• Smart Structures and Systems
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• 제25권5호
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Nuclear Engineering and Technology
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• 제35권5호
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• pp.497-505
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• 2003

This paper describes a computational approach to the quantification of primary damage under irradiation and demonstrates the effect of neutron energy spectra on the formation of the displacement cascade. The development of displacement cascades in ${\alpha}-Iron$ has been simulated using the MOLDY code - a molecular dynamics code for simulating radiation damage. The primary knock-on atom energy, key input to the MOLDY code, was determined from the SPECTER code calculation on two neutron spectra. The two neutron spectra include; (i) neutron spectrum in the instrumented irradiation capsule of the high-flux advanced neutron application reactor (HANARO), and (ii) neutron spectrum at the inner surface of the reactor pressure vessel steel for the Younggwang nuclear power plant No.5 (YG 5). Minor differences in the normalized neutron spectra between the two spectra produce similar values of PKA energy, which are 4.7 keV for HANARO and 5.3 keV for YG 5. This similarity implies that primary damage to the components of the commercial nuclear reactors should be well simulated by irradiation in the HANARO. Moreover, the application of the MD calculations corroborates this statement by comparing cascades simulation results.

• Structural Engineering and Mechanics
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• 제58권6호
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• pp.1021-1044
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• 2016

Different versions of a damage index (DI) along with a formulation to find the number of cycles at failure due to fatigue, applicable to reinforced concrete (RC) structures are presented. These are based on an energetic analysis method and applicable to both global and local levels. The required data can be found either from the numerical simulation of structures or from the experimental tests. A computer program has been developed to simulate numerically the nonlinear behavior of RC columns under cyclic loading. The proposed DI gives a regular distribution of structural damages up to failure and is validated by the results of the tests carried out on RC columns subjected to cyclic loading. In general, the local and global damage indices give approximately similar results, while each of them has its own advantages. The advantage of the implicit version of DI is that, it allows the comparison of the results with those of the monotonic loading case, while the explicit version makes it possible to estimate the number of loading cycles at failure due to fatigue, and the advantage of the simplified version is that; the monotonic loading data is not needed for the cyclic loading case.

• Smart Structures and Systems
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• 제15권3호
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• pp.577-592
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• 2015

Damage quantification is a major goal of the SHM community. Methodologies to introduce a quantity for actual condition of a structure into the assessment process are desired. The idea that the condition of a structure is represented in the character of its dynamic response is fully accepted by the SHM community. The VCLIFE methodology quantifies condition analyzing input from monitoring.

• 대한교통학회지
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• 제10권3호
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• pp.59-74
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• 1992

The change of industrial structure, the expansion of economic scale, the elevation of national life level and rapid motorization bring about social problems that are traffic accident, traffic congestion, etc. Especially, big traffic accidents bring about the great number of dead and wounded person with damage of property. But there are no study results available mainly focused on the big traffic accident analysis. Accordingly, this study is essentially for the factor analysis of big accident types including laws violation and the assaulting driver's death using Quantification ll method.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1998년도 춘계학술발표회논문집(1)
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• pp.899-905
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• 1998

The analysis priority makings the recommendation to reduce the total core damage frequency (CDF) of Wolsong nuclear Power Plant nits 2/3/4 was Performed in this paper. In order to derive the recommendation, the sensitivity analysis of CDF on which major contributors effect m performed based on the accident quantification results during Level 1 Probabilistic safety assessment (PSA). Priorities were ranked in tile way that compares the CDF reduction rate with efforts required to implement those recommendations using risk matrix

• 한국안전학회지
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• 제8권2호
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• pp.58-63
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• 1993

Recently, a large amount of flammable or explosive materials have been handled or stored in chemical industries. H the equipments fail or the materials release in consequence of operation errors, fire and explosion could occur to them. Thus, risk assessment using quantification of risks is very important when design of processes and modifications of installed processes are performed. The purpose of this study is to develop the program for fire and explosion index in order to quantify the expected damage of fire and explosion incidents in chemical plants, to identify equipment that would be likely to contribute to the creation or escalation of an incident, to comunicate the potential fire and explosion risk to management and to account of damage cost.

• Earthquakes and Structures
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• 제14권1호
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• pp.73-84
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• 2018

Low cyclic loading tests are conducted on the steel reinforced recycled concrete (SRRC) column-steel (S) beam composite frame joints. This research aims to evaluate the earthquake damage performance of composite frame joints by performing cyclic loading tests on eight specimens. The experimental failure process and failure modes, load-displacement hysteresis curves, characteristic loads and displacements, and ductility of the composite frame joints are presented and analyzed, which shows that the composite frame joints demonstrate good seismic performance. On the basis of this finding, seismic damage performance is examined by using the maximum displacement, energy absorbed in the hysteresis loops and Park-Ang model. However, the result of this analysis is inconsistent with the test failure process. Therefore, this paper proposes a modified Park-Ang seismic damage model that is based on maximum deformation and cumulative energy dissipation, and corrected by combination coefficient ${\alpha}$. Meanwhile, the effects of recycled coarse aggregate (RCA) replacement percentage and axial compression ratio on the seismic damage performance are analyzed comprehensively. Moreover, lateral displacement angle is used as the quantification index of the seismic performance level of joints. Considering the experimental study, the seismic performance level of composite frame joints is divided into five classes of normal use, temporary use, repair after use, life safety and collapse prevention. On this basis, the corresponding relationships among seismic damage degrees, seismic performance level and quantitative index are also established in this paper. The conclusions can provide a reference for the seismic performance design of composite frame joints.

• Smart Structures and Systems
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• 제22권5호
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• 인터넷정보학회논문지
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• 제25권2호
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.