• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.

• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Advances in Computational Design
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• v.3 no.3
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• pp.289-302
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• 2018

The use of optimum content of supplementary cementing materials (SCMs) such as limestone filler (LF) to blend with Portland cement has been resulted in many environmental and technical advantages, such as increase in physical properties, enhancement of sustainability in concrete industry and reducing $CO_2$ emission are well known. Artificial neural networks (ANNs) have been already applied in civil engineering to solve a wide variety of problems such as the prediction of concrete compressive strength. The feed forward back propagation (FFBP) algorithm and Tan-sigmoid transfer function were used for the ANNs training in this study. The training, testing and validation of data during the backpropagation training process yielded good correlations exceeding 97%. A parametric study was conducted to study the sensitivity of the developed model to certain essential parameters affecting the compressive strength of concrete. The effects and benefits of limestone filler on hardened properties of the concrete such as compressive strength were well established endorsing previous results in the literature. The results of this study revealed that the proposed ANNs model showed a high performance as a feasible and highly efficient tool for simulating the LF concrete compressive strength prediction.

• Journal of Fashion Business
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• v.21 no.5
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• pp.109-121
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• 2017

In this study, we proposed the Bustier which improves the fit and functionality to compensate the physical defects by maximizing the volume up effect using the breast detailed measurements and shapes of the poor breast female subjects. Based on the preliminary study of the problem and preference according to the characteristics of the poor breast women consumers, we produced 1/2 volume mold cups based on the previous research. Respectively. A total of 5 subjects were selected, and new 3 bustier patterns based on the pattern making system of industry were created through direct measurement and shapes. As a result of verifying the usability of the developed bustier by testing the commercially available bustiers and the newly developed bustiers for 5 subjects. In order to compare the existing bustiers with the newly developed bustiers, the appearance evaluation by the expert, the evaluation of the adaptability and satisfaction by the subjects were utilized. Through this experiment, the newly developed bustiers were superior in the evaluation of the motion adaptability and wear comfort as well as appearance test. It was shown that the wear effect of the bustier with the longest back length was the best.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.1
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• pp.267-277
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• 2003

The purpose of this study makes a retrofit and rehabilitation practice trough the analysis and the improvement for the underlying problem of current retrofit and rehabilitation methods. Therefore, the deterioration process, the damage cause, the condition classification, the fatigue mechanism and the applied quantity of strengthening methods for slab bridge decks were analysed. Artificial neural networks are efficient computing techniqures that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a management on existing slab bridge decks from damage cause, damage type, and integrity assessment at the initial stsge is need. The training and testing of the network were based on a database of 36. Four different network models werw used to study the ability of the neural network to predict the desirable output of increasing degree of accuracy. The neural networks is trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterms were minimized. This generally occurred after about 5,000 cycles of training.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.1
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• pp.101-107
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• 2018

In this study, the analysis of radiation noise and chattering noise of vehicle seat recliner was conducted through testing and analysis. First, operating noise was measured by seat back frame and recliner, and chattering noise was confirmed. Next, the transient dynamic analysis was performed, and the result was mapped to the acoustic analysis. Finally, the test and analysis were compared and analyzed. The results are as follow. First, it was found that the peaks appeared in common in the range of 620~650Hz, 1,240~1,290 Hz, and 1,840~1,940 Hz. It was judged that the dynamic characteristics of the recliner system overlapped with the rotation component of the motor to cause amplification of noise and vibration. Next, as a result of imaging the radiation noise analysis, it was judged that the noise radiated in the forward and backward direction has a greater influence than the direction of the rotation axis when the ear position of the person is taken as a reference.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.37-43
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• 2012

LED TV integration test unit are the equipment for testing LED BLU as LED BLU's operation, error check, AGING work. The previous test unit were inconvenient because it must change test unit in production line according to LCD's kind and size. This paper develops by integration style test unit that can accommodate various LED TV BLU model of 32 ~ 52 inch that is not one LED TV BLU model. We make that output current is various to dimension of 80 ~ 215 mA. We make functions that are LED BLU state check, Diming and heat occurrence mitigation. Also we develop firmware program and that the program can be upgraded and that can support new panel.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.227-236
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• 2002

The purpose of this study makes a retrofit and rehabilitation practice trough the analysis and the improvement for the underlying problem of current retrofit and rehabilitation methods. Therefore, the deterioration process, the damage cause, the condition classification, the fatigue mechanism and the applied quantity of strengthening methods for RC deck slabs were analyzed. Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a management on existing reinforced concrete bridge decks from damage cause, damage type, and integrity assessment at the initial stage is need. The training and testing of the network were based on a database of 36. Four different network models were used to study the ability of the neural network to predict the desirable output of increasing degree of accuracy. The neural networks is trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterns were minimized. This generally occurred after about 5,000 cycles of training.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.05a
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• pp.321-325
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• 2010

Supersonic ejectors are simple mechanical components, which generally perform mixing and/or recompression of two fluid streams. Ejectors have found many applications in engineering. In aerospace engineering, they are used for altitude testing of a propulsion system by reducing the pressure of a test chamber. It is composed of three major sections: a vacuum test chamber, a propulsive nozzle, and a supersonic exhaust diffuser. This paper aims at the improvement of ejector-diffuser performance by focusing attention on reducing exhaust back flow into the test chamber, since alteration of the backflow or recirculation pattern appears as one of the potential means of significantly improving low supersonic ejector-diffuser performance. The simplest backflow-reduction device was an orifice plate at the duct inlet, which would pass the jet and entrained fluid but impede the movement of fluid upstream along the wall. Results clearly showed that the performance of ejector-diffuser system was improved for certain a range of system pressure ratios, whereas the orifice plate was detrimental to the ejector performance for higher pressure ratios. It is also found that there is no change in the performance of diffuser with orifice at its inlet, in terms of its pressure recovery. Hence an appropriately sized orifice system should produce considerable improvement in the ejector-diffuser performance in the intended range of pressure ratios.