• 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 Engineering and Mechanics
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• v.61 no.5
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• pp.625-635
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• 2017

Sets of nonlinear formulations together with an energy-based damage index (DI) are proposed to model the behavior and quantify the damage of the confined and unconfined concretes under monotonic and cyclic loading. The proposed formulations and DI can be employed in numerical simulations to determine the stresses and the damages to the fibers or the layers within the sections of reinforced concrete (RC) components. To verify the proposed formulations, an adaptive finite element computer program was generated to simulate the RC structures subjected to monotonic and cyclic loading. By comparing the simulated and the experimental test results, on both the full-scale structural members and concrete cylindrical samples, the proposed uniaxial behavior modeling formulations for confined and unconfined concretes under monotonic and cyclic loading, based on an iterative process, were accordingly adjusted, and then validated. The proposed formulations have strong mathematical structures and can readily be adapted to achieve a higher degree of precision by improving the relevant coefficients based on more precise tests. To apply the proposed DI, the stress-strain data of concrete elements is required. It can easily be calculated by using the proposed nonlinear constitutive laws for confined and unconfined concretes in this paper.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Smart Structures and Systems
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• v.22 no.4
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• pp.399-411
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• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• Earthquakes and Structures
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• v.15 no.5
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• pp.463-471
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• 2018

The virtual forces of the original local flexibility method are restricted to inducing stress on the local parts of a structure. To circumvent this restriction, we developed a pseudo local flexibility (PLFM) method that can successfully detect damage to hyperstatic beam structures using fewer modes. For this study, we further developed the PLFM so that it could detect damage in plate structures. We also devised the theoretical background for the PLFM with non-local virtual forces for plate structures, and both the lateral and rotary degree of freedom (DOF) measurements were considered separately. This study investigates the effects of the number of modes, the actual location that sustained damage, multiple damage locations, and noise in modal parameters for the damage detection results obtained from damaged numerical plates. The results revealed that the PLFM can be used for damage detection, localization, and quantification for plate structures, regardless of the use of the lateral DOF and/or rotary DOF.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.237-254
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• 2019

This paper presents an improved time series based damage detection approach with experimental verifications for detection, localization, and quantification of damage in shear-type structures under varying mass effects using output-only vibration data. The proposed method can be very effective for automated monitoring of buildings to develop proactive maintenance strategies. In this method, Auto-Regressive Moving Average models with eXogenous inputs (ARMAX) are built to represent the dynamic relationship of different sensor clusters. The damage features are extracted based on the relative difference of the ARMAX model coefficients to identify the existence, location and severity of damage of stiffness and mass separately. The results from a laboratory-scale shear type structure show that different damage scenarios are revealed successfully using the approach. At the end of this paper, the methodology limitations are also discussed, especially when simultaneous occurrence of mass and stiffness damage at multiple locations.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1146-1156
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• 2021

Single-unit probabilistic safety assessment (SUPSA) has complex Boolean logic equations for accident sequences. Multi-unit probabilistic safety assessment (MUPSA) model is developed by revising and combining SUPSA models in order to reflect plant state combinations (PSCs). These PSCs represent combinations of core damage and non-core damage states of nuclear power plants (NPPs). Since all these Boolean logic equations have complemented gates (not gates), it is not easy to generate exact Boolean solutions. Delete-term approximation method (DTAM) has been widely applied for generating approximate minimal cut sets (MCSs) from the complex Boolean logic equations with complemented gates. By applying DTAM, approximate conditional core damage probability (CCDP) has been calculated in SUPSA and MUPSA. It was found that CCDP calculated by DTAM was overestimated when complemented gates have non-rare events. Especially, the CCDP overestimation drastically increases if seismic SUPSA or MUPSA has complemented gates with many non-rare events. The objective of this study is to suggest a new quantification method named probability subtraction method (PSM) that replaces DTAM. The PSM calculates accurate CCDP even when SUPSA or MUPSA has complemented gates with many non-rare events. In this paper, the PSM is explained, and the accuracy of the PSM is validated by its applications to a few MUPSAs.

• 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.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.5
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• pp.295-305
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• 2010

Although the rubble mound breakwaters in Korea have been damaged by typhoons almost every year, quantification of erosion of armor block have seldomly been made. In this paper, the damage of armor units is standardized by the relative damage. In the case where the number of damaged units is reported, it is divided by the total number of units to calculate the relative damage. In the case where the rehabilitation cost is reported, the relative damage is calculated by using its relationship with the present value of the past rehabilitation cost. The relative damage is shown to have strong correlations with the typhoon parameters such as nearest central air pressure and maximum wind speed at each site. On the other hand, the existing numerical methods for calculating the cumulative damage are compared with hydraulic model tests. The method of Melby and Kobayashi (1998) is shown to give a reasonable result, and it is used to calculate the relative damage, which is compared with the measured damage. A good agreement is shown for the East Breakwater of Yeosu Harbor, while poor agreement is shown for other breakwaters. The poor agreement may be because waves of larger height than the design height occurred due to strong typhoons associated with climate change so that the relative damage increased during the last several decades.