• Smart Structures and Systems
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• 제18권6호
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• 한국콘텐츠학회논문지
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• 제13권4호
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• pp.198-207
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• 2013

본 연구는 자연재해에 대한 공중의 위험특성과 위험인식이 위험 심각성에 어떠한 영향을 미치는지를 규명하고자 하였다. 연구결과는 다음과 같다. 첫째, 자연재해 위험평가 차원에서 일반 공중이 인식하는 자연재해의 심각성을 분류한 결과, 지변재해, 풍수해, 한해로 분류되었고, 자연재해 중 태풍을 가장 심각한 자연재해로 인식하였다. 자연재해 대한 위험특성은 '친근하지 않은', '과학에 의해 알려지지 않은', '발견할 수 없는' 등이 비교적 높은 평균을 보여 자연재해를 미지의 위험영역으로 인식하는 경향이 강하였다. 둘째, 공중이 인식하는 자연재해 위험특성과 위험인식 간의 상관관계를 살펴본 결과, 유의한 상관이 있는 것으로 나타났다. 셋째, 자연재해에 대한 위험특성이 위험평가 차원의 심각성에 미치는 영향을 살펴본 결과, 위험특성은 위험평가 차원의 심각성에 정적 영향을 미쳤고, 자연재해 중에서도 호우나 태풍, 홍수와 같은 풍수해를 지변재해나 한해보다 심각한 것으로 인식하였다. 넷째, 공중의 자연재해에 대한 위험인식이 위험평가 차원의 심각성에 미치는 영향을 살펴본 결과, 자연재해에 대한 위험인식은 자연재해의 심각성에 정적 영향을 미쳤고, 자연재해 심각성 중에서도 풍수해를 가장 심각하게 인식하는 것으로 나타났다.

• 한국화재소방학회논문지
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• 제33권2호
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• pp.139-144
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• 2019

노인인구의 증가로 노인운전자의 손상과 사망자도 증가하였다. 하지만 노인운전자의 손상과 중증도에 대한 연구는 활발히 이루어지지 않아 영향 요인을 파악하지 못하고 있다. 본 연구에서는 정면충돌에서의 노인운전자에 손상과 중증도에 영향을 미치는 요인을 찾아 중증도 분류에 추가적으로 활용하고자 하였다. Collision Deformation Classification Code를 통해 차량 파손 정도를 확인하였으며 간편손상척도(Abbreviated Injury Scale, AIS)로 손상부위와 정도를, 손상중증도점수(Injury Severity Score, ISS)로 환자의 중증도를 확인하였다. 중증외상환자의 발생률은 5이상의 차량 파손 정도를 가진 대상자에서 Odds ratio가 7.381로 나타났으며 선형회귀분석을 통한 중증도 요인 분석에서도 차량 파손 정도의 ${\beta}$값이 0.453으로 나타났다. 따라서 5이상의 차량 파손 정도는 노인운전자에서 중증도 분류에 추가적으로 활용될 수 있는 기준으로 제안될 수 있다.

• Steel and Composite Structures
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• 제14권4호
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Structural Engineering and Mechanics
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• 제75권5호
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• pp.585-594
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• 2020

Detecting the damage of indeterminate trusses is of major importance in the literature. This paper proposes a quick approach in this regard, utilizing a precise mathematical approach based on Finite Element Method. Different to a general two-step method defined in the literature essentially based on optimization approach, this method consists of three steps including Damage-Suspected Element Identification step, Imminent Damaged Element Identification step, and finally, Damage Severity Detection step and does not need any optimizing algorithm. The first step focuses on the identification of damage-suspected elements using an index based on modal residual force vector. In the second step, imminent damage elements are identified among the damage-suspected elements detected in the previous step using a specific technique. Ultimately, in the third step, a novel relation is derived to calculate the damage severity of each imminent damaged element. To show the efficiency and quick function of the proposed method, three examples including a 25-bar planar truss, a 31-bar planar truss, and a 52-bar space truss are studied; results of which indicate that the method is innovatively capable of suitably detecting, for indeterminate trusses, not only damaged elements but also their individual damage severity by carrying out solely one analysis.

• Smart Structures and Systems
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• 제25권3호
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• pp.345-368
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• 2020

The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

• 대한불안의학회지
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• 제8권1호
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• pp.31-40
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• 2012

Objective : The purpose of this study was to analyze the correlation between symptom severity and neurocognitive factors in traumatic head injury patients. In addition, the effect of frontal lobe damage on these parameters was examined. Methods : We selected 18 patients who had brain damage for the moderate to severe traumatic brain injury (MSTBI) group, and 17 patients who met the diagnostic criteria for post-traumatic stress disorder (PTSD) without the finding of brain damage for the comparison group. For the evaluation of neurocognitive function, K-WAIS, Rey-Kim Memory Test, K-FENT, WCST, and MMPI-2 were used. Results : The results of the comparison (using the malingering scale) revealed that the values of PDS and PK, which express the severity of symptoms, and the values of the validity scale F, F (B), and F (P) were significantly higher in the overly-expressed group. F (B) in overly-expressed group and PK, Pt, and Sc in the properly-expressed group had significant correlation with the severity of symptoms. F (B), S, and Stroop error inhibition in PTSD, and PK, Pt, Sc, and MQ in MSTBI had significant correlation with the severity of symptoms. The results of the comparison based on the finding of frontal lobe damage revealed that PDS, EIQ, and MQ ware significantly higher in the group without brain damage. Conclusions : It was revealed that each neurocognitive factor was correlated with the severity of symptoms. There was a decrease in complaints or symptoms reported by the frontal lobe injury group, and this is believed to be due to degenerative change in the personality and emotional functioning of these patients following frontal lobe damage.

• Journal of Ship and Ocean Technology
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• 제3권3호
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• pp.1-12
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• 1999

Spectral fatigue damage calculations has been performed on four ships in order to assess the effect that the probabilistic modeling of sea states has on the estimated fatigue life. The damage estimation method is based on the Miner- Palmgren fatigue damage formulation and a spectral approach is used to determine the necessary variances of the stress processes. Both the horizontal and vertical hull girder bending induced stress process together with the local water pressure induced stress process is taken into account. The wave scatter diagrams are applied in the calculations and their fatigue severity is assessed by analyzing the results obtained with the ten scatter diagrams and the four ships. All four ships are analyzed both in full load and ballast conditions and while traveling at both full and reduced speed. It is found that the fatigue severity of a wave scatter diagram is dependent on several parameters, some of these being the extreme wave hight extrapolated from the scatter diagram and the mean zero up-crossing period in conjunction with the ship length . Based on these three parameters and expression is derived in order to calculate one single number describing the fatigue severity of a scatter diagram with respect to a certain ship.

• 한국해양공학회지
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• 제8권1호
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• pp.144-153
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• 1994

본 연구에서는, 소수의 모드형상의 진동반응만이 측정된 자켓형 해양구조물에 존재하는 손상의 위치와 그 크기를 결정할 수 있는 알고리듬이 제시된다. 먼저, 모드형상의 변화로 부터 직접 손상위치와 크기를 결정하는 이론이 제시된다. 다음으로, 세개의 진동모드형상이 측정된 자켓형 해양구조물의 수치예를 이용하여 알고리듬의 적합성이 예증된다. 본 연구의 결과는 다음과 같다. 첫째로, 자켓형 해양구조물에 존재하는 손상의 위치가 정확하게 예측 되었다. 둘째로, 예측된 손상의 크기가 비교적 정확하게 예측되었다.

• Smart Structures and Systems
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• 제21권2호
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• pp.235-248
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• 2018

An efficient method utilizing the multi-stage improved differential evolution algorithm (MSIDEA) as an optimization solver is presented here to detect the multiple-damage of structural systems. Natural frequency changes of a structure are considered as a criterion for damage occurrence. The structural damage detection problem is first transmuted into a standard optimization problem dealing with continuous variables, and then the MSIDEA is utilized to solve the optimization problem for finding the site and severity of structural damage. In order to assess the performance of the proposed method for damage identification, an experimental study and two numerical examples with considering measurement noise are considered. All the results demonstrate the effectiveness of the proposed method for accurately determining the site and severity of multiple-damage. Also, the performance of the MSIDEA for damage detection compared to the standard differential evolution algorithm (DEA) is confirmed by test examples.