• 한국안전학회지
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• 제34권3호
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• pp.102-110
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• 2019

This paper describes the work and results of the preliminary Probabilistic Safety Assessment (PSA) for a research reactor in the design phase. This preliminary PSA was undertaken to assess the level of safety for the design of a research reactor and to evaluate whether it is probabilistically safe to operate and reliable to use. The scope of the PSA described here is a Level 1 PSA which addresses the risks associated with core damage. After reviewing the documents and its conceptual design, eight typical initiating events are selected regarding internal events during the normal operation of the reactor. Simple fault tree models for the PSA are developed instead of the detailed model at this conceptual design stage. A total of 32 core damage accident sequences for an internal event analysis were identified and quantified using the AIMS-PSA. LOCA-I has a dominant contribution to the total CDF by a single initiating event. The CDF from the internal events of a research reactor is estimated to be 7.38E-07/year. The CDF for the representative initiating events is less than 1.0E-6/year even though conservative assumptions are used in reliability data. The conceptual design of the research reactor is designed to be sufficiently safe from the viewpoint of safety.

• Earthquakes and Structures
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• 제25권6호
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Structural Engineering and Mechanics
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• 제16권5호
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• pp.581-595
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• 2003

Since the conventional direct approaches are hard to be applied for damage diagnosis of complex large-scale structures, a two-step approach for diagnosing the joint damage of framed structures is presented in this paper by using artificial neural networks. The first step is to judge the damaged areas of a structure, which is divided into several sub-areas, using probabilistic neural networks with natural Frequencies Shift Ratio inputs. The next step is to diagnose the exact damage locations and extents by using the Radial Basis Function (RBF) neural network with the second Element End Strain Mode of the damaged sub-area input. The results of numerical simulation show that the proposed approach could diagnose the joint damage of framed structures induced by earthquake action effectively and has reliable anti-jamming abilities.

• Wind and Structures
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• 제13권2호
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• pp.191-206
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• 2010

Windborne debris is a major cause of structural damage during severe windstorms and hurricanes owing to its direct impact on building envelopes as well as to the 'chain reaction' failure mechanism it induces by interacting with wind pressure damage. Estimation of debris risk is an important component in evaluating wind damage risk to residential developments. A debris risk model developed by the authors enables one to analytically aggregate damage threats to a building from different types of debris originating from neighboring buildings. This model is extended herein to a general debris risk analysis methodology that is then incorporated into a vulnerability model accounting for the temporal evolution of the interaction between pressure damage and debris damage during storm passage. The current paper (Part I) introduces the debris risk analysis methodology, establishing the mathematical modeling framework. Stochastic models are proposed to estimate the probability distributions of debris trajectory parameters used in the method. It is shown that model statistics can be estimated from available information from wind-tunnel experiments and post-damage surveys. The incorporation of the methodology into vulnerability modeling is described in Part II.

• Structural Monitoring and Maintenance
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• 제2권1호
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.453-464
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• 2001

A commercial nuclear power station contains at least tow emergency diesel generators(EDG) to control the risk of severe core damage during station blackout accidents. Therefore, the reliability of the EDG's to start and load-run on demand must be maintained at a sufficiently high level. Probabilistic safety assessments(PSA) are increasingly being used to quantify the public risk of operating potentially hazardous systems such as nuclear power reactors. In this paper, to perform PSA, we will introduce three different types of data and use Bayes procedure to estimate the error rate of nuclear power plant EDG, and using practical examples, illustrate which method is more reasonable in our situation.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.473-480
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• 2006

Liquefaction of soil foundation is one of the major seismic damage types of infrastructures. In this paper, deterministic and probabilistic approaches for the evaluation of liquefaction potential are briefly summarized and the risk assessment method is newly proposed using seismic fragility and seismic hazard curves. Currently the deterministic approach is widely used to evaluate the liquefaction potential in Korea. However, the there are a certain degree of uncertainties in the soil properties such as elastic modulus and resistant capacity, therefore the probabilistic approach is more promising. Two types of probabilistic approach are introduced including (1) failure probability for a given design earthquake and (2) the seismic risk of liquefaction of soil for a given service life. The results from different methods show a similar trend, and the liquefaction potential can be more quantitatively evaluated using risk analysis method.

• Geomechanics and Engineering
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• 제9권2호
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• pp.125-144
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• 2015

This paper presents the probabilistic seismic microzonation of densely populated Kolkata city, situated on the world's largest delta island with very soft alluvial soil deposit. At first probabilistic seismic hazard analysis of Kolkata city was carried out at bedrock level and then ground motion amplification due to sedimentary deposit was computed using one dimensional (1D) wave propagation analysis SHAKE2000. Different maps like fundamental frequency, amplification at fundamental frequency, peak ground acceleration (PGA), peak ground velocity (PGV), peak ground displacement (PGD), maximum response spectral acceleration at different time period bands are developed for variety of end users, structural and geotechnical engineers, land use planners, emergency managers and awareness of general public. The probabilistically predicted PGA at bedrock level is 0.12 g for 50% exceedance in 50 years and maximum PGA at surface level it varies from 0.095 g to 0.18 g for same probability of exceedance. The scenario of simulated ground motion revealed that Kolkata city is very much prone to damage during earthquake.

• Earthquakes and Structures
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• 제22권3호
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• pp.289-304
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• 2022

During strong ground motions, adjacent structures with insufficient separation distances collide with each other causing considerable architectural and structural damage or collapse of the whole structure. Generally, existing design procedures for determining the separation distance between adjacent buildings subjected to structural pounding are based on approximations of the buildings' peak relative displacement. These procedures are based on unknown safety levels. This paper attempts to evaluate the influence of foundation flexibility on the structural seismic response by considering the variability in the system and uncertainties in the ground motion characteristics through comprehensive numerical simulations. Actually, the aim of this study is to evaluate the influence of foundation flexibility on probabilistic evaluation of structural pounding. A Hertz-damp pounding force model has been considered in order to effectively capture impact forces during collisions. In total, 5.25 million time-history analyses were performed over the adopted models using an ensemble of 25 ground motions as seismic input within OpenSees software. The results of the study indicate that the soil-structure interaction significantly influences the pounding-involved responses of adjacent structures during earthquakes and generally increases the pounding probability.

• 대한기계학회논문집
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• 제19권8호
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• pp.1915-1926
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• 1995

The Fatigue characteristics of 8-harness satin woven CFRP composites with a circular hole are experimentally investigated under constant amplitude tension-tension loading. It is found in this study that the fatigue damage accumulation behavior is very random and history-independent, and the fatigue cumulative damage is linearly related with the mean number of cycles to a specified damage state. From these results, it is known that the fatigue characteristics of CFRP composites satisfy the basic assumptions of Markov chain theory and the parameter of Markov chain model can be determined only by mean and variance of fatigue lives. The predicted distribution of the fatigue cumulative damage using Markov chain model shows a good agreement with the test results. For the fatigue life distribution, Markov chain model makes similar accuracy to 2-parameter Weibull distribution function.