Seungsoo Lee; Seunghee Park. Heatwave Damage Prediction Using Random Forest Model in Korea. Applied Sciences 2020, 10, 8237 .
AMA StyleMinsoo Park, Daekyo Jung, Seungsoo Lee, Seunghee Park. Heatwave Damage Prediction Using Random Forest Model in Korea. Applied Sciences. 2020; 10 (22):8237.
Chicago/Turabian StyleMinsoo Park; Daekyo Jung; Seungsoo Lee; Seunghee Park. 2020. "Heatwave Damage Prediction Using Random Forest Model in Korea." Applied Sciences 10, no. 22: 8237.
To minimize the damage caused by wildfires, a deep learning-based wildfire-detection technology that extracts features and patterns from surveillance camera images was developed. However, many studies related to wildfire-image classification based on deep learning have highlighted the problem of data imbalance between wildfire-image data and forest-image data. This data imbalance causes model performance degradation. In this study, wildfire images were generated using a cycle-consistent generative adversarial network (CycleGAN) to eliminate data imbalances. In addition, a densely-connected-convolutional-networks-based (DenseNet-based) framework was proposed and its performance was compared with pre-trained models. While training with a train set containing an image generated by a GAN in the proposed DenseNet-based model, the best performance result value was realized among the models with an accuracy of 98.27% and an F1 score of 98.16, obtained using the test dataset. Finally, this trained model was applied to high-quality drone images of wildfires. The experimental results showed that the proposed framework demonstrated high wildfire-detection accuracy.
Minsoo Park; Dai Tran; Daekyo Jung; Seunghee Park. Wildfire-Detection Method Using DenseNet and CycleGAN Data Augmentation-Based Remote Camera Imagery. Remote Sensing 2020, 12, 3715 .
AMA StyleMinsoo Park, Dai Tran, Daekyo Jung, Seunghee Park. Wildfire-Detection Method Using DenseNet and CycleGAN Data Augmentation-Based Remote Camera Imagery. Remote Sensing. 2020; 12 (22):3715.
Chicago/Turabian StyleMinsoo Park; Dai Tran; Daekyo Jung; Seunghee Park. 2020. "Wildfire-Detection Method Using DenseNet and CycleGAN Data Augmentation-Based Remote Camera Imagery." Remote Sensing 12, no. 22: 3715.
In the construction industry, it is difficult to predict occupational accidents because various accident characteristics arise simultaneously and organically in different types of work. Furthermore, even when analyzing occupational accident data, it is difficult to deduce meaningful results because the data recorded by the incident investigator are qualitative and include a wide variety of data types and categories. Recently, numerous studies have used machine learning to analyze the correlations in such complex construction accident data; however, heretofore the focus has been on predicting severity with various variables, and several limitations remain when deriving the correlations between features from various variables. Thus, this paper proposes a data processing procedure that can efficiently manipulate accident data using optimal machine learning techniques and derive and systematize meaningful variables to rationally approach such complex problems. In particular, among the various variables, the most influential variables are derived through methods such as clustering, chi-square, Cramer’s V, and predictor importance; then, the analysis is simplified by optimally grouping the variables. For accident data with optimal variables and elements, a predictive model is constructed between variables, using a support vector machine and decision-tree-based ensemble; then, the correlation between the dependent and independent variables is analyzed through an alluvial flow diagram for several cases. Therefore, a new processing procedure has been introduced in data preprocessing and accident prediction modelling to overcome difficulties from complex and diverse construction occupational accident data, and effective accident prevention is possible by deriving correlations of construction accidents using this process.
Jae Yun Lee; Young Geun Yoon; Tae Keun Oh; Seunghee Park; Sang Il Ryu. A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry. Applied Sciences 2020, 10, 7949 .
AMA StyleJae Yun Lee, Young Geun Yoon, Tae Keun Oh, Seunghee Park, Sang Il Ryu. A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry. Applied Sciences. 2020; 10 (21):7949.
Chicago/Turabian StyleJae Yun Lee; Young Geun Yoon; Tae Keun Oh; Seunghee Park; Sang Il Ryu. 2020. "A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry." Applied Sciences 10, no. 21: 7949.
Measuring the concrete slump is necessary for ensuring the quality of concrete before its use. However, manual slump measurements have limited accuracy and are time-consuming and labor-intensive. Herein, an approach based on deep learning and stereo vision techniques is proposed to effectively improve the determination of concrete slump in outdoor environments. Input images obtained from a stereo camera system were classified into three slump cases via deep learning. The actual slumps were then calculated using depth maps obtained via stereo vision. The results were analyzed for the correlations between camera mounting heights and the distance between the cameras to identify the optimal settings for an onsite working system. A baseline of 70 mm and working height of 1.7–1.9 m yielded optimal results with errors ≤2.05%. Additionally, the mask-region-based volumetric results exhibited errors of <8.9% relative to ground truth, thereby validating the reliability of the proposed approach.
Nguyen Manh Tuan; Quach Van Hau; Sangyoon Chin; Seunghee Park. In-situ concrete slump test incorporating deep learning and stereo vision. Automation in Construction 2020, 121, 103432 .
AMA StyleNguyen Manh Tuan, Quach Van Hau, Sangyoon Chin, Seunghee Park. In-situ concrete slump test incorporating deep learning and stereo vision. Automation in Construction. 2020; 121 ():103432.
Chicago/Turabian StyleNguyen Manh Tuan; Quach Van Hau; Sangyoon Chin; Seunghee Park. 2020. "In-situ concrete slump test incorporating deep learning and stereo vision." Automation in Construction 121, no. : 103432.
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.
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 StyleDai 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 StyleDai 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.
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.
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 StyleKassahun 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 StyleKassahun 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.
The Light Detection And Ranging (LiDAR) system has become a prominent tool in structural health monitoring. Among such systems, Terrestrial Laser Scanning (TLS) is a potential technology for the acquisition of three-dimensional (3D) information to assess structural health conditions. This paper enhances the application of TLS to damage detection and shape change analysis for structural element specimens. Specifically, estimating the deflection of a structural element with the aid of a Lidar system is introduced in this study. The proposed approach was validated by an indoor experiment by inducing artificial deflection on a simply supported beam. A robust genetic algorithm method is utilized to enhance the accuracy level of measuring deflection using lidar data. The proposed research primarily covers robust optimization of a genetic algorithm control parameter using the Taguchi experiment design. Once the acquired data is defined in terms of plane, which has minimum error, using a genetic algorithm and the deflection of the specimen can be extracted from the shape change analysis.
Michael Bekele Maru; Donghwan Lee; Gichun Cha; Seunghee Park. Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data. Sensors 2020, 20, 2144 .
AMA StyleMichael Bekele Maru, Donghwan Lee, Gichun Cha, Seunghee Park. Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data. Sensors. 2020; 20 (7):2144.
Chicago/Turabian StyleMichael Bekele Maru; Donghwan Lee; Gichun Cha; Seunghee Park. 2020. "Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data." Sensors 20, no. 7: 2144.
Corrosion detection using a pulsed laser scanning system can be performed via ultrasonic wave propagation imaging. This method outputs illustrations of the wave field within the host structure; thus, it can depict wave–corrosion area interactions. Additionally, post-processing can be performed to enhance the visualization of corroded areas. The wavefield energy computed using RMS (Root Mean Square) is a validated post-processing tool capable of displaying the location and area of corrosion-damaged regions. Nonetheless, to characterize corrosion, it is necessary to determine its depth. The measurement of depth in conjunction with that of the corroded area via the RMS distribution enables the determination of all dimensions of corrosion damage. Thereafter, the flaw severity can be evaluated. This study employed a wavefield within a plate on which corrosion was developed artificially to generate frequency–wavenumber dispersion curves. The curves were compared with their counterparts from a corrosion-free plate. Alternatively, they could be compared with dispersion curves drawn using the depth and material properties of a pristine plate via a computer program. Frequency–wavenumber pairs were extracted from the dispersion curves produced using the portion of the wavefield within the corroded area. These were inserted into the Rayleigh–Lamb equation, from which depths were calculated and averaged.
Kassahun Demissie Tola; Dai Quoc Tran; Byoungjoon Yu; Seunghee Park. Determination of Plate Corrosion Dimension Using Nd:YAG Pulsed Laser-generated Wavefield and Experimental Dispersion Curves. Materials 2020, 13, 1436 .
AMA StyleKassahun Demissie Tola, Dai Quoc Tran, Byoungjoon Yu, Seunghee Park. Determination of Plate Corrosion Dimension Using Nd:YAG Pulsed Laser-generated Wavefield and Experimental Dispersion Curves. Materials. 2020; 13 (6):1436.
Chicago/Turabian StyleKassahun Demissie Tola; Dai Quoc Tran; Byoungjoon Yu; Seunghee Park. 2020. "Determination of Plate Corrosion Dimension Using Nd:YAG Pulsed Laser-generated Wavefield and Experimental Dispersion Curves." Materials 13, no. 6: 1436.
In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused only on storms, floods, and earthquakes, and they have not used past data. This research proposes a new conceptual framework of an IDSS for disaster management, with particular attention paid to wildfires and cold/heat waves. The IDSS uses big data collected from open application programming interface (API) and artificial intelligence (AI) algorithms to help decision-makers make faster and more accurate decisions. In addition, a simple example of the use of a convolutional neural network (CNN) to detect fire in surveillance video has been developed, which can be used for automatic fire detection and provide an appropriate response. The system will also consider connecting to open source intelligence (OSINT) to identify vulnerabilities, mitigate risks, and develop more robust security policies than those currently in place to prevent cyber-attacks.
Daekyo Jung; Vu Tran Tuan; Dai Quoc Tran; Minsoo Park; Seunghee Park. Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management. Applied Sciences 2020, 10, 666 .
AMA StyleDaekyo Jung, Vu Tran Tuan, Dai Quoc Tran, Minsoo Park, Seunghee Park. Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management. Applied Sciences. 2020; 10 (2):666.
Chicago/Turabian StyleDaekyo Jung; Vu Tran Tuan; Dai Quoc Tran; Minsoo Park; Seunghee Park. 2020. "Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management." Applied Sciences 10, no. 2: 666.
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.
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 StyleJu-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 StyleJu-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.
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.
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 StyleJu-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 StyleJu-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.
Recently, the early-age strength prediction for RC (reinforced concrete) structures has been an important topic in the construction industry, relating to project-time reduction and structural safety. To address this, numerous destructive and NDTs (non-destructive tests) are applied to monitor the early-age strength development of concrete. This study elaborates on the NDT techniques of ultrasonic wave propagation and concrete maturity for the estimation of compressive strength development. The results of these comparative estimation approaches comprise the concrete maturity method, penetration resistance test, and an ultrasonic wave analysis. There is variation of the phase transition in the concrete paste with the changing of boundary limitations of the material in accordance with curing time, so with the formation of phase-transition changes, changes in the velocities of ultrasonic waves occur. As the process of hydration takes place, the maturity method produces a maturity index using the time-feature reflection on the strength-development process of the concrete. Embedded smart temperature sensors (SmartRock) and PZT (piezoelectric) sensors were used for the data acquisition of hydration temperature history and wave propagation. This study suggests a novel relationship between wave propagation, penetration tests, and hydration temperature, and creates a method that relies on the responses of resonant frequency changes with the change of boundary conditions caused by the strength-gain of the concrete specimen. Calculating the changes of these features provides a pattern for estimating concrete strength. The results for the specimens were validated by comparing the strength results with the penetration resistance test by a universal testing machine (UTM). An algorithm used to relate the concrete maturity and ultrasonic wave propagation to the concrete compressive strength. This study leads to a method of acquiring data for forecasting in-situ early-age strength of concrete, used for secure construction of concrete structures, that is fast, cost effective, and comprehensive for SHM (structural health monitoring).
Najeebullah Tareen; Kim; Seunghee Park; Park; Junkyeong Kim; Won-Kyu Kim. Comparative Analysis and Strength Estimation of Fresh Concrete Based on Ultrasonic Wave Propagation and Maturity Using Smart Temperature and PZT Sensors. Micromachines 2019, 10, 559 .
AMA StyleNajeebullah Tareen, Kim, Seunghee Park, Park, Junkyeong Kim, Won-Kyu Kim. Comparative Analysis and Strength Estimation of Fresh Concrete Based on Ultrasonic Wave Propagation and Maturity Using Smart Temperature and PZT Sensors. Micromachines. 2019; 10 (9):559.
Chicago/Turabian StyleNajeebullah Tareen; Kim; Seunghee Park; Park; Junkyeong Kim; Won-Kyu Kim. 2019. "Comparative Analysis and Strength Estimation of Fresh Concrete Based on Ultrasonic Wave Propagation and Maturity Using Smart Temperature and PZT Sensors." Micromachines 10, no. 9: 559.
This study investigates the applicability of an embedded EM sensor using a series of experimental studies. To verify the embedded EM sensor, the magnetic hysteresis of various types of PS tendons is measured. After that, the embedded EM sensor is embedded into the concrete and the possibility of obtaining measurements is verified. Finally, the downscaled PSC girder specimen having a sheath with a different curvature is fabricated and the influence of the sheath curvature is investigated. The magnetic hysteresis was changed constantly even though the type of PS tendon was changed, and the embedded EM sensor can measure the magnetic hysteresis, even in the concrete and curved sheath. The area of magnetic hysteresis was decreased according to the increase in the tension force, but the actual values were different according to the number and cross-sectional area of tendons and the initial state of sensors. To compensate for the measured data, the tensile force was converted to the tensile stress and the area ratio was used to compensate for the initial value of the EM sensor. According to the test results, the embedded EM sensor could be applied to the actual PS girder and it can measure the actual tension, which includes the friction loss.
Junkyeong Kim; Ju-Won Kim; Seunghee Park. Investigation of Applicability of an Embedded EM Sensor to Measure the Tension of a PSC Girder. Journal of Sensors 2019, 2019, 1 -12.
AMA StyleJunkyeong Kim, Ju-Won Kim, Seunghee Park. Investigation of Applicability of an Embedded EM Sensor to Measure the Tension of a PSC Girder. Journal of Sensors. 2019; 2019 ():1-12.
Chicago/Turabian StyleJunkyeong Kim; Ju-Won Kim; Seunghee Park. 2019. "Investigation of Applicability of an Embedded EM Sensor to Measure the Tension of a PSC Girder." Journal of Sensors 2019, no. : 1-12.
Minsoo Park; Donghwan Lee; Gichun Cha; Junghyun Im; Seunghee Park; Sungkyunkwan University. Development of Building Diagnosis 3D Visualization Solution for Disaster. Journal of Korean Society of Hazard Mitigation 2018, 18, 127 -132.
AMA StyleMinsoo Park, Donghwan Lee, Gichun Cha, Junghyun Im, Seunghee Park, Sungkyunkwan University. Development of Building Diagnosis 3D Visualization Solution for Disaster. Journal of Korean Society of Hazard Mitigation. 2018; 18 (2):127-132.
Chicago/Turabian StyleMinsoo Park; Donghwan Lee; Gichun Cha; Junghyun Im; Seunghee Park; Sungkyunkwan University. 2018. "Development of Building Diagnosis 3D Visualization Solution for Disaster." Journal of Korean Society of Hazard Mitigation 18, no. 2: 127-132.
In this study, the hardening process of ultra high performance concrete (UHPC) was monitored non-destructively using a single embedded sensor system and the characteristics of guided waves, especially the Lamb wave. Lamb wave propagation depends on the material properties of the medium and boundary conditions. Since the boundary conditions of the embedded sensor system continuously change during the hardening process of concrete materials, the measured characteristics of the propagating waves also vary. To understand the variations in wave propagation, the Lamb modes were decomposed using the polarization characteristics of piezoelectric sensors, which were used to measure wave responses. Additionally, a traditional penetration resistance method was adopted to estimate the time for phase transition of UHPC. The decomposed Lamb modes were compared to measurements of penetration resistance. The strength development of UHPC, with and without short-fiber reinforcement, was estimated using the variation of patterns of the decomposed Lamb modes after the phase transition. Based on the proposed methodology, which measures the propagation and variation of the Lamb waves, it is possible to estimate the time of phase transition and the strength development of UHPC.
Changgil Lee; Seunghee Park; John E. Bolander; Sukhoon Pyo. Monitoring the hardening process of ultra high performance concrete using decomposed modes of guided waves. Construction and Building Materials 2018, 163, 267 -276.
AMA StyleChanggil Lee, Seunghee Park, John E. Bolander, Sukhoon Pyo. Monitoring the hardening process of ultra high performance concrete using decomposed modes of guided waves. Construction and Building Materials. 2018; 163 ():267-276.
Chicago/Turabian StyleChanggil Lee; Seunghee Park; John E. Bolander; Sukhoon Pyo. 2018. "Monitoring the hardening process of ultra high performance concrete using decomposed modes of guided waves." Construction and Building Materials 163, no. : 267-276.