• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.1
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• pp.94-101
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• 2019

Most of the existing rail structures have undergone a lot of aging since a considerable period of time has passed from completion. In particular, among existing railway bridges, many of the plate girder bridges are older bridges that have lived 40 to 60 years or more. Since the treadmill is directly connected to the girder without the ballast, the running load of the vehicle is directly transmitted to the bridge. Therefore, the shock and noise applied to the bridge are larger than those of the ballast bridge, and the dynamic shock and vibration are also relatively large. Therefore, it is very urgent to develop appropriate maintenance, repair and reinforcement technology for existing steel plate bridge. In this study, the authors introduced the characteristics of embedded rail (ERS) developed for improving the performance of the existing plate girder bridge and the techniques solving the vibration and noise problems. In order to evaluate the vibration and noise reduction performance of ERS, a non-ballast plate girder bridge with 5m length of sleepers installed and a plate girder bridge with ERS were fabricated. And, then, the vibration response generated under the same excitation condition was measured and analyzed. Also, the radiated noise analysis was performed using the vibration response data obtained from the experiment as the input data of the acoustic analysis model. As a result of experiments and analyses, it was confirmed that the plate girder bridge's vibration using ERS was reduced by 15.0~18.8dB and the average noise was reduced by 7.7dB(A) more than the non-ballast bridge.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.42-48
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• 2019

The significance of PNT information in the fourth industrial revolution is viewed differently in relation to the past. Autonomous vehicles, autonomous vessels, smart grids, and national infrastructure require sustainable and reliable services in addition to their high precision service. Satellite navigation system, which is the most representative system for providing PNT information, receive signals from satellites outside the earth so signal reception power is low and signal structures for civilian use are open to the public. Therefore, it is vulnerable to intentional and unintentional interference or hacking. Satellite navigation systems, which can easily acquire high performance of PNT information at low cost, require alternatives due to its vulnerability to the hacking. This paper proposed R-Mode (Ranging Mode) technology that utilizes currently operated navigation and communication infrastructure in terms of Signals of OPportunity (SoOP). For this, the Nationwide Differential Global Navigation Satellite System (NDGNSS), which currently gives a service of Medium Frequency (MF) navigation signal broadcasting, was used to validate the feasibility of a backup infrastructure in domestic maritime areas through simulation analysis.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.411-429
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• 2022

Instrumentations are essential in NATM tunnels, however measuring instruments are installed and applied without performance verification procedures due to insufficient research on methods, procedures, regulations, etc. to verify the reliability of the measuring instruments. In this study, domestic and foreign regulations relating to the verification and calibration of instruments were investigated and necessities for accreditation standards were proposed. In order to identify the causes of the defects, an external inspection was performed on rock bolt stressmeter and rod extensometer, which are measuring instruments with relatively complex structures. For verifying the performance of these instruments, verification devices were developed that can load step-by-step and the causes of defects were identified in measuring instruments of nine domestic manufacturers. Through the performance test, a number of measuring instruments were found to be defective. It was important to test the performance of the instruments in the state of a finished product and accordingly performance inspection methods and procedures were proposed. The results of this study are expected to help preparing related regulations for verifying instrument performance and selecting instruments in the field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.537-542
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• 2021

This paper improves the possibility of recognizing fast-moving objects through the YOLO (You Only Look Once) deep learning recognition model in an application environment for object recognition in images. The purpose was to study the method of collecting semantic data through processing. In the recognition model, the moving object recognition error was identified as unrecognized because of the difference between the frame rate of the camera and the moving speed of the object and a misrecognition due to the existence of a similar object in an environment adjacent to the object. To minimize the recognition errors by compensating for errors, such as unrecognized and misrecognized objects through the proposed data collection method, and applying vision processing technology for the causes of errors that may occur in images acquired for sports (tennis games) that can represent real similar environments. The effectiveness of effective secondary data collection was improved by research on methods and processing structures. Therefore, by applying the data collection method proposed in this study, ordinary people can collect and manage data to improve their health and athletic performance in the sports and health industry through the simple shooting of a smart-phone camera.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.82-92
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• 2022

This study aims to investigate the effect of total electrochemical cell resistance (TECR) on electrochemical impedance (EI) measurements of reinforced concrete (RC) by electrochemical impedance spectroscopy (EIS) using a three-electrode system. A series of experimental study is performed to measure electrochemical behavior of a steel bar embedded in a concrete cube specimen, with a side length of 200 mm, in various experimental conditions. Main variables include concrete dry conditions, coupling resistance between sensing electrodes and concrete surface, and area of the counter electrode. It is demonstrated that EI values remains stable when the compliant voltage of a measuring device is sufficiently great compared to the potential drop caused by TECR of concrete specimens. It is confirmed that the effect of the coupling resistance of TECR is far more influential than other two factors (concrete dry conditions and area of the counter electrode). The results in this study can be used as a fundamental basis for development of a surface-mount sensor for corrosion monitoring of reinforced concrete structures exposed to wet-and-dry cycles under marine environment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.67-76
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• 2023

In this study, in order to enhance the joint capacity between the existing reinforced concrete (R/C) frame and the reinforcement member, we proposed a novel concept of Internal Composite Seismic Strengthening Method (CSSM) for seismic retrofit of existing domestic medium-to-low-rise R/C buildings. The Internal CSSM rehabilitation system is a type of strength-enhancing reinforcement systems, to easily increase the ultimate horizontal shear capacity of R/C structures without seismic details in Korea, which show shear collapse mechanism. Two test specimens of full-size two-story R/C frame were fabricated based on an existing domestic R/C building without seismic details, and then retrofitted by using the proposed CSSM seismic system; therefore, one control test specimen and one test specimen reinforced with the CSSM system were used. Pseudo-dynamic testing was carried out to evaluate seismic strengthening effects, and the seismic response characteristics of the proposed system, in terms of the maximum shear force, response story drift, and seismic damage degree compared with the control specimen (R/C bare frame). Experiment results indicated that the proposed CSSM reinforcement system, internally installed to the existing R/C frame, effectively enhanced the horizontal shear force, resulting in reduced story drift of R/C buildings even under a massive earthquake.

• Smart Structures and Systems
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• v.30 no.5
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• pp.521-535
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• 2022

Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-mean-square errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.3
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• pp.184-191
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• 2023

With increasing corrosion in RC (Reinforced Concrete) structures, cracks occurred due to corrosion products and bearing load resistance decreased. In this study, corrosion was induced through an accelerated corrosion test (ICM: Impressed Current Method) with 140 hours of duration, and changes in USV (Ultra-Sonic Velocity), flexural failure load, and corrosion weight were evaluated before and after corrosion test. Three levels of cover depth (20 mm, 30 mm, and 40 mm) were considered, and the initial cracking period increased and the rust around steel decreased with increasing cover depth. In addition, the USV linearly decreased with decreasing cover depth and increasing amount of corrosion. In the flexural loading test, the bending capacity decreased by more than 10% due to corrosion, but a clear correlation could not be obtained since the corrosion ratio was small, so that the effect of slip was greater than that of reduced cross-sectional area of steel due to corrosion. As cover depth increased, the produced corrosion amount and USV changed with a clear linear relationship, and the cracking period due to corrosion could be estimated by the gradient of the measured corrosion current.