• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.702-703
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• 2015

Currently, according to the climate change, serious damage by Typhoon has been occurred in the world. In this respect, the research on the damage prediction model to minimize the damage from various natural disaster has been conducted in several developed countries. In the case of U.S, various damage prediction models of buildings from natural disasters have been used widely in many organizations such as insurance companies and governments. In South Korea, although studies regarding damage prediction model of hurricane have been conducted, the scope has been only limited to consider the property of hurricane. However, it is necessary to consider various factors such as socio-economic, physical, geographical, and built environmental factors to predict the damages. Therefore, to address this issue, correlation analysis is conducted between various variables based on the data of hurricane from 2003 to 2012. The findings of this study can be utilized to develop for predicting the damage of hurricane on buildings.

• Wind and Structures
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• 제13권2호
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• pp.95-108
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• 2010

This review paper discusses research from the last few years relating to windborne debris risk models and the essential elements of engineering damage prediction models. Generic types of windborne debris are discussed. The results of studies of debris trajectories that are relevant to damage models are described - in particular the horizontal component of debris velocity as a function of distance travelled. The merits of impact momentum versus impact kinetic energy as a relevant parameter for predicting damage are considered, and how published data from generic cannon Impact tests can be used in risk models. The quantitative variation of debris impact damage with wind speed is also discussed. Finally the main elements of previously-proposed debris damage models are described.

• 한국산학기술학회논문지
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• 제19권2호
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• pp.668-675
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• 2018

전 세계적으로 도시화와 산업화의 발달은 많은 양의 전력을 필요로 하였다. 그리하여 연안 지역에 원자력 발전소를 비롯한 주요 사회기반시설의 건설이 가속화되었다. 또한 지구 온난화와 이상 기후 현상에 의해 자연 재해의 강도는 증가하고 있다. 자연 재해는 발생 지점과 규모를 예측하기 어렵고, 인명 피해와 재산 피해에 영향을 주고 있다. 이러한 문제로 인하여 연안 지역의 피해 예측과 재해 규모의 산정은 중요한 문제가 되었다. 그리하여 본 연구에서는 예측 가능한 기상 자료를 바탕으로 풍랑 피해의 피해액을 예측하고 예측한 결과를 바탕으로 풍랑 피해에 대하여 사전 대비 차원의 재난 관리가 가능할 것이라 판단된다. 본 연구에서는 재해 통계 자료가 부족한 시 군 구는 인접한 기상 관측소의 자료를 활용하는 지역은 군집분석을 활용하였다. 예측 가능한 기상자료와 지역 등급을 반영하였고, 재해 통계를 기반으로 남해연안지역의 풍랑 피해 예측함수를 개발 하였고, 검증 작업으로는 NRMSE를 활용하였다. 그 결과 NRMSE는 1.61%에서 21.73%로 분석되었다.

• Structural Monitoring and Maintenance
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• 제5권3호
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• Steel and Composite Structures
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• 제52권6호
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• pp.695-711
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• 2024

Suspension bridges are critical to urban transportation, but those in earthquake-prone areas face unique challenges. In the event of a moderate or strong earthquake, conventional linear theory-based approaches for detecting bridge damage become inadequate. This study presents an efficient method for identifying damage in suspension bridges using time history nonlinear inelastic analysis. A practical advanced analysis program is employed to model cable-supported bridges with low computational cost, generating a dataset for four hybrid models: PSO-DT, PSO-RF, PSO-XGB, and PSO-CGB. These models combine decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with particle swarm optimization (PSO) to capture nonlinear correlations between displacement response and damage. Principal component analysis reduces dataset dimensions, and PSO selects the optimal model. A numerical case study of a suspension bridge under simulated earthquake conditions identifies PSO-XGB as the best model for predicting stiffness reduction. The results demonstrate the method's robustness for nonlinear damage detection in suspension bridges under earthquake excitation.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.616-621
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• 2003

Fluid-elastic instability is believed to be a cause of the large-amplitude vibration and resulting rapid wear of heat exchanger tubes when the flow velocity exceeds a critical value. For sub-critical flow velocities, the random turbulence excitation is the main mechanism to be considered in predicting the long-term wear of steam generator tubes. Since flow-induced interactions of the tubes with tube supports in the sub-critical flow velocity can cause a localized tube wear, tube movement in the clearance between the tube and tube support as well as the normal contact force on the tubes by fluid should be maintained as low as possible. A simplified method is used for predicting fretting-wear damage of the double $90^{\circ}$U-bend tubes. The approach employed is based on the straight single-span tube analytical model proposed by Connors, the linear structural dynamic theory of Appendix N-1300 to ASME Section III and the Archard's equation for adhesive wear. Results from the presented method show a similar trend compared with the field data. This method can be utilized to predict the fretting-wear of the double $90^{\circ}$U-bend tubes in steam generators.

• Steel and Composite Structures
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• 제14권4호
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Computers and Concrete
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• 제16권6호
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• pp.933-961
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• 2015

Damage by high-speed impact fracture is a dominant mode of failure in several applications of concrete structures. Numerical modelling can play a crucial role in understanding and predicting complex fracture processes. The commonly used mesh-based Finite Element Method has difficulties in accurately modelling the high deformation and disintegration associated with fracture, as this often distorts the mesh. Even with careful re-meshing FEM often fails to handle extreme deformations and results in poor accuracy. Moreover, simulating the mechanism of fragmentation requires detachment of elements along their boundaries, and this needs a fine mesh to allow the natural propagation of damage/cracks. Smoothed Particle Hydrodynamics (SPH) is an alternative particle based (mesh-less) Lagrangian method that is particularly suitable for analysing fracture because of its capability to model large deformation and to track free surfaces generated due to fracturing. Here we demonstrate the capabilities of SPH for predicting brittle fracture by studying a slender concrete structure (column) under the impact of a high-speed projectile. To explore the effect of the projectile material behaviour on the fracture process, the projectile is assumed to be either perfectly-elastic or elastoplastic in two separate cases. The transient stress field and the resulting evolution of damage under impact are investigated. The nature of the collision and the constitutive behaviour are found to considerably affect the fracture process for the structure including the crack propagation rates, and the size and motion of the fragments. The progress of fracture is tracked by measuring the average damage level of the structure and the extent of energy dissipation, which depend strongly on the type of collision. The effect of fracture property (failure strain) of the concrete due to its various compositions is found to have a profound effect on the damage and fragmentation pattern of the structure.

• 정보처리학회논문지B
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• 제18B권5호
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• pp.271-278
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• 2011

최근 급증하는 기상이변 및 기후온난화 현상은 풍수로 인한 피해를 더욱 가속시키고 있어 풍수해 발생가능성을 미리 예측하여 선제적으로 대응할 방안 마련이 필요하다. 재난 재해의 위험성 분석은 주로 확률 통계기법에 기반한 수식모델 연구가 주류를 이루고 있고 소방방재청 국립방재연구소에서 구축한 태풍위원회 재해정보시스템(TCDIS: Typhoon Committee Disaster Information System) 또한 지역별 풍수해 위험성 분석에 확률모델을 활용하고 있다. 본 논문에서는 경험적 패턴인식에 탁월한 성능을 가진 신경망 알고리즘을 활용하여 개발한 풍수해 예측모델을 소개하고 이 모델과 TCDIS의 KDF 확률밀도함수를 이용한 풍수해 예측모델의 성능 비교 결과를 제시하여 기존 TCDIS의 위험성 분석기능에 신경망 모델을 적용함으로써 시스템의 강건성과 예측 정확도 향상이 가능함을 보이고자 한다.

• Coupled systems mechanics
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• 제4권1호
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• pp.19-36
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• 2015

Modelling and analysis of a brick masonry building involves uncertainties like modelling assumptions and properties of local material. Therefore, it is necessary to perform a calibration to evaluate the dynamic properties of the structure. The response of the finite element model is improved by predicting the parameter by performing linear dynamic analysis on experimental data by comparing the acceleration. Further, a nonlinear dynamic analysis was also performed comparing the roof acceleration and damage pattern of the structure obtained analytically with the test findings. The roof accelerations obtained analytically were in good agreement with experimental roof accelerations. The damage patterns observed analytically after every shock were almost similar to that of experimental observations. Damage pattern with amplification in roof acceleration exhibit the potentiality of earthquake resistant measures in brick masonry models.