• Structural Engineering and Mechanics
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• v.54 no.1
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• pp.105-119
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• 2015

Structural damage detection using modal strain energy (MSE) is one of the most efficient and reliable structural health monitoring techniques. However, some of the existing MSE methods have been validated for special types of structures such as beams or steel truss bridges which demands improving the available methods. The purpose of this study is to improve an efficient modal strain energy method to detect and quantify the damage in complex structures at early stage of formation. In this paper, a modal strain energy method was mathematically developed and then numerically applied to a fixed-end beam and a three-story frame including single and multiple damage scenarios in absence and presence of up to five per cent noise. For each damage scenario, all mode shapes and natural frequencies of intact structures and the first five mode shapes of assumed damaged structures were obtained using STRAND7. The derived mode shapes of each intact and damaged structure at any damage scenario were then separately used in the improved formulation using MATLAB to detect the location and quantify the severity of damage as compared to those obtained from previous method. It was found that the improved method is more accurate, efficient and convergent than its predecessors. The outcomes of this study can be safely and inexpensively used for structural health monitoring to minimize the loss of lives and property by identifying the unforeseen structural damages.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.231-242
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• 2018

Subway tunnel structure has been rapidly developed in many cities for its strong transport capacity. The model-based damage detection of subway tunnel structure is usually difficult due to the complex modeling of soil-structure interaction, the indetermination of boundary and so on. This paper proposes a new data-based method for the damage detection of subway tunnel structure. The root mean square acceleration and cross correlation function are used to derive a statistical pattern recognition algorithm for damage detection. A damage sensitive feature is proposed based on the root mean square deviations of the cross correlation functions. X-bar control charts are utilized to monitor the variation of the damage sensitive features before and after damage. The proposed algorithm is validated by the experiment of a full-scale two-rings subway tunnel lining, and damages are simulated by loosening the connection bolts of the rings. The results verify that root mean square deviation is sensitive to bolt loosening in the tunnel lining and X-bar control charts are feasible to be used in damage detection. The proposed data-based damage detection method is applicable to the online structural health monitoring system of subway tunnel lining.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of Korean Society for Atmospheric Environment
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• v.2 no.3
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• pp.19-26
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• 1986

The degree of air pollution is usually presented in terms of concentration. But, in evaluating the environmental impacts or air pollution control policies, the concentration values need to be interpreted in terms of damage effects on property and human health. The damage effect varies with the types of pollution, subjects and land use pattern of an affected area. Therefore, this study is aimed at developing a method of analyzing effects of various types of air pollutions on surrounding environmental setting with the EIMS (Environmental Information Management System) developed for land suitability analysis. Using the method formulated in this study, the long- term effects of such pollutants as $SO_2$ and HF on types of vegetation and residents, and potential, short-term effects of HCl leak accidents from manufacturing facilities in Ulsan and Onsan Industrial complex are analyzed. The presentation of the damage effects of air pollution rather than the concentration of pollutants will be useful for the preparation of environmental impact statements, the formulation of environmental policies, and the development of land use plans in heavily industrialized areas.

• Environmental Mutagens and Carcinogens
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• v.25 no.2
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• pp.71-75
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• 2005

Green tea and their major constituents such as catechins are famous materials for their anti-oxidative and anti-carcinogenic activity, but many compounds with reducing power can promote the oxidation in their oxidized form or in the presence of metal ion. We investigated the pro-oxidative effect of the ethanol extract equivalent up to 30mg of dried weight of green tea leaves in four in vitro systems which could be used for detecting DNA damage. Although ethanol extract of green tea did not show significant mutagenicity in Salmonella typhimurium TA102, which is sensitive strain to oxidative stress, it degraded deoxyribose extensively in the presence of $FeCl_3-EDTA$ complex, promoted 8-oxoguanine formation in the live bacteria cell, Salmonella typhimurium TAI04, and cleaved super coiled DNA strand with the help of copper ion. It suggested that green tea, famous anti-oxidative material, can be pro-oxidant according to the condition of extraction or metal existence.

• Smart Structures and Systems
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• v.22 no.2
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• pp.193-200
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• 2018

From the viewpoint of engineering applications, the prediction of the failure of bogies plays an important role in preventing the occurrence of fatigue. Fatigue is a complex phenomenon affected by many uncertainties (such as load, environment, geometrical and material properties, and so on). The key to predict fatigue damage accurately is how to quantify these uncertainties. A Bayesian model is used to account for the uncertainty of various sources when predicting fatigue damage of structural components. In spite of improvements in the design of fatigue-sensitive structures, periodic non-destructive inspections are required for components. With the help of modern nondestructive inspection techniques, the fatigue flaws can be detected for bogie structures, and fatigue reliability can be updated by using Bayesian theorem with inspection data. A practical fatigue analysis of welded bogies is utilized to testify the effectiveness of the proposed methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.203-208
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• 2018

In this paper, we selected power and power distribution facilities corresponding to urban infrastructure from the types of damage that could be caused by earthquakes and studied how they were calculated to damage. To calculate the damage, a graph of the magnitude of the damage was produced by applying the vulnerability curve calculation formula, which can be calculated for each type and type of facility. The scale of the earthquake and the probability of the occurrence of damage by the maximum earthquake acceleration were shown in the form of a vulnerability rate when the earthquake occurred in the urban infrastructure facility for utilizing the calculation result. It also applied a method of quantifying the fragility, which is a method of converting the calculated fragility into an integrated form, to represent a constant value for the magnitude of the damage. Continuing research, such as the method applied in this paper, could help identify in advance the types of structures affected by an earthquake and respond to reducing damage.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• The Korean Journal of Pain
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• v.29 no.4
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• pp.266-269
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• 2016

The superficial peroneal nerve is vulnerable to damage from ankle sprain injuries and fractures as well as surgery to this region. And it is also one of the most commonly involved nerves in complex regional pain syndrome type II in the foot and ankle region. We report two cases of ultrasound-guided pulsed radiofrequency treatment of superficial peroneal nerve for reduction of allodynia in CRPS patients.

• Korean Journal of Veterinary Service
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• v.39 no.3
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• pp.137-140
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• 2016

The correct and quick diagnosis can be minimized damage from honeybee diseases. This study was carried out to detect infectious pathogens in honeybee in Jeonbuk province. 183 samples were collected from 8 area of Jeonbuk beekeeping farms in 2015 and 10 of infectious pathogens were examined through PCR and RT-PCR. Among 183 samples, positive rates of each disease were as follows; BQCV 43.7%, SBV 24.6%, DWV 16.4%, SB 15.8%, CB 10.4%, Nosemosis 7.1%, AFB 6.6%, EFB 1.1%, CBPV 1.1%, ABPV 0.0%. Among 28 beekeeping farms, 19 farms (67.9%) were infected with a complex of two or more diseases. The highest frequency of complex infections was BQCV.