• Earthquakes and Structures
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• v.7 no.4
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• pp.485-510
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• 2014

In performance-based seismic design procedures Peak Ground Acceleration (PGA) and pseudo-Spectral acceleration ($S_a$) are commonly used to predict the response of structures to earthquake. Recently, research has been carried out to evaluate the predictive capability of these standard Intensity Measures (IMs) with respect to different types of structures and Engineering Demand Parameter (EDP) commonly used to measure damage. Efforts have been also spent to propose alternative IMs that are able to improve the results of the response predictions. However, most of these IMs are not usually employed in probabilistic seismic demand analyses because of the lack of reliable Ground Motion Prediction Equations (GMPEs). In order to define seismic hazard and thus to calculate demand hazard curves it is essential, in fact, to establish a GMPE for the earthquake intensity. In the light of this need, new GMPEs are proposed here for the elastic input energy spectra, energy-based intensity measures that have been shown to be good predictors of both structural and non-structural damage for many types of structures. The proposed GMPEs are developed using mixed-effects models by empirical regressions on a large number of strong-motions selected from the NGA database. Parametric analyses are carried out to show the effect of some properties variation, such as fault mechanism, type of soil, earthquake magnitude and distance, on the considered IMs. Results of comparisons between the proposed GMPEs and other from the literature are finally shown.

• Earthquakes and Structures
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• v.20 no.1
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.5
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• pp.373-386
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• 2023

To analyze the prediction of geological conditions and water-bearing zones, TSP was performed in the collapse zone of the fault zone. The results of the TSP were verified by comparing them to the face mapping results of the prediction zone. The rock quality prediction result of the TSP had an error of about 3 to 10 meters compared to the face mapping result, but the overall rock quality change and ground condition were analyzed to be relatively similar. In the water-bearing zones of the face mapping results, the Vp/Vs ratio ranges from 1.79 to 2.37 and the Poisson's ratio ranges from 0.27 to 0.39. In the sections other than the water-bearing zones, the Vp/Vs ratio ranges from 1.61 to 1.89, and the Poisson's ratio ranges from 0.19 to 0.3. As a result of analyzing the Vp/Vs ratio and Poisson's ratio in the water-bearing zones, it is analyzed that the sections with a Vp/Vs ratio of 2.0 or more and a Poisson's ratio of 0.3 or more have a high possibility of being water-bearing zones.

• The Journal of Engineering Geology
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• v.27 no.1
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• pp.81-90
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• 2017

Preceding displacements in tunnel are difficult to predict since the measurements of displacements after excavation can not be performed immediately. In the present study, The longitudinal displacements which can be measured immediately after excavation are used to predict the crown settlements occurring before excavation only if fault is located at the tunnel crown. Three-dimensional finite element analysis was conducted using 28 numerical models with various fault attitudes to analyze the correlation between the longitudinal displacements on tunnel face and preceding crown settlements. The results, $L_{face}/C$ ratio show 2~12% in the drives with dip models and 2~13% in the drives against dip models individually. In addition, each model has a certain $L_{face}/C$ ratio. The result of the regression analysis show that the coefficient of determination is over 0.8 in most models. Therefore, crown settlements occurring before excavation can be predicted by analyzing the longitudinal displacements occurring on tunnel faces.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.1-12
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• 2010

Recently, large amount of information in IDS(Intrusion Detection System) can be un manageable and also be mixed with false prediction error. In this paper, we propose a data mining methodology for IDS, which contains uncertainty based on training process and post-processing analysis additionally. Our system is trained to classify the existing attack for misuse detection, to detect the new attack pattern for anomaly detection, and to define border patter between attack and normal pattern. In experimental results show that our approach improve the performance against existing attacks and new attacks,from 0.62 to 0.84 about 35%.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.324-325
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• 2011

This paper addresses the assessment of voltage sag costs based on the stochastic prediction of voltage sags. When voltage sags below a certain voltage threshold occur at sensitive industrial process, the industrial customer will experience financial damage. In order to mitigate voltage sag costs and devise efficient solutions to mitigate damage, a study on the financial loss assessment of voltage sags is basically needed. In order to assess the voltage sag costs, the expected sag frequency at a sensitive load point should be calculated by using the concept of the area of vulnerability and historical fault statistics. Then, financial loss due to voltage sags can be obtained by multiplying the expected sag frequency by the cost per sag event.

• Proceedings of the Materials Research Society of Korea Conference
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• 2003.11a
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• pp.91-91
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• 2003

Sliding wear behaviors of Inconel 600 and 690 were investigated at room temperature in air. In the present study, Archard's equation which has low reliability was modified. In the prediction of wear volume by Archard's equation, the reliabilities of Inconel 600 and 690 were about from 26.3% to 45.7% and from 69. l% to 88.6%, respectively, The sliding wear behaviors of Inconel 600 and 690 turned out to be influenced by their stacking fault energy, and the fact was confirmed by using TEM and micro-hardness test Based on experimental results, the wear coefficient was modified as a function of the sliding distance. The calculation with the modified wear equation showed that the reliability of Inconel 600 tested with 409 ferritic stainless steel increased from 45.7% to 93.4%.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.635-637
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• 1998

시험 단계나 운용 단계에서 발견된 소프트웨어의 오류를 수정하기 위해서는 많은 비용을 투자해야 한다. 시스템 개발 초기 단계인 설계 단계에서 소프트웨어 시스템의 신뢰도에 영향을 많이 미치는 부분을 찾아 오류를 사전에 방지하는 연구가 많이 진행되고 있다. 모듈의 신뢰도를 설계 단계에서 예측할 수 있다면 프로젝트 관리자는 결함 경향이 강한 모듈 개발에 더 많은 자원을 할당함으로써 보다 신뢰성 있는 소프트웨어를 생산 할 수 있다. 본 논문에서는 실시간 소프트웨어의 설계 결과에 대한 복잡도 측정치를 토대로 신뢰도를 예측하는 모델을 제안하다. 유전자 알고리즘으로 찾아낸 이 모델을 사용하여 결함 경향이 강한(fault prone) 모듈과 그렇지 않은 모듈은 96%의 정확도로 선별해 낼 수 있다.

• KIEAE Journal
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• v.10 no.4
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• pp.75-80
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• 2010

Generally, ground-source heat pump (GSHP) systems have a higher performance than conventional air-source systems. However, the major fault of GSHP systems is their expensive boring costs. Therefore, it is important issue that to reduce initial cost and ensure stability of system through accurate prediction of the heat extraction and injection rates of the ground heat exchanger. Conventional analysis methods employed by line source theory are used to predict heat transfer rate between ground heat exchanger and soil. Shape of ground heat exchanger was simplified by equivalent diameter model, but these methods do not accurately reflect the heat transfer characteristics according to the heat exchanger geometry. In this study, a numerical model that combines a user subroutine module that calculates circulation water conditions in the ground heat exchanger and FEFLOW program which can simulate heat/moisture transfer in the soil, is developed. Heat transfer performance was evaluated for 3 different types ground heat exchanger(U-tube, Double U-tube, Coaxial).