• International conference on construction engineering and project management
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• 2011.02a
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• 보존과학연구
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• s.30
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• pp.93-102
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• 2009

This study was carried out to investigate the effect of artificial accelerated ageing treatment on the permanence of lining papers produced from Korea and Japan. As time gone by, organic cultural properties are affected by chemical and physical deterioration because of various factors including the conditions of preservation environment and their material properties. In the public historical storage or owned as private collections, are vulnerable to sever amages caused by poor preservation environment as well as by other natural factors. In this study, the deterioration behaviors of lining paper in optical & mechanical properties were discussed. Overall, lining papers produced from Korea showed lower reduction in mechanical strength properties compared to the lining papers produced from Japan.

• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1966-1971
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• 2011

Generally, the characteristics of the conductor that was affected by forest fire can be analyzed only when the forest fire is accurately modeled and its effect is identified. Few studies have been conducted with a forest fire model for transmission lines, and no results of the examination of the actual test specimens that were exposed to forest fire have been reported. As the deterioration characteristics of a forest fire are difficult to analyze in the actual field, an environment that was similar to that in the field was used in this study. Deterioration was deposited on a wire using an artificial flame experiment device, to analysis the temperature, surface and component characteristics. It seems that this analysis data in this study can be used as the basic data for the database that can be utilized to analyze wires exposed to forest fire and deterioration and to predict the ACSR wire refurnishment life.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.959-969
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• 2018

The aging water pipes induce various problems, such as water supply suspension due to breakage, insufficient water pressure, deterioration of water quality, damage by sink holes, and economic losses due to water leaks. However, it is impractical and almost impossible to repair and/or replace all deteriorated water pipes simultaneously. Hence, it is required to quantitatively evaluate the deterioration rate of individual pipes indirect way to determine the rehabilitation order of priority. In this study, ANN(Artificial Neural Network)-Clustering method is suggested as a new approach to assess and assort the water pipes. The proposed method has been applied to a water supply network of YG-county in Jeollanam-do. To assess the applicability of the model, the evaluation results were compared with the results of the Numerical Weighting Method (NWM), which is being currently utilized in practice. The assessment results are depicted in a water pipe map to intuitively grasp the degree of deterioration of the entire pipelines. The application results revealed that the proposed ANN-Clustering models can successfully assess the water pipe deterioration along with the conventional approach of NWM.

• Journal of Industrial Technology
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• v.33 no.A
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Electrical & Electronic Materials
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• v.8 no.4
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• pp.495-503
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• 1995

As lengthen gap leng of artificial needle shape void 0.24.rarw.1.5[mm]), the amplitude of AE(Acoustic Emission) pulses was enlarged and number of pulses be generated was few. The longer gap length of void, the shorter breakdown time. As a result of this, I found that existen of void in insulation mate was fatal deterioration of insulation. According to phase angle of applied voltage, time being void was scattered largely in region of phase angle of pulses of origination in phase angle of applied voltage. The result will be used to analysis of void diagnosis.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.357-358
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• 2010

This study was performed an evaluation of chloride attack resistance properties of marine concrete by accelerated deterioration test of artificial seawater. As the results of study, when considering the compressive strength and chloride ion penetration of concrete, the proper type to improvement of chloride attack resistance is thought to marine cement.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.5
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• pp.199-204
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• 2002

In order to recognize the deterioration of insulation system by partial discharge (PD), the characteristics of PD patterns which are occurring at the interface between epoxy and rubber materials in extra high voltage cable joints, have been investigated. The artificial defects such as voids, metal particles, insulation fiber and water impregnated insulation fiber are planted between the interfaces. A high frequency partial discharge detection system was used for measuring PD signals. An analysis of the PD patterns is focused on the shape of PD pattern, phase, width and time-dependence for each artificial defect. The PD Patterns in each defect show the different behaviors and it is suggested that the precise discrimination of PD patterns could be used for the diagnosis of deterioration in the insulation systems.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.