• 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.

• Korean journal of communication and information
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• v.76
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• pp.124-150
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• 2016

The aim of this study is to see the change of the general characteristics and frame of nuclear power plant news in Korea from comparing the before Fukushima nuclear accident in Japan on March 11, 2011 with the after. To this aim, the national daily newspapers and the local daily newspapers in Busan located nuclear power plants were selected, and the content analysis of the newspaper stories about nuclear power plants was done. In research results, the stories about nuclear power plants in Korean newspapers increased greatly after Fukushima nuclear accident. Before the accident the nuclear power plant stories about economy held a large majority, while after the accident the stories about society held. Fukushima nuclear accident served as the momentum that the nuclear power plant stories in Korea became main news. Meanwhile, the frame of nuclear power plant stories in Korean newspapers changed greatly after the accident. Justly the environmental security frame increased greatly, because of increasing greatly the stories about security of nuclear power plants with Fukushima nuclear accident. Particularly in the local daily newspapers in Busan before the accident the environmental security frame was 29.3% of stories about nuclear power plants, and after the accident the frame was 77.6%.

• Applied Biological Chemistry
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• v.41 no.2
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• pp.181-186
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• 1998

The effects of sewage treatment on characteristics of sludge as a composting material were investigated for a year during the initial operation at the full-scale Chunan sewage treatment plant. Due to the shortage of design capacity of belt press, a sludge dewatering unit, non-volatile solids were recirculating and concentrating in the treatment plant, resulting in an increase of MLSS and a decrease in F/M ratio at the activated sludge system. Special attention is required for long term operations since the increase of non-volatile solids in the plant would deteriorates the treatment efficiency. The sewage sludge of the Chunan sewage treatment plant showed 79.5% of water content, 11.6% of organic content, and C/N ratio of 6.1, and contained As 1.8 mg/kg, Cd 27 mg/kg, Hg <0.1 mg/kg, Pb 54 mg/kg, T-Cr 370 mg/kg, and Cu 1,100mg/kg of heavy metals. In order to be used as raw material for optimum composting, the sewage sludge requires bulking agents for moistrure/porosity control and a carbon source for adjusting C/N ratio. However, the sewage sludge is not adequate as a soil conditioner after composing due to a high content of heavy metals. If the sewage sludge has to he used as a soil conditioner after composting, it as required to identify and remove tire industrial wastewater portions in tire influent of the plant since heavy metals in the influent were mostly concentrated in dewatered sludge.

• Resources Recycling
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• v.15 no.3 s.71
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• pp.38-45
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• 2006

The thermal filter press dewatering(TFPD) technology to improve the dewaterability through increasing the inner vapor pressure, lowering the filtration viscosity and forming the porosity easily within cake as applying the heat at the sludge layer was developed in this study. The hot water with temperature of $95^{\circ}C$ and pressure of $1.2kg_f/cm^2$ was supplied to the heating plate equipped between filter plates with plate size of $470{\times}470mm$ and material of polypropylene. Sludge was dewaterd by supplying pressure of $5kg_f/cm^2$ and then by squeezing pressure of $15kg_f/cm^2$. As a results of estimating the characteristics of thermal dewatering to consider the initial water content and organic content to be influenced by a period of water shortage and rainwater, the dewatered cake water content was about 35 wt% and dewatering velocity was $4DSkg/m^2{\cdot}hr$ under the rainwater period, and the dewatered cake water content was about 50 wt% and dewatering velocity was $1.5DSkg/m^2{\cdot}hr$ in the case of sludge of water shortage season. These results was superior to the mechanical dewatering performance with water content of 70wt% and dewatering velocity of $0.9DSkg/m^2{\cdot}hr$. On the base of the results of TFPD, energy consumpted to deal with DS(Dry Solid) of 1kg was estimated by 300 kJ. It was analyzed that the energy consumption of TFPD was decreased about one third with comparison to the dryer system. Dewatering velocity of this technology was faster than the one of mechanical dewatering equipment and it was easier to product low water content cake. Therefore, this technology was recognized that dewaterability was predominant because of the fast of dewatering velocity and production of low water content cake, and also this known as economical efficiency was excellent because of low energy consumption in comparison with dryer.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.