• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.861-872
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• 2022

The purpose of this study was to establish a complex disaster scenario that can comprehensively consider various disaster situations that may occur in the utility tunnel. Method: In order to comprehensively consider the correlation between disasters, a composite disaster scenario was derived from a combination of damage factors, respectively. A risk assessment was performed in order to derive the priorities of the scenarios. And based on the results, the priorities of complex disaster scenarios were set. Result: Based on the disaster cases in the utility tunnel, a plan was prepared for complex disaster scenarios centered on damage. A complex disaster scenario was specified using a semi-quantitative evaluation method for single and multiple disaster factors such as fire, flooding, and earthquake. Conclusion: The composite disaster scenario derived from this study can be used for the prevention and preparation of damage when the precursor symptoms of a disaster are detected. In addition, the results of this study are expected to be used as basic data for preparing strategic plans and preparing complex disaster response technologies to induce rapid response and recovery in case of emergency disasters.

• Korean Journal of Environment and Ecology
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• v.9 no.1
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• pp.1-14
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• 1995

To investigate the damage on the forest plant community by the air pollution around Daesan, Kunsan industrial complex, five sites(Daesan) and three sites(Kunsan) were set up and surveyed(Area : 500$m^2$/site). In the analysis of community structure, the dominant species in the canopy and understory layer were Pinus densiflora, P. thunbergiana. ones in the shrub layer appeared Rhododendron mucronulatum and Quercus spp. in the Daesan industrial complex. Species diversity of the plant community in Daesan industrial complex showed very severe difference between plots. Soil acidity measured 4.67~5.12. It was not found that plant community in Daesan industrial complex appeared serious damage by the air pollutant. In the analysis of community structure the dominant species in the canopy and understory layer were P. densiflora, P. thunbergiana in Kunsan industrial complex. Species diversity of the plant community in Kunsan industrial complex was prove to low as nearer from the pollutant. Soil acidity measured 4.32~4.76 and it was very strong acidity. It was found that plant community as nearer from the pollutant appeared visible damage entirely by the air pollutant in Kunsan industrial complex.

• Structural Engineering and Mechanics
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• v.16 no.5
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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.

• Earthquakes and Structures
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• v.17 no.6
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• pp.577-590
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• 2019

The seismic response of structures to strong ground motions is a complex problem that has been studied for decades. However, most of current seismic regulations do not assess the potential level of damage that a structure may undergo during a strong earthquake. This will happen in spite that the design objectives for any structural system are formulated in terms of acceptable levels of damage. In this article, we analyze the expected damage in single-degree-of-freedom systems subjected to long-duration ground motions generated in soft soil sites, such as those located in the lakebed of Mexico City. An energy-based methodology is formulated, under the consideration of input energy as the basis for the evaluation process, to estimate expected damage. The results of the proposed methodology are validated with damage curves established directly with nonlinear dynamic analyses.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.4
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• pp.386-397
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• 2016

Given the growing interest in damage control training for the naval ships and their organizations, expectations for a new concept of training program have also increased. The existing training programs and its concept focus on training crew to be more proficient and skilled so that they can respond better to damage situations, i.e., fires and flooding. This paper suggests a development procedure of damage control training scenarios using the survivability analysis results as a new concept of damage control training programs employing advanced systems such as damage control console, automation system, and kill cards. This approach could help the decision maker not only enhance his or her capability but also improve the reacting capability of crew members for complex situations induced by a weapon hit.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.173-190
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• 2019

Steel-confined reinforced concrete (SCRC) columns feature highly complex and invisible mechanisms that make damage evaluation and pattern recognition difficult. In the present article, the prevailing acoustic emission (AE) technique was applied to monitor and evaluate the damage process of steel-confined RC columns in a quasi-static test. AE energy-based indicators, such as index of damage and relax ratio, were proposed to trace the damage progress and quantitatively evaluate the damage state. The fuzzy C-means algorithm successfully discriminated the AE data of different patterns, validity analysis guaranteed cluster accuracy, and principal component analysis simplified the datasets. A detailed statistical investigation on typical AE features was conducted to relate the clustered AE signals to micro mechanisms and the observed damage patterns, and differences between steel-confined and unconfined RC columns were compared and illustrated.

• Physical Therapy Korea
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• v.12 no.1
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• pp.71-79
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• 2005

The purpose of this study was to analyze the presence of ipsilesional movement deficit, with segmental performance in each proximal or distal upper extremity. The visuoperceptual complex task of the ipsilesional upper extremity was investigated in patients with unilateral brain damage and a control group of healthy sex-age-matched controls. Tracking movements were tested in the proximal and distal upper extremities. Movements were measured by the accuracy index, which was normalized to each subject's own range of motion and took into account any differences between subjects in the excursion of the tracking target. The findings revealed that stroke patients experienced difficulties with tracking movement of both proximal and distal segments in the upper extremities on the so-called "non-affected side", irrespectively of the extent of patient's age, time since onset, or severity of contralateral upper extremity. Therefore, the unilateral brain damage affected ipsilateral motor function of the proximal and distal upper limbs in the performance of complex motor tasks, requiring central processing and the higher order cognitive function in the integrity of both hemispheres.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.4
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• pp.306-313
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• 2011

The damage tolerance analysis is required to guarantee the structural safety and the reliability for aircraft components. The damage tolerance method, which evaluate the life considering the initial crack, considers a fatigue design model of the aircraft main structure. The fatigue crack growth life should be calculated in damage tolerance analysis and the inspection time to define the replacement cycle. In this paper, the damage tolerance analysis is performed for a turbine wheel which has complex geometry. The equation of the stress intensity factor for complex geometry is hard to know, so that they are usually processed by finite element analysis which takes long time. To solve this problem, the stress intensity factors at specified crack are obtained by the FEA and the crack growth life is evaluated using the surrogate model which is generated by the regression analysis of the FEA data. From the results, the efficiency of the crack growth life calculation and the damage tolerance analysis could be increased by taking the surrogate model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.278-281
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• 2011

The global coordinates generated from Isomap algorithm provide a simple way to analyze and manipulate high dimensional observations in terms of their intrinsic nonlinear degrees of freedom. Thus, Isomap can find globally meaningful coordinates and nonlinear structure of complex data sets, while neither principal component analysis (PCA) nor multidimensional scaling (MDS) are successful in many cases. It is demonstrated that the adapted Isomap algorithm successfully enhances the quality of pattern classification for damage identification in various numerical examples.