• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.167-175
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• 2014

If volumetric flaws such as bearing pad fretting flaws and debris fretting flaws are detected in the pressure tubes of pressurized heavy water reactors during in-service inspection, the initiation of fatigue cracks and delayed hydrogen cracking from the detected volumetric flaws shall be assessed by using elastic stress concentration factors in accordance with CSA N285.8-05. The CSA N285.8-05 presents only an approximate formula based on linear elastic fracture mechanics for the debris fretting flaw. In this study, an engineering formula considering the geometric characteristics of the debris fretting flaw in detail was derived using two-dimensional finite element analysis and Kinectrics, Inc.'s engineering procedure with slight modifications. Comparing the application results obtained using the derived formula with the three-dimensional finite element analysis results, it is found that the results obtained using the derived formula agree well with the results of the finite element analysis.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.1-5
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• 2010

Acoustic emission(AE) has emerged as a powerful nondestructive tool to detect or monitor preexisting defects and leaks in the vessel structures. A Bragg grating based acoustic emission sensor system is developed. Various type of fiber Bragg grating sensor including the variable length of sensing part was fabricated and prototype sensor system was tested by using PZT pulser and pencil lead break sources. Two types of sensor attachment were used. First, the fiber Bragg grating sensor was attached fully to the surface using bonding agent. Second one is that one part of fiber was attached to the surface partly by bonding and the other part of fiber will be act as a cantilever. That is, the resonant frequency of the fiber Bragg grating sensor will depend on the length of sensing part. The final goal of the sensor system is to provide on-line monitoring of cracks or leaks in reactor vessel head penetration of nuclear power plants.

• Journal of the Korean Geotechnical Society
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• v.26 no.4
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• pp.59-68
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• 2010

Piping, a common form of internal embankment erosion, is caused by progressive movement of soil particles through an embankment. The phenomenon commonly occurs with precursory signs of development of fractures in dam structures, but also occurs without any noticeable signs in dams that showed satisfactory dam performance for several years, due to dissolution of soluble material in an embankment. While piping accounts for nearly 50% of the causes for dam failure, few studies have been made for systematic evaluation of the phenomenon. In this study, we attempted to monitor the changes in electrical resistivities of fill-dam material while a saddle dam is dismantled for the construction of emergency spillways of Daechung dam. Two artificial subhorizontal boreholes were drilled into the embankment structure to simulate piping along the two artificial flow channels. Monitoring of changes in electrical resistivity showed an increase in resistivity values during piping. Thus, the investigation of resistivity over time could be an effective method for piping prediction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.561-568
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• 2023

Various defects occur in concrete structures due to internal and external environments. If there is a defect, it is important to efficiently identify and maintain it because there is a problem with the structural safety of concrete. However, recent deep learning research has focused on cracks in concrete, and studies on exfoliation and contamination are lacking. In this study, focusing on exfoliation and contamination, which are difficult to label, four models were developed and their performance evaluated through unlabelling method, filtering method, the convergence of transfer learning based k-means clustering. As a result of the analysis, the convergence model classified the defects in the most detail and could increase the efficiency compared to direct labeling. It is hoped that the results of this study will contribute to the development of deep learning models for various types of defects that are difficult to label in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.1-11
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• 2024

Bridges crack and become damaged due to age and external factors such as earthquakes, lack of maintenance, and weather conditions. With the number of aging bridge on the rise, lack of maintenance can lead to a decrease in safety, resulting in structural defects and collapse. To prevent these problems and reduce maintenance costs, a system that can monitor the condition of bridge and respond quickly is needed. To this end, existing research has proposed artificial intelligence model that use sensor data to identify the location and extent of cracks. However, existing research does not use data from actual bridge to determine the performance of the model, but rather creates the shape of the bridge through simulation to acquire data and use it for training, which does not reflect the actual bridge environment. In this paper, we propose a bridge safety determination edge AI model that detects bridge abnormalities based on artificial intelligence by utilizing acceleration data from bridge occurring in the field. To this end, we newly defined filtering rules for extracting valid data from acceleration data and constructed a model to apply them. We also evaluated the performance of the proposed bridge safety determination edge AI model based on data collected in the field. The results showed that the F1-Score was up to 0.9565, confirming that it is possible to determine safety using data from real bridge, and that rules that generate similar data patterns to real impact data perform better.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.225-235
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• 2020

Ultrasonic investigation of damage detection has been widely used for non-destructive testing of various concrete structures. This study focuses on damage detection analysis with the aid of wave propagation in two-phase composite concrete with aggregate (inclusion) and mortar (matrix). To fabricate a realistic simulation model containing a variety of irregular aggregate shapes, the mesh generation technique using an image processing technique was proposed. Initially, the domains and boundaries of the aggregates were extracted from the digital image of a typical concrete cut-section. This enables two different domains: aggregates and mortar in heterogeneous concrete sections, and applied the grids onto these domains to discretize the model. Subsequently, finite element meshes are generated in terms of spatial and temporal requirements of the model size. For improved analysis results, all meshes are designed to be quadrilateral type, and an additional process is conducted to improve the mesh quality. With this simulation model, wave propagation analyses were conducted with a central frequency of 75 kHz of the Mexican hat incident wave. Several void damages, such as needle-shaped cracks and void-shaped holes, were artificially introduced in the model. Finally, various formats of internal damage were detected by implementing energy mapping based signal processing.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.6
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• pp.612-617
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• 2001

A nondestructive evaluation technique for detecting internal defects of an hot forged Al-Si alloy part is established in this study. Ultrasonic characteristics of various internal planar defects are investigated by experiments for establishing a reliable test procedure. The effect of the angle between ultrasonic energy propagation directions and planar defects on the ultrasonic signal configuration is evaluated in the pulse-echo technique. A characteristic of ultrasonic signal for the internal planar defect located near the edge is also evaluated. The applicability of the through-transmission technique is also discussed. Reliability of the presented approach is validated by the destructive testing for more than 500 specimens.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.19-27
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• 2022

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

• Korean Journal of Construction Engineering and Management
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• v.1 no.1 s.1
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• pp.87-94
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• 2000

Crack sealing, a routine and necessary part of pavement maintenance, is a dangerous, costly, and labor-intensive operation. Within the North America, about ${\$}200$ million is spent annually on crack sealing, with the Texas Department of Transportation (TxDOT) spending about ${\$}7$ million annually (labor alone accounts for over 50 percent of these costs). Prompted by concerns of safety and cost, the University of Texas at Austin, in cooperation with TxDOT and the Federal Highway Administration (FHWA) has developed a unique computer-guided Automated Road Maintenance Machine (ARMM) for pavement crack sealing. In 1999, successful field tests have been undertaken in 8 States around the U.S. This paper first describes significance of the automated crack sealing and technical advances in automated crack sealers including the ARMM, developed in the U.S. It then discusses the ARMM's field implementation and performance evaluation results, and improvements and modifications suggested through the technology evaluation during the field trials. Current research efforts and future work plans in its further development are also presented in this paper.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.56-64
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• 2021

Leakage due to deterioration and damage is one of the major causes of volume change by freezing and thawing, and it leads micro-cracking and surface scaling in concrete structures. The deterioration of damaged concrete accelerates with the chloride attack. Thus, in the detailed guidelines for facility performance evaluation (2020), the quality of cover concrete and the freeze-thaw (FT) repetition cycle were newly suggested for concrete durability assessment. The quality of cover concrete should be evaluated by the rebound hammer test and the FT repetition cycle should be also considered in the deterioration environmental assessment. This study suggested the application of fast dynamic based nonlinear ultrasound method to monitor initial micro-scale damage under freezing and thawing environment. Concrete specimens were fabricated with different water-cement ratios (40%, 60%) and air contents (1.5% and 3.0%). The compressive strength, rebound number, relative dynamic modulus, and nonlinear ultrasound were measured with different FT cycles. The scanning electron microscopy was also performed to investigate the micro-scale FT damage. As a result, both the rebound number and the relative dynamic modulus had difficulty to detect early damage but the proposed method showed a potential to detect initial micro-scale damage and predict the FT resistance performance of concrete.