• Smart Structures and Systems
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• v.14 no.2
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• Journal of Ocean Engineering and Technology
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• v.21 no.2 s.75
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• pp.35-41
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• 2007

In general, coastal damage is mostly occurred by the action of complex factors, like severe water waves. If the maximum storm surge height combines with high tide, severe water waves will overflow coastal structures. Consequently, it can be the cause of lost lives and severe property damage. In this study, using the numerical model, the storm surge was simulated to examine its fluctuation characteristics at the coast in front of Noksan industrial complex, Korea. Moreover, the shallow water wave is estimated by applying wind field, design water level considering storm surge height for typhoon Maemi to SWAN model. Under the condition of shallow water wave, obtained by the SWAN model, the wave overtopping rate for the dike in front of Noksan industrial complex is calculated a hydraulic model test. Finally, based on the calculated wave-overtopping rate, the inundation regime for Noksan industrial complex was predicted. And, numerically predicted inundation regimes and depths are compared with results in a field survey, and the results agree fairly well. Therefore, the inundation modelthis study is a useful tool for predicting inundation regime, due to the coastal flood of severe water wave.

• Biomedical Science Letters
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• v.12 no.1
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• pp.49-52
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• 2006

To elucidate the effect of antioxidant complex containing $\beta-carotene$, vitamin E, vitamin C, Ginkgo Biloba leaf extract and selenium on oxygen :tree radical production and detoxification system, rats were fed normal diet and normal diet with antioxidant complex 0.1%, 0.3% and 0.5% for 3 weeks. Feed efficiency ratio, changes in body weight, weight gain and amounts of feces of rat are similar in four groups. Liver weight per body weight and hepatic lipid peroxide weight increased in 0.5% group. However, hepatic glutathione contents in all antioxidant complex added groups were significantly increased compare with normal control group. On the other hand, the activity of xanthine oxidase was a little increased due to the amounts of antioxidant complex. Superoxide dismutase and gutathione peroxidase activity of 0.1% antioxidant complex added group were increased about $10{\sim}20%$ in comparison to normal control group. These results suggest that the supplementation of antioxidant complex 0.1% to basal diet may reduce the hepatic damage caused by free radicals.

• Smart Structures and Systems
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• v.11 no.1
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• pp.69-86
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• 2013

An efficient methodology using static test data and changes in natural frequencies is proposed to identify the damages in structural systems. The methodology consists of two main stages. In the first stage, the Damage Signal Match (DSM) technique is employed to quickly identify the most potentially damaged elements so as to reduce the number of the solution space (solution parameters). In the second stage, a particle swarm optimization (PSO) approach is presented to accurately determine the actual damage extents using the first stage results. One numerical case study by using a planar truss and one experimental case study by using a full-scale steel truss structure are used to verify the proposed hybrid method. The identification results show that the proposed methodology can identify the location and severity of damage with a reasonable level of accuracy, even when practical considerations limit the number of measurements to only a few for a complex structure.

• Computers and Concrete
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• v.16 no.5
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• pp.683-701
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• 2015

When approached using nonlinear finite element (FE) techniques, structural analyses generate, for real RC structures, large complex numerical problems. Damage is a major part of concrete behavior, and the discretization technique is critical to limiting the size of the problem. Based on previous work, the ${\mu}$ damage model has been designed to activate the various damage effects correlated with monotonic and cyclic loading, including unilateral effects. Assumptions are formulated to simplify constitutive relationships while still allowing for a correct description of the main nonlinear effects. After presenting classical 2D finite element applications on structural elements, an enhanced simplified FE description including a damage description and based on the use of multi-fiber beam elements is provided. Improvements to this description are introduced both to prevent dependency on mesh size as damage evolves and to take into account specific phenomena (permanent strains and damping, steel-concrete debonding). Applications on RC structures subjected to cyclic loads are discussed, and results lead to justifying the various concepts and assumptions explained.

• Steel and Composite Structures
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• v.10 no.4
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• pp.331-348
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• 2010

Ample research effort has been oriented into developing damage indices with the aim of estimating in a reasonable manner the consequences, in terms of structural damage and deterioration, of severe plastic cycling. Although several studies have been devoted to calibrate damage indices for steel and reinforced concrete members; currently, there is a challenge to study and calibrate the use of such indices for the practical evaluation of complex structures. The aim of this paper is to introduce an energy-based damage index for multi-degree-of-freedom steel buildings that accounts explicitly for the effects of cumulative plastic deformation demands. The model has been developed by complementing the results obtained from experimental testing of steel members with those derived from analytical studies regarding the distribution of plastic demands on several steel frames designed according to the Mexico City Building Code. It is concluded that the approach discussed herein is a promising tool for practical structural evaluation of framed structures subjected to large energy demands.

• Journal of KIBIM
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• v.9 no.1
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• pp.42-51
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• 2019

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.

• Smart Structures and Systems
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• v.5 no.3
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• pp.291-316
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• 2009

Damage detection using structural classical deflection flexibility has received considerable attention due to the unique features of the flexibility in the last two decades. However, for relatively complex structures, most methods based on classical deflection flexibility fail to locate damage sites to the exact members. In this study, for structures whose members are dominated by axial forces, such as truss structures, a more feasible flexibility for damage detection is proposed, which is called the Axial Strain (AS) flexibility. It is synthesized from measured modal frequencies and axial strain mode shapes which are expressed in terms of translational mode shapes. A damage indicator based on AS flexibility is proposed. In addition, how to integrate the AS flexibility into the Damage Location Vector (DLV) approach (Bernal and Gunes 2004) to improve its performance of damage localization is presented. The methods based on AS flexbility localize multiple damages to the exact members and they are suitable for the cases where the baseline data of the intact structure is not available. The proposed methods are demonstrated by numerical simulations of a 14-bay planar truss and a five-story steel frame and experiments on a five-story steel frame.

• Preventive Nutrition and Food Science
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• v.7 no.3
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• pp.323-326
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• 2002

Ionizing radiation can be used to sanitize herbs contaminated by various microorganisms. However, health concerns related to irradiation damage to complex molecules in plants necessitate that methods be developed to monitor such damage. To elucidate DNA damage of herbs caused by irradiation, the DNA comet assay was used for Astragalus membranaceus Bunge and Havenia dulcis Thumb, irradiated at 1, 5, 7, and 10 kGy. With increasing irradiation doses, the tails of comets became longer with average tail length increasing from 17 (non-irradiated) to 124 (10 kGy) $\mu$m in Astragalus membranaceus Bunge. Above 7 kGy, some of the tails were separated from the heads of comets. Distribution patterns of the tail length of In comets selected randomly in the irradiated herbs were analyzed to quantify the DNA damage. These results clearly suggest that the DNA comet assay is an effective and inexpensive tool for the detection of irradiation damage to DNA in herbs.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.6
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• pp.258-269
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• 1999

It is important to evaluate fatigue damage of in-service material in respect to assure safety and remaining fatigue life in structure and mechanical components under cyclic load . Fatigue damage is represented by mathematical modelling with crack growth rate da/dN and cycle ration N/Nf and is detected by X-ray diffraction and ultrasonic wave method etc. But this is estimated generally by single parameter but influenced by many test conditions The characteristics of it indicates fatigue damage has complex fracture mechanism. Therefore, in this study we propose that back-propagation neural networks on the basis of ration of X-ray half-value breath B/Bo, fractal dimension Df and fracture mechanical parameters can construct artificial intelligent networks estimating crack growth rate da/dN and cycle ratio N/Nf without regard to stress amplitude Δ $\sigma$.