• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.24 no.3
• /
• pp.336-344
• /
• 2014

Objectives: This study was performed to assay the effect of neodymium oxide on the generation of reactive oxygen species and DNA oxidative damage by intratracheal instillation. Methods: Two groups of rats were exposed to neodymium oxide($Nd_2O_3$) via intratracheal instillation with doses of 0.5 mg and 2.0 mg, respectively. At two days and at 12 weeks after exposure, the contents of neodymium oxide in the lung, liver, kidney, heart and brain, leukocyte, olive tail moment, ROS, RNS, lactate dehydrogenase, albumin, cytokine and MDA from BALF were measured. Results: Neodymium oxide contents in the liver, kidney, heart, and brain were detected at less than $1{\mu}g/g$ tissue concentration. However, in the lungs at four weeks the highest amount were detected and then found to be drastically reduced at 12 weeks. ROS and RNS in bronchoalveolar lavage increased in concentration dependently at two days, four weeks and 12 weeks after neodymium oxide instillation. However, ROS and RNS decreased with the passage of time. At two days the total number of WBC in BALF in the high concentration group was significantly increased, and at four weeks the total number of WBC were significantly increased in the low and high concentration groups(p<0.01). At two days after exposure, the LDH of the low and high concentration groups was significantly increased. At 12 weeks, only the LDH of the high concentration group was significantly increased compared to in the control group(p<0.01). As a result of Comet assay, after two days, damage to the DNA of the low and high concentration groups was observed. Conclusions: Intratracheal instillation of neodymium oxide induces the generation of ROS and DNA damage in rats.

• Smart Structures and Systems
• /
• v.9 no.3
• /
• pp.253-271
• /
• 2012

Finite element (FE) model updating is a useful tool for global damage detection technique, which identifies the damage of the structure using measured vibration data. This paper presents the application of a finite element model updating method to detect the damage of a small-scale reinforced concrete building structure using measured acceleration data from shaking table tests. An iterative FE model updating strategy using the least-squares solution based on sensitivity of frequency response functions and natural frequencies was provided. In addition, a side constraint to mitigate numerical difficulties associated with ill-conditioning was described. The test structure was subjected to six El Centro 1942 ground motion histories with different Peak Ground Accelerations (PGA) ranging from 0.06 g to 0.5 g, and analytical models corresponding to each stage of the shaking were obtained using the model updating method. Flexural stiffness values of the structural members were chosen as the updating parameters. In model updating at each stage of shaking, the initial values of the parameter were set to those obtained from the previous stage. Severity of damage at each stage of shaking was determined from the change of the updated stiffness values. Results indicated that larger reductions in stiffness values occurred at the slab members than at the wall members, and this was consistent with the observed damage pattern of the test structure.

• Structural Engineering and Mechanics
• /
• v.63 no.6
• /
• pp.779-788
• /
• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Structural Monitoring and Maintenance
• /
• v.7 no.2
• /
• pp.125-147
• /
• 2020

Damage detection of structures is one of the most important topics in structural health monitoring. In practice, the response is not available at all structural degrees of freedom, and due to the installation of sensors at some degrees of freedom, responses exist only in limited number of degrees of freedom. This paper is investigated the damage detection of structures by applying two approaches, AllDOF and Dynamic Condensation Method (DCM), based on the Modified Modal Strain Energy Method (MMSEBI). In the AllDOF method, mode shapes in all degrees of freedom is available, but in the DCM the mode shapes only in some degrees of freedom are available. Therefore by methods like the DCM, mode shapes are obtained in slave degrees of freedom. So, in the first step, the responses at slave degrees of freedom extracted using the responses at master degrees of freedom. Then, using the reconstructed mode shape and obtaining the modified modal strain energy, the damages are detected. Two standard examples are used in different damage cases to evaluate the accuracy of the mentioned method. The results showed the capability of the DCM is acceptable for low mode shapes to detect the damage in structures. By increasing the number of modes, the AllDOF method identifies the locations of the damage more accurately.

• Structural Engineering and Mechanics
• /
• v.17 no.3_4
• /
• pp.331-346
• /
• 2004

A new sensor system is proposed for measuring damage indexes. The damage index is a physical value that is well correlated to a critical damage in a device or a structure. The mechanism proposed here utilizes elastic buckling of a thin wire and does not require any external power supply for memorizing the index. The mechanisms to detect peak strain, peak displacement, peak acceleration and cumulative deformation as examples of damage indexes are presented. Furthermore, passive and active wireless data retrieval mechanisms using electromagnetic induction are proposed. The passive wireless system is achieved by forming a closed LC circuit to oscillate at its natural frequency. The active wireless sensor can transmit the data much further than the passive system at the sacrifice of slightly complicated electric circuit for the sensor. For wireless data retrieval, no wire is needed for the sensor to supply electrical power. For the active system, electrical power is supplied to the sensor by radio waves emitted from the retrieval system. Thus, external power supply is only needed for the retrieval system when the retrieval becomes necessary. Theoretical and experimental studies to show excellent performance of the proposed sensor are presented. Finally, a prototype damage index sensor installed into a 7 storey base-isolated building is explained.

• Journal of Nutrition and Health
• /
• v.37 no.6
• /
• pp.440-447
• /
• 2004

In this study the in vitro protective effects of several antioxidant vitamins (vitamin C, $\alpha$-tocopherol, $\beta$-carotene), fruits and vegetables (strawberry, tangerine, orange and 100％ orange juice, carrot juice), on the levels of isolated human lymphocyte DNA damage was measured using Comet assay. Comet assay has been used widely to assess the level of the DNA damage in the individual cells. Lymphocytes were pre-treated for 30 minutes with antioxidant vitamins (10, 50, 100, 500 $\mu$M) or fruits$.$vegetables (10, 100, 500, 1000 $\mu$g/ml), an4 then oxidatively challenged with 100 $\mu$M $H_2O$$_2$ for 5 min at 4$^{\circ}C$. The protective effect of antioxidant vitamins against DNA damage at a concentration of 50 $\mu$M were 50％ in vitamin C, 32％ in $\alpha$-tocopherol, whereas, fJ-carotene showed a 55％ protection at a dose as low as 10 $\mu$M. The inhibitory effects of DNA damage by strawberry, tangerine, orange, orange juices, carrot juices were 50 - 60％ with wide ranges of doses. The results of the present study indicate that most the antioxidant vitamins and fruits$.$vegetables juices produced a significant reduction in oxidative DNA damage.

• Smart Structures and Systems
• /
• v.13 no.5
• /
• pp.869-890
• /
• 2014

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.

• Smart Structures and Systems
• /
• v.14 no.2
• /
• pp.105-128
• /
• 2014

The stochastic damage locating vector (SDLV) method has been studied extensively in recent years because of its potential to determine the location of damage in structures without the need for measuring the input excitation. The SDLV method has been shown to be a particularly useful tool for damage localization in steel truss bridges through numerical simulation and experimental validation. However, several issues still need clarification. For example, two methods have been suggested for determining the observation matrix C identified for the structural system; yet little guidance has been provided regarding the conditions under which the respective formulations should be used. Additionally, the specific layout of the sensors to achieve effective performance with the SDLV method and the associated relationship to the specific type of truss structure have yet to be explored. Moreover, how the location of truss members influences the damage localization results should be studied. In this paper, these three issues are first investigated through numerical simulation and subsequently the main results are validated experimentally. The results of this paper provide guidance on the effective use of the SDLV method.

• Smart Structures and Systems
• /
• v.18 no.3
• /
• pp.525-540
• /
• 2016

The CFRP-confined circular concrete-filled steel tubular column is composed of concrete, steel, and CFRP. Its failure mechanics are complex. The most important difficulties are lack of an available method to establish a relationship between a specific damage mechanism and its acoustic emission (AE) characteristic parameter. In this study, AE technique was used to monitor the evolution of damage in CFRP-confined circular concrete-filled steel tubular columns. A fuzzy c-means method was developed to determine the relationship between the AE signal and failure mechanisms. Cluster analysis results indicate that the main AE sources include five types: matrix cracking, debonding, fiber fracture, steel buckling, and concrete crushing. This technology can not only totally separate five types of damage sources, but also make it easier to judge the damage evolution process. Furthermore, typical damage waveforms were analyzed through wavelet analysis based on the cluster results, and the damage modes were determined according to the frequency distribution of AE signals.

• Smart Structures and Systems
• /
• v.23 no.5
• /
• pp.507-520
• /
• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.