• Smart Structures and Systems
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• v.18 no.6
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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.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.198-207
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• 2013

This study was to examine the effects of risk characteristic and risk perception on risk severity of natural disaster. The findings showed that the risk severity of natural disaster were classified into geographical disaster, storm and flood damage, drought damage. Typhoon among storm and flood damage showed high scores on risk severity of natural disaster. Moreover participants showed high scores on unfamiliar, undiscoverable, and unknown by scientific knowledge among risk characteristic of natural disaster. Second, risk characteristic was significantly correlated to risk perception. Third, risk characteristic influenced positively on risk severity of natural disaster. Especially, risk characteristic had great effect on storm and flood damage among natural disaster. Fourth, risk perception influenced positively on risk severity of natural disaster. Especially, risk perception had great effect on storm and flood damage among natural disaster.

• Fire Science and Engineering
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• v.33 no.2
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• pp.139-144
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• 2019

The increase in the elderly population also increased the damage and deaths of the elderly drivers. However, studies on the severity and severity of the elderly driver are not actively conducted and the factors are unknown. In this study, I tried to find out the factors affecting the damage and severity of the elderly driver in the frontal collision and to utilize them additionally in the severity classification. Collision Deformation Classification (CDC) Code was used to check the extent of damage to the vehicle. Abbreviated Injury Scale (AIS) was used to determine the injury parts and severity of injury, and the Injury Severity Score (ISS) to confirm the severity of the patient. The odds ratios of severe injury patients were found to be 7.381 in the subjects with 5 or more deformation extent and the ${\beta}$ value of the deformation extent was 0.453 in the analysis of the severity by linear regression analysis. Therefore, the degree of deformation extent of 5 or more can be suggested as a criterion that can be used additionally to the severity classification in the elderly driver.

• Steel and Composite Structures
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• v.14 no.4
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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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• v.75 no.5
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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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• v.25 no.3
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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.

• Anxiety and mood
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• v.8 no.1
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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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• v.3 no.3
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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.

• Journal of Ocean Engineering and Technology
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• v.8 no.1
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• pp.144-153
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• 1994

An algorithm to locate and estimate severity of damage in jacket-type offshore structures for which modal responses are availabit' for very few vibrational modes is presented. First, a theory of damage locaization and severity estimation(which yields information on the location and severity of damage directly from changes in mode shapes) is formulated. Next, the feasibility the damage detection algorithm is demonstrated by using a numerical example of an offshore jacket platform for which only three vibration modes are measured. Form the material presented here, two major results are observed. First, all damage locations in the offshore jacket platform are correctly predicted. Next, predicted damage is relatively correctly estimated.

• Smart Structures and Systems
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• v.21 no.2
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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.