• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.4
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• pp.347-355
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• 2013

A damage estimation method for monopile support structure of offshore wind turbine using modal properties and committee of neural networks is presented for effective structural health monitoring. An analytical model for a monopile support structure is established, and the natural frequencies, mode shapes, and mode shape slopes for the support structure are calculated considering soil condition and added mass. The input to the neural networks consists of the modal properties and the output is composed of the stiffness indices of the support structure. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated stiffness indices from different neural networks are averaged. Ten damage cases are estimated using the proposed method, and the identified damage locations and severities agree reasonably well with the exact values. The accuracy of the estimation can be improved by applying the committee of neural networks which is a statistical approach averaging the damage indices in the functional space.

• The Journal of Engineering Geology
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• v.25 no.4
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• pp.439-447
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• 2015

In this study, specific sections vulnerable to debris flow damage were selected, and a complete enumeration survey was performed for the sections with debris flow hazards. Based on this, the characteristics of the sections with debris flow hazards and the current status of actions against debris flow were examined, and an efficient installation plan for a debris flow damage prevention method that is required in the future was suggested. The results indicated that in the Route 56 section where the residential density is relatively higher between the two model survey sections, facilities for debris flow damage reduction were insufficient compared to those in the Route 6 section which is a mountain area. It is thought that several sites require urgent preparation of a facility for debris flow damage reduction. In addition, a numerical analysis showed that for debris barriers installed as a debris flow damage prevention method, distributed installation of a number of small-scale barriers facilities within a valley part was more effective than single installation of a large-scale debris barrier at the lower part of a valley.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.66-73
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• 2021

This report documents the results of an at-power internal events Level 1 Probabilistic Safety Assessment (PSA) for a Korea research reactor (KRR). The aim of the study is to determine the accident sequences, construct an internal level 1 PSA model, and estimate the core damage frequency (CDF). The accident quantification is performed using the AIMS-PSA software version 1.2c along with a fault tree reliability evaluation expert (FTREX) quantification engine. The KRR PSA model is quantified using a cut-off value of 1.0E-15/yr to eliminate the non-effective minimal cut sets (MCSs). The final result indicates a point estimate of 4.55E-06/yr for the overall CDF attributable to internal initiating events in the core damage state for the KRR. Loss of Electric Power (LOEP) is the predominant contributor to the total CDF via a single initiating event (3.68E-6/yr), providing 80.9% of the CDF. The second largest contributor is the beam tube loss of coolant accident (LOCA), which accounts for 9.9% (4.49E-07/yr) of the CDF.

• Journal of Environmental Impact Assessment
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• v.23 no.1
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• pp.39-50
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• 2014

In recent years, the frequency and scale of the natural disasters are growing rapidly due to the global climate change. In case of the urban flooding, high-density of population and infrastructure has caused the more intensive damages. In this study, we analyzed the spatial characteristics of urban flooding damage factors using GWR(Geographically Weighted Regression) for effective disaster prevention and then, classified the causes of the flood damage by spatial characteristics. The damage factors applied consists of natural variables such as the poor drainage area, the distance from the river, elevation and slope, and anthropogenic variables such as the impervious surface area, urbanized area, and infrastructure area, which are selected by literature review. This study carried out the comparative analysis between OLS(Ordinary Least Square) and GWR model for identifying spatial non-stationarity and spatial autocorrelation, and in the results, GWR model has higher explanation power than OLS model. As a result, it appears that there are some differences between each of the flood damage areas depending on the variables. We conclude that the establishment of disaster prevention plan for urban flooding area should reflect the spatial characteristics of the damaged areas. This study provides an improved understandings of the causes of urban flood damages, which can be diverse according to their own spatial characteristics.

• Journal of Mechanical Science and Technology
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• v.18 no.2
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• pp.230-239
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• 2004

A compact model approach of a network of spring elements for elastic loading is presented for the thermal deformation analysis of BGA package assembly. High-sensitivity moire interferometry is applied to evaluate and calibrated the model quantitatively. Two ball grid array (BGA) package assemblies are employed for moire experiments. For a package assembly with a small global bending, the spring model can predict the boundary conditions of the critical solder ball excellently well. For a package assembly with a large global bending, however, the relative displacements determined by spring model agree well with that by experiment after accounting for the rigid-body rotation. The shear strain results of the FEM with the input from the calibrated compact spring model agree reasonably well with the experimental data. The results imply that the combined approach of the compact spring model and the local FE analysis is an effective way to predict strains and stresses and to determine solder damage of the critical solder ball.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Earthquakes and Structures
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• v.22 no.2
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• pp.185-201
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• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.5
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• pp.1311-1314
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• 2006

This study was peformed to investigate possible mechanisms underlying possible effect of Coicis Semen composition (CSC) on inflammatory diseases using in vivo model of RA in the mice. Results are summarized as follows. In production of inflammatory cytokines, INF-${\gamma}$ in the spleen and IL-6 in the serum were decreased by CSC treatment. TNF-${\alpha}$ in serum was significantly decreased, IL-4 in the spleen was significantly increased by CSC treatment. In production of rheumatoid factors, IgM and IgG were significantly decreased by CSC treatment. The present data suggest that CSC treatment can improve pathological damage by CIA. So we expect that CSC should be used as a effective drugs for not only rheumatoid arthritis but also another autoimmune disease. Therefore we have to survey continuously in looking for the effective substance and mechanism in the future.

• Journal of the Korean Society of Safety
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• v.35 no.3
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• pp.86-95
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• 2020

This paper describes the work and the results of the final Probabilistic Safety Assessment (PSA) for the Jordan Research and Training Reactor (JRTR). This final 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, nine typical initiating events were selected regarding internal events during the normal operation of the reactor. AIMS-PSA (Version 1.2c) was used for the accident quantification, and FTREX was used as the quantification engine. 1.0E-15/yr of the cutoff value was used to deliminate the non-effective Minimal Cut Sets (MCSs) when quantifying the JRTR PSA model. As a result, the final result indicates a point estimate of 2.02E-07/yr for the overall Core Damage Frequency (CDF) attributable to internal initiating events in the core damage state for the JRTR. A Loss of Primary Cooling System Flow (LOPCS) is the dominant contributor to the total CDF by a single initiating event (9.96E-08/yr), and provides 49.4% of the CDF. General Transients (GTRNs) are the second largest contributor, and provide 32.9% (6.65E-08/yr) of the CDF.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.