• Korean Journal of Materials Research
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• v.26 no.11
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• pp.628-634
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• 2016

The cold rolling workability and mechanical properties of two new alloys, designed and cast Al-5.5Mg-2.9Si and Al-7Mg-0.9Zn alloys, were investigated in detail. The two alloy sheets of 4 mm thickness, 30 mm width and 100 mm length were reduced to a thickness of 1 mm by multi-pass rolling at ambient temperature. The rolling workability was better for the Al-7Mg-0.9Zn alloy than for the Al-5.5Mg-2.9Si alloy; in case of the former alloy, edge cracks began to occur at 50% rolling reduction, and their number and length increased with rolling reduction; however, in the latter alloy, the sheets did not have any cracks even at higher rolling reduction. The mechanical properties of tensile strength and elongation were also better in the Al-7Mg-0.9Zn alloy than in Al-5.5Mg-2.9Si alloy. Work hardening ability after cold rolling was also higher in the Al-7Mg-0.9Zn alloy than in the Al-5.5Mg-2.9Si alloy. At the same time, the texture development was very similar for both alloys; typical rolling texture developed in both alloys. These differences in the two alloys can primarily be explained by the existence of precipitates of $Mg_2Si$. It is concluded that the Al-7Mg-0.9Zn alloy is better than the Al-5.5Mg-2.9Si alloy in terms of mechanical properties.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.563-571
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• 2021

Recently, due to the increase in high-rise apartment and residential-commercial complex buildings, a number of mega-class mass concrete members with a thickness of 3m or more have been designed. As the construction of mass concrete such as transfer beam and slab is increasing not only in foundation members but also in special structures, research on reducing temperature cracks in mass concrete is being conducted. To review temperature cracks in mass concrete, it is important to review the thermal properties of concrete, but it is difficult to use an adiabatic temperature rise tester in the field, so the semi-adiabatic temperature rise test is mainly used. In this study, to improve the accuracy of the results of concrete heat characteristics gained by the semi-adiabatic temperature rise test, various factors affecting heat loss compensation and methods were reviewed and presented.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.194-200
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• 2013

In this paper, we propose a multi-scale crack detection method using scaling. It is based on morphology algorithm, crack features, and scaling. We use a morphology operator which extracts patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. However, morphology methods using only one structure element could detect only fixed width crack. Thus, we use a scaling method. We use bilinear interpolation for scaling. Our method calculates values of properties such as the number of pixels and the maximum length of the segmented region. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed multi-scale crack detection method has better results than those of existing detection methods.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.95-103
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• 2023

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.9
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• pp.1227-1233
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• 2010

In the hot section of a gas turbine, the turbine blades were protected from high temperature by providing a thermal barrier coating (TBC) as well as by cooling air flowing through internal passages within the blades. The cooling air then passed through discrete holes on the blade surface, creating a film of cooling air that further protects the surface from the hot mainstream flow. The holes are subjected to stresses resulting from the lateral growth of thermally grown oxide, the thermal expansion misfit between the constituent layers, and the centrifugal force due to high-speed revolution; these stresses often result in cracking. In this study, the deformation and cracks occurring near a hole on a heat-resistant alloy subjected to thermo-mechanical cycling were investigated. The experiment showed that cracks formed around the hole depending on the applied stress level and the number of cycles. These results could be explained by our analytic solution.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.161-168
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• 2021

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.73-78
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• 2003

Ultrasonic measurement is one of important technologies in the lift-time maintenance of nuclear poler plant. Ultrasonic inspection system is consisted of the operator, equipment and procedure. The reliability of ultrasonic inspection system is affected by its ability. The performance demonstration round robin was conducted to quantify the capability of ultrasonic inspection for in-service. The small number of teams who employed procedures that met or exceeded ASME Sec. XI Code requirements detected the piping of nuclear power plant with various cracks to evaluate the capability of detection and sizing. In this paper, the statistical reliability assessment of ultrasonic nondestructive inspection data using Monte Carlo simulation is presented. The results of the probability of detection (POD) analysis using Monte Carlo simulation are compared to these of logistic probability model. In these results, Monte Carlo simulation was found to be very useful to the reliability assessment f3r the small hit/miss data sets.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.3
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• pp.31-39
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• 2021

In this study, the fiber blending ratio and strain rate effect on the tensile properties synergy effect of hybrid fiber reinforced cement composite was evaluated. Hooked steel fiber(HSF) and smooth steel fiber(SSF) were used for reinforcing fiber. The fiber blending ratio of HSF+SSF were 1.5+0.5, 1.0+1.0 and 0.5+1.5vol.%. As a results, in the cement composite(HSF2.0) reinforced with HSF, as the strain rate increases, the tensile stress sharply decreased after the peak stress because of the decrease in the number of straightened pull-out fibers by increase of micro cracks in the matrix around HSF. When 0.5 vol.% of SSF was mixed, the micro cracks was effectively controlled at the static rate, but it was not effective in controlling micro cracks and improving the pull-out resistance of HSF at the high rate. On the other hand, the specimen(HSF1.0SSF1.0) in which 1.0vol.% HSF and 1.0vol.% SSF were mixed, each fibers controls against micro and macro cracks, and SSF improves the pull-out resistance of HSF effectively. Thus, the fiber blending effect of the strain capacity and energy absorption capacity was significantly increased at the high rate, and it showed the highest dynamic increase factor of the tensile strength, strain capacity and peak toughness. On the other hand, the incorporation of 1.5 vol.% SSF increases the number of fibers in the matrix and improves the pull-out resistance of HSF, resulting in the highest fiber blending effect of tensile strength and softening toughness. But as a low volume fraction of HSF which controlling macro crack, it was not effective for synergy of strain capacity and peak toughness.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.2
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• pp.134-140
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• 2014

The paper presents an overview of the present state of study on the fatigue behaviors at very high number of cycles ($N_f$ > $10^7$). A classification of materials with typical S-N curves and influencing factors such as notches, residual stresses, temperatures, corrosion environments and stress ratios are given. The microstructural inhomogeneities of materials and micro-cracks played an important roles in very high cycle fatigue behaviors. The failure mechanisms for the fatigue design of materials and components are mentioned.