• Computational Structural Engineering : An International Journal
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• 제1권1호
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• pp.59-69
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• 2001

This study is intended to propose a systematic approach for reliability-based assessment of life cycle cost (LCC) effectiveness and economic efficiency for cost-effective seismic upgrading of existing bridges. The LCC function is expressed as the sum of the upgrading cost and all the discounted life cycle damage costs, which is formulated as a function of the Park-Ang damage index and structural damage probability. The damage costs are expressed in terms of direct damage costs such as repair/replacement costs, human losses and property damage costs, and indirect damage costs such as road user costs and indirect regional economic losses. For dealing with a variety of uncertainties associated with earthquake loads and capacities, a simulation-based reliability approach is used. The SMART-DRAIN-2DX, which is a modified version of the well-known DRAIN-2DX, is extended by incor-porating LCC analysis based on the LCC function developed in the study. Economic efficiencies for optimal seismic upgradings of the continuous PC segmental bridges are assessed using the proposed LCC functions and benefit-cost ratio.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.309-325
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• 2013

Environmental and operational variables are inevitable concerns by researchers and engineers when implementing the damage detection algorithm in practical projects, because the change of structural behavior could be masked by the conditions in a large extent. Thus, reliable damage detection methods should have a virtue of immunity from environmental and operational variables. In this paper, the pair cointegration method was presented as a novel way to remove the effect of environmental variables. At the beginning, the concept and procedure of this approach were introduced, and then the theoretical formulation and numerical simulations were put forward to illustrate the feasibility. The jump exceeding the control limit in the residual indicates the occurrence of damage, while the direction and magnitude imply the most potential damage location. In addition, the simulation results show that the proposed method has strong ability to resist the noise.

• Smart Structures and Systems
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• 제15권2호
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• pp.395-408
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• 2015

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

• 한국환경보건학회지
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• 제37권3호
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• pp.173-181
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• 2011

Koo-ni Firing Range in Maehyang-ri consisted of a firing range around 'Nong-sum' and a land-based firing range in the nearby coastal area. From 1951, when the US Air Force began field firing, residents of Maehyang-ri suffered damage to health, environment, life and property due to 54 years of field firing and noise. The purpose of this article is to provide an overview of the environmental damage, property loss, and health impairment by reviewing literature and related lawsuits. Environmental damage, the destruction of coastal fisheries and wetlands, would be a result of heavy metal contamination of soil. Fishing restrictions and the confiscation of farmlands caused financial hardship. Errant bombings led to the loss of life and property, including buildings and livestock. As a result, residents repeatedly sued the Republic of Korea for damages. The court decided partially in favor of the plaintiffs. Finally, Koo-ni firing range was closed down in 2005. Through the literature review, it is presumed that the noise caused hearing problems, hypertension, and mental illness. Since earlier studies were confined to a small fragment of the damage, additional studies focused on chronic health hazards are needed.

• Asian Pacific Journal of Cancer Prevention
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• 제16권15호
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• pp.6535-6539
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• 2015

This study was designed to assess, whether a new chemotherapeutic microtubule inhibitor, Epothilone B (EpoB, Patupilone), can induce DNA damage in normal ovarian cells (MM14.Ov), and to evaluate if such damage could be repaired. The changes were compared with the effect of paclitaxel (PTX) commonly employed in the clinic. The alkaline comet assay technique and TUNEL assay were used. The kinetics of DNA damage formation and the level of apoptotic cells were determined after treatment with IC50 concentrations of EpoB and PTX. It was observed that PTX generated significantly higher apoptotic and genotoxic changes than EpoB. The peak was observed after 48 h of treatment when the DNA damage had a maximal level. The DNA damage induced by both tested drugs was almost completely repaired. As EpoB in normal cells causes less damage to DNA it might be a promising anticancer drug with potential for the treatment of ovarian tumors.

• 한국환경과학회지
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• 제11권5호
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• pp.445-454
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• 2002

Material exposure experiments were performed to evaluate the relationship between air pollution and material corrosion rates based on collaboration with researchers in China, Japan, and Korea. Qualitative and quantitative atmospheric corrosion was estimated from damage caused to bronze, copper, steel, marble, cedar, cypress, and lacquer plates exposed to outdoor and indoor conditions in certain East Asian cities. The effects of atmospheric and meteorological factors on the damage to the copper plates and marble pieces were estimated using a regression analysis. The results indicated that sulfur dioxide produced the most destruction of the materials, especially in South Korea and China. In Japan, the copper plates were damaged as a result of natural conditions and sea salt. Copper was also found to be damaged by the surface deposition of sulfur and chlorine. Meanwhile, marble was substantially degraded by gaseous sulfur dioxide, yet sulfate ions in rain had no effect. Accordingly, the analysis of air pollution from the perspective of material damage was determined to be very useful in evaluating and substantiating the intensity of air pollution in East Asia.

• Structural Engineering and Mechanics
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• 제32권2호
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• pp.301-320
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• 2009

Damage assessment for buried structures against an internal blast is conducted by considering the soil-structure interaction. The structural element under analysis is assumed to be rigid-plastic and simply-supported at both ends. Shear failure, bending failure and combined failure modes are included based on five possible transverse velocity profiles. The maximum deflections with respect to shear and bending failure are derived respectively by employing proper failure criteria of the structural element. Pressure-Impulse diagrams to assess damage of the buried structures are subsequently developed. Comparisons have been done to evaluate the influences of the soil-structure interaction and the shear-to-bending strength ratio of the structural element. A case study for a buried reinforced concrete structure has been conducted to show the applicability of the proposed damage assessment method.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.399-414
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• 2013

The smart sensor technology has opened new horizons for assessing and monitoring structural health of civil infrastructure. Smart sensor's unique features such as onboard computation, wireless communication, and cost effectiveness can enable a dense network of sensors that is essential for accurate assessment of structural health in large-scale civil structures. While most research efforts to date have been focused on realizing wireless smart sensor networks (WSSN) on bridge structures, relatively less attention is paid to applying this technology to buildings. This paper presents a decentralized damage detection using the WSSN for building structures. An existing flexibility-based damage detection method is extended to be used in the decentralized computing environment offered by the WSSN and implemented on MEMSIC's Imote2 smart sensor platform. Numerical simulation and laboratory experiment are conducted to validate the WSSN for decentralized damage detection of building structures.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.763-765
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• 2003

We made a hazard map using GIS and remote sensing for he greatest inundation damage that happened for the 20th century. We calculated the land cover classification using Landsat from 1983 to 2000. We calculated it from a damage report and an aerial photo for a flood. We considered relation of both land cover classification and the damage. We expected the inundation damage in the future and made a hazard map.

• Smart Structures and Systems
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• 제30권6호
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.