• Wind and Structures
• /
• v.2 no.1
• /
• pp.25-40
• /
• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

• Computers and Concrete
• /
• v.1 no.2
• /
• pp.211-226
• /
• 2004

A relatively simple multiple-vertical-line-element macro model has been incorporated into a standard computer code DRAIN-2D. It was used in blind predictions of seismic response of cantilever RC walls subjected to a series of consequent earthquakes on a shaking table. The model was able to predict predominantly flexural response with relative success. It was able to predict the stiffness and the strength of the pre-cracked specimen and time-history response of the highly nonlinear wall as well as to simulate the shift of the neutral axis and corresponding varying axial force in the cantilever wall. However, failing to identify the rupture of some brittle reinforcement in the third test, the model was not able to predict post-critical, near collapse behaviour during the subsequent response to two stronger earthquakes. The analysed macro model seems to be appropriate for global analyses of complex building structures with RC structural walls subjected to moderate/strong earthquakes. However, it cannot, by definition, be used in refined research analyses monitoring local behaviour in the post critical region.

• Computers and Concrete
• /
• v.28 no.6
• /
• pp.635-642
• /
• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.3
• /
• pp.149-155
• /
• 2020

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper's aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.

• Nuclear Engineering and Technology
• /
• v.29 no.2
• /
• pp.158-166
• /
• 1997

Integrated performance test of Chemical and Volume Control System was successfully performed in 1994. However, an extensive effort to correct hardware and software problems in the letdown line was required mainly due to the lack of adequate simulation code to predict the test accurately. Although the LTC computer code was used during the YGN 3'||'&'||'4 NSSS design process, the code can not satisfactorily predict the test due to it insufficient letdown line modeling. This study developed a numerical model to simulate the letdown test by modifying the current LTC code, and then verified the model by comparing with the test data. The comparison shows that the modified LTC computer code can predict the transient behavior of letdown system lese very well. Especially, the model was verified to be able to predict the "Stiction (composition of stick and friction)" phenomena which caused instantaneous fluctuations in the letdown backpressure and flowrate. Therefore, it is concluded that the modified LTC computer code with the ability of calculating the "Stiction" phenomena will be very useful for future plant design and test predictions.predictions.

• 한국전산유체공학회:학술대회논문집
• /
• 2006.10a
• /
• pp.77-82
• /
• 2006

Evaluation of the elliptic blending turbulence model (EBM) together with the two-layer model, shear stress transport (SST) model and elliptic relaxation model (V2-F) is performed for a better prediction of natural convection and thermal stratification. For a natural convection problem the models are applied to the prediction of a natural convection in a rectangular cavity and the computed results are compared with the experimental data. It is shown that the elliptic blending model predicts as good as or better than the existing second moment differential stress and flux model for the mean velocity and turbulent quantities. For thermal stratification problem the models are applied to the thermal stratification in the upper plenum of liquid metal reactor. In this analysis there exist much differences between the turbulence models in predicting the temporal variation of temperature. The V2-F model and EBM better predict the steep gradient of temperature at the interface of thermal stratification, and the V2-F model and EBM predict properly the oscillation of temperature. The two-layer model and SST model fail to predict the temporal oscillation of temperature.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.49-54
• /
• 1994

A dynamic model for the cutting process in the end milling process is developed. This model, which describes the dynamic response of the end mill, the chip load geometry including tool runout, the dependence of the cutting forces on the chip load, is used to predict the dynamic cutting force during the end milling process. In order to predict accurately cutting forces and tool vibration, the model, which uses instantaneous specific cutting force, includes both regenerative effect and penetration effect. The model is verified through comparisons of model predicted cutting force with measured cutting forces obtained from machining experiments.

• Proceedings of the KIEE Conference
• /
• 1985.07a
• /
• pp.127-130
• /
• 1985

A stochastic model to predict radio interference field as caused by corona discharges on high voltage transmission lines has been developed. This model is based on corona discharge distributed randomly in time and space. A stochastic model for the corona current induced by corona discharges on power lines is proposed. On the basis of the proposed corona current model, a rigorous analysis is presented to evaluate the radio interference (RI) field caused by corona discharges on a single conductor using the stochastic method.

• Journal of Korean Society for Atmospheric Environment
• /
• v.26 no.6
• /
• pp.666-682
• /
• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.644-655
• /
• 2008

Conventional hyperbolic model does not satisfactorily predict the overall stress-strain behaviors of various geomaterials. Tatsuoka and Shibuya(1992) suggest the generalized hyperbolic equation(GHE) considering strain dependency and calculated performance is in good agreement with precise triaxial compression test results of stress-strain relations over wide range of strains before peak stress condition in some cases, but GHE model also does not satisfactorily predict stress-strain relations as strain goes on state of peak stress in most cases. For improve a weak point of the GHE, in this study, modified form of generalized hyperbolic equation (MGHE model) is proposed which can predict highly nonlinear stress-strain behavior for various geomaterials from small strain to peak stress condition.