• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.3
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• pp.167-175
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• 2002

In this paper, an analytical model is proposed for analyzing the hysteretic behavior of RC beam with relocated plastic hinge region under load reversals. The plastic hinge is modeled not to be concentrated on a point but to be distributed on a finite size in beam. This is based on the assumption that the plastic hinge is formed over a certain region, in which the curvature varies. Tangential matrix is reformed using stiffness coefficients including variales such as the length and location of plastic hinge region. In order to construct the hysteretic rule of hinge, modified Takeda rule is also proposed on the base of regression analysis for the previous test results. Previous specimens are analyzed using the proposed model and the result is compared with test result. On the result of the comparison, it was shown that the hysteretic behavior of beams with different location of plastic hinge region could be prediced using the proposed analytical process.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.2
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• pp.233-240
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• 1999

Even though the recent construction safety disasters not only result in the loss inside construction sites but also become to a large public disasters, safety activities are managed in an irrational way and safety rules are ignored in the construction sites which leads to occur same type of disasters repeatedly. In this paper, a fuzzy set theoretic approach to risk analysis is proposed as an alternative to the techniques currently used in the general construction projects safety. Then the concept of risk evaluation using linguistic representation of the likelihood, exposure and consequences is introduced. A risk assessment model using approximate reasoning technique base on fuzzy logic is presented to drive fuzzy values of risk and numerical example for risk analysis is also presented to illustrate the results.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Nuclear Engineering and Technology
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• v.36 no.4
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• pp.316-326
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• 2004

The $40\%$ of wall criterion, which is generally used for the plugging of steam generator tubes, is applied only to a single crack. In a previous study, a total number of 9 failure models were proposed to estimate the local failure of the ligament between cracks, and the optimum coalescence model of multiple collinear cracks was determined among these models. It is, however known that parallel axial cracks are more frequently detected than collinear axial cracks during an in-service inspection. The objective of this study is to determine the plastic collapse model that can be applied to steam generator tubes containing two parallel axial through-wall cracks. Three previously proposed local failure models were selected as the candidates. Subsequently, the interaction effects between two adjacent cracks were evaluated to screen them. Plastic collapse tests for the plate with two parallel through-wall cracks and finite element analyses were performed to determine the optimum plastic collapse model. By comparing the test results with the prediction results obtained from the candidate models, a COD base model was selected as an optimum model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1301-1316
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• 2024

In modern warfare, missiles play a pivotal role but typically spend the majority of their lifecycle in long-term storage or standby mode, making it difficult to detect failures. Preemptive detection of missiles that will fail is crucial to preventing severe consequences, including safety hazards and mission failures. This study proposes a contamination-based stacking ensemble model, employing the local outlier factor (LOF), to detect such missiles. The proposed model creates multiple base LOF models with different contamination values and combines their anomaly scores to achieve a robust anomaly detection. A comparative performance analysis was conducted between the proposed model and the traditional single LOF model, using production-related inspection data from missiles deployed in the military. The experimental results showed that, with the contamination parameter set to 0.1, the proposed model exhibited an increase of approximately 22 percentage points in accuracy and 71 percentage points in F1-score compared to the single LOF model. This approach enables the preemptive identification of potential failures, undetectable through traditional statistical quality control methods. Consequently, it contributes to lower missile failure rates in real battlefield scenarios, leading to significant time and cost savings in the military industry.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.55-64
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• 2017

In this paper, we propose and implement an IoT based mobile smart monitoring system in the view point of safety inspection for solar power generation. The main features and contributions of proposed system are as follows. First, the proposed system model can evaluate periodically in the view point of safety inspection the conditions of the system and structure of solar power generation. Second, the proposed system automatically re-processes the measurement data of the system and structure for solar power generation and save it into database. Third, using the re-processed and saved information, the proposed system can provide the monitoring information with webpage form to both administrator and owner of solar power generation system, thus they can measure and confirm directly in the view point of safety inspection the conditions of the solar generation structure without visiting those places. Fourth, the provided web pages for the monitoring of solar power generation can be accessed regardless of the system structures. The performance evaluations of the proposed system show that the proposed monitoring system can save efficiently the data received from the sensors installed in the structure of solar power generation into the data base in the collecting server. And the proposed system can support that both administrator and user of solar power generation system access webpage in real time without considering places by using mobile phone and desktop computer and obtain the information for the conditions of the system and structure of solar power generation with graph forms.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.1
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• pp.179-191
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• 2002

This paper presents a systematic approach to the seismic nonlinear analysis and retrofit strategies for existing bridges with isolation system using retrofit slate function newly proposed in this study. A seismic retrofit scheme using sliding base isolation system was presented to reduce the seismic hazard for bridge structures. In this study, two types of isolation systems such as lead bearings and sliding isolators were used. The behavior of sliding isolators was modeled by a triaxial interaction model. And three types of earthquakes such as El Centro, San Fernando, and the artificial were used as earthquake ground excitations. Seismic response analyses of the bridge before and after retrofit were effectively carried out by using a three-dimensional nonlinear seismic analysis program, IDARC-Bridge. Also, this paper proposes a retrofit state function for easily representing the efficiency of a retrofit scheme.

• Smart Structures and Systems
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• v.29 no.1
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• Nuclear Engineering and Technology
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• v.31 no.4
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• pp.375-384
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• 1999

The credibility of CHECWORKS FAC model analysis was evaluated for plant application in a model plant chosen for demonstration. The operation condition at each pipe component was defined before the wear rate analysis by plant data base, water chemistry analysis, and network flow analysis. The predicted wear was compared with the measured wear for 57 sample components selected from 43 susceptible line groups analysed. The inspected 57 locations represent components of highest predicted wear in each line group. Both absolute value and relative ranking comparisons indicated reasonable correlations between the predicted and the measured values. Four components showed much higher measured wear rates than the predicted ones in the feed water train from main feed water pump discharge to steam generator, probably due to high hydrazine concentration operation the effect of which had not been incorporated into the CHECWORKS model. The measured wear was higher than the predicted one consistently for components with least susceptibility to FAC. It is believed that the conservatism maintained during UT data analysis dominated the measurement accuracy. A great deal of enhancement is anticipated over the current plant pipe management program when a comprehensive plant pipe management program is implemented based on the model analysis.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.1
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• pp.1-9
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• 2013

In this study of Eccentric Braced Frames have identified the following target eccentricity on the length of the inelastic behavior of the reaction by calculating the correction factor by comparing it to the value suggested by the earthquake provided material for the rational design aims to There are. As a variable-length V-braced frame analysis model stations were set up. Eccentricity faults in the model according to the length stiffness ratio, the maximum amount of energy dissipation were analyzed base shear and multi-layered model of the reaction from the eccentricity correction factor calculated on the length of the building standards proposed by KBC 2009 in response eccentricity correction factor calculated from The length varies. does not have the same response modification factor was confirmed.