• 한국건설관리학회논문집
• /
• 제4권1호
• /
• pp.97-105
• /
• 2003

건설실패에 관련된 정보는 연구문헌, 사례집, 보고서 등에서 제공하고 있지만, 실패정보에 대한 체계적인 분류가 구축되어 있지 않아, 정보의 활용에 많은 문제점이 있다. 따라서 본 논문에서는 국내$\cdot$외의 건설실패연구 관련기관 및 문헌을 조사$\cdot$분석하여 건설실패정보 분류체계를 제안하였는데, 시설물 일반정보, 실패상황정보, 실패원인정보, 실패대책정보의 4개의 대분류로 구성되어 있다. 그리고 각각의 대분류항목은 중분류항목과 소분류항목으로 구성하였다. 본 연구에서 제시한 건설실패분류 체계는 실패사례의 정형화$\cdot$표준화를 통하여 건설산업 참여주체들이 실패정보를 공유함으로써 실패의 재발을 방지하는 유용한 자료로 활용될 수 있을 것이다.

• Structural Engineering and Mechanics
• /
• 제84권1호
• /
• pp.1-16
• /
• 2022

An efficient and accurate classification method for failure modes of reinforced concrete (RC) columns was proposed based on key characteristic parameters. The weight coefficients of seven characteristic parameters for failure modes of RC columns were determined first based on the support vector machine-recursive feature elimination. Then key characteristic parameters for classifying flexure, flexure-shear and shear failure modes of RC columns were selected respectively. Subsequently, a support vector machine with key characteristic parameters (SVM-K) was proposed to classify three types of failure modes of RC columns. The optimal parameters of SVM-K were determined by using the ten-fold cross-validation and the grid-search algorithm based on 270 sets of available experimental data. Results indicate that the proposed SVM-K has high overall accuracy, recall and precision (e.g., accuracy>95%, recall>90%, precision>90%), which means that the proposed SVM-K has superior performance for classification of failure modes of RC columns. Based on the selected key characteristic parameters for different types of failure modes of RC columns, the accuracy of SVM-K is improved and the decision function of SVM-K is simplified by reducing the dimensions and number of support vectors.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2003년도 추계학술대회 논문집(II)
• /
• pp.259-264
• /
• 2003

Failure code system must include data of maintenance history, classification of failure, affective range and situation when failure occur. But the present failure code system have used a simple code system for classification to include only merchandise and tools. Advantageously, expansional standard code system that will be developed, it make that users can take steps of standardized overhaul and inspection as proposal maintain contents when failure occur or something wrong in vehicle of urban transit. Standardized failure codes must be developed and used that manufacturing companies and urban transit operating companies in order to give effect to maintenance works.

• 한국지진공학회논문집
• /
• 제28권2호
• /
• pp.113-119
• /
• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• 산업경영시스템학회지
• /
• 제43권2호
• /
• pp.33-38
• /
• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• Structural Engineering and Mechanics
• /
• 제75권6호
• /
• pp.771-784
• /
• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

• 동의생리병리학회지
• /
• 제27권4호
• /
• pp.460-464
• /
• 2013

The purpose of this case was to report the effect of Korean medical treatment for patient with pleural effusion due to congestive heart failure. The patient was treated with herbal medicine(Cheongsingeonbi-tang) and acupuncture. The effect of treatment was evaluated by chest X-ray, New York Heart Association(NYHA) functional classification, and Hugh-Jones classification. After 3 weeks of treatment, the amount of pleural effusion was decreased and NYHA class, Hugh-Jones grade were improved. NYHA functional classification improved class III to II and Hugh-Jones classification changed grade IV to II. This result suggests that herbal medicine(Cheongsingeonbi-tang) and acupuncture treatment might have an effect on patient with pleural effusion due to congestive heart failure.

• Geomechanics and Engineering
• /
• 제20권2호
• /
• pp.113-120
• /
• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• 시스템엔지니어링학술지
• /
• 제18권1호
• /
• pp.46-57
• /
• 2022

In this paper, we proposed the DFMEA Implementation Method for safety design of Weapon System. First, we presented the process for DFMEA. And then, the case analysis of OOO missile was performed in accordance with the process presented. After defining the system requirements of OOO missile, failure definition scoring criteria was set. In order to clarify the definition of failure, the failure was classified into safety, reliability, maintainability and others. After performing the function analysis, the relationship matrix analysis was performed to identify the failure mode according to the function without omission. After clarifying the failure classification, mode of failure, cause of failure and effect were analyzed to calculate the severity, occurrence and detection values. After the action priority was judged, the recommended action according to the failure classification was identified for the determined action priority. The results of this study can be used as a relevant basis for the design reflection and resource re-allocation of stakeholders.

• 대한조선학회논문집
• /
• 제60권6호
• /
• pp.441-449
• /
• 2023

With the enforcement of environmental regulations by the International Maritime Organization, the market for eco-friendly ships is expanding, and ships using electric propulsion devices are emerging as a promising solution. Many studies have been conducted to predict the failure of ships, but most of them are mainly research on the main diesel engine of ships. As the ship's propulsion method changes, new data is needed to predict the failure of electric propulsion ships. In this paper aims to analyze the failure characteristics of the electric propulsion system in consideration of the difference in the type of failure between the internal diesel engine and the electric propulsion system. The ship's propulsion unit assumed a DC motor and a signal pattern for normal conditions and general failure modes, but the failure record of the electric propulsion device operated on the actual ship was not available, so it generated a failure signal for small electric motor equipment to identify the failure signal. Assuming unbalance, misalignment, and bearing failure, which are the primary failure modes of the ship's electric motor, a failure signal was generated using a "rotator vibration data generator," and the frequency band, size, and phase difference of the measured vibration signal were analyzed to analyze the characteristics of each failure condition. Finally, the characteristics of each failure condition were identified so that the signals according to the failure type could be classified.