• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.283-300
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• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

• Journal of Mechanical Science and Technology
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• v.15 no.9
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• pp.1292-1301
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• 2001

The mean-line method using empirical models is the most practical method of predicting off-design performance. To gain insight into the empirical models, the influence of empirical models on the performance prediction results is investigated. We found that, in the two-zone model, the secondary flow mass fraction has a considerable effect at high mass flow-rates on the performance prediction curves. In the TEIS model, the first element changes the slope of the performance curves as well as the stable operating range. The second element makes the performance curves move up and down as it increases or decreases. It is also discovered that the slip factor affects pressure ratio, but it has little effect on efficiency. Finally, this study reveals that the skin friction coefficient has significant effect on both the pressure ratio curve and the efficiency curve. These results show the limitations of the present empirical models, and more resonable empirical models are reeded.

• Food Science and Preservation
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• v.3 no.1
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• pp.39-53
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• 1996

Ginseng, one of the important economic crops, is processed into medicine, teas, beverages and even foods. Drying is the most important and burdensome work in the processing of ginseng, so development of ginseng dryer is needed for efficient drying and good quality of ginseng. Investigation of drying model is essential for development of ginseng dryer. Drying models for peeled ginseng were investigated to determine dominant drying factors and fitted with five selected drying models and an empirical model. Thompson and the empirical model showed best fit with the experimental data. Pother experiment is necessary to prove the superiority of the empirical models.

• Computers and Concrete
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• v.26 no.6
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• pp.533-546
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• 2020

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

• Journal of Biosystems Engineering
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• v.34 no.5
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• pp.297-304
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• 2009

This study was conducted to investigate the adequacy of the representative empirical models which are developed for predicting the tractive performance of the tractor operating in various soil conditions. Four representative empirical models which are widely used in the traction prediction of tractor were selected through literature review. Four models were Wismer-Luth, Brixius, Dwyer and Hernandez model, which were empirical traction models of a single wheel. The efficacy of four models were confirmed via comparison of the tractions of tractor predicted using the four models with those measured from traction tests which were conducted for two different driving type (2WD and 4WD) of the tractor on two different soil conditions. The results showed that tractions predicted by Brixius' model, especially for slip range under 20% which the operating efficiency of a tractor is very high, were well consistent with the ones measured from traction test better than the tractions predicted by models which are proposed by Wismer-Luth, Dwyer and Hernandez.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.38.1-38.1
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• 2013

We present the Yonsei evolutionary population synthesis (YEPS) models based on the high-resolution empirical spectral energy distributions (SEDs). We have adopted the MILES library in the optical wavelength, and our new models based on the MILES library show good agreements with our previous models presented in the YEPS I. The effect of hot horizontal-branch (HB) stars on the integrated properties of simple stellar populations (SSPs) is again confirmed by our models based on empirical SEDs. In addition, we have extended our empirical models to the near-IR wavelength and predicted the strengths of the calcium II triplet (CaT) and the Paschen triplet (PaT) based on the INDO-US and the Cenarro library. We find that the effect of HB stars and the age of SSPs on the CaT is almost negligible. On the other hands, the PaT models are very sensitive to the existence of hot stars, e.g., HB stars and young turn-off stars, and show very similar results with Balmer lines. Interestingly, the CaT distribution of GCs in NGC 1407, which is at odds with the optical (B-I) color distribution, can be explained by the unique feature of the CaT-[Fe/H] relations that show almost the same equivalent widths in the metal-rich regime. We will also discuss the impact of the second-generation populations on the strength of the CaT.

• Tunnel and Underground Space
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• v.13 no.4
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• pp.260-269
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• 2003

In order to introduce to engineers the suitable calculation techniques of TBM advance rate (ad.) and ultimately promote to understand the designing process, this study was carried out. We analyzed the 17 bored data of TBM which applied to the roadway and water supply tunnels in Korea. From this analysis, it was able to how that the average utilization is 30.83% md the correlation equation of Ad and TBM´s diameter (D) is Ad(m/month) = 506.05ㆍ $e^{-0.1162}$$\times$D than the correlation coefficient ($R^2$) is 0.76. In the object of the W tunnel of Seoul-Busan highspeed railway, the Ad of TBM 5.0mø was analyzed by the variety of empirical models and upper correlation equation. Average Ad of the empirical models was calculated to be larger than one of the upper equations. But considering only the results of 3.0~5.0mø TBM in the 17 bored data, the average Ad by the models belongs to the similar range of bored data. Therefore, when the reliability and representative of parameters are decreased, a reliability test should be carried out through the comparison a variety of empirical models with the upper correlation equation.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.307-314
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• 2013

The Bidirectional Reflectance Distribution (BRD) effect is critical to interpret the surface information using remotely sensed data. This effect was caused by geometric relationship between sensor, target and solar that is inevitable effect for data of optical sensor. To remove the BRD effect, semi-empirical BRDF models are widely used. It is faster to calculate than physical models and demanded less observation than empirical models. In this study, Ross-Li kernel and Roujean kernel were used respectively in National Aeronautics and Space Administration (NASA) and European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) that are used to compare each other. The semi-empirical model consists of three parts which are isotropic, geometric and volumetric scattering. Each part contained physical kernel and empirical coefficients that were calculated by statistical method. Red and NIR channel of SPOT/VEGETATION product were used to compute Nadir BRDF Adjusted Reflectance (NBAR) over East Asia area from January 2009 to December 2009. S1 product was provided by VITO that was conducted atmospheric correction using Simplified Method of Atmospheric Correction (SMAC). NBAR was calculated using corrected reflectance of red and NIR. Previous study has revealed that Roujean geometric kernel had unphysical values in large zenith angles. We extracted empirical coefficients in three parts and normalized reflectance to compare both BRDF models. Two points located forest in Korea peninsular and bare land in Gobi desert were selected for comparison. As results of time series analysis, both models showed similar reflectance change pattern and reasonable values. Whereas in case of empirical coefficients comparison, different changes pattern of values were showed in isotropic coefficients.

• Korean Management Science Review
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• v.11 no.3
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• pp.153-167
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• 1994

Credibility theory has provided with a useful tool the assignment of weighting factor that reflects the credibility of the observed individual and collective experience to secure fair experience rate-,making. We review credibility models which can effectively estimate risk premiums using credibility theory, and suggest an empirical Bayed model based on the collective statistics to estimate the structural parameters. To illustrate the use of evolutionary models, the models are applied to the actual data, such as loss ratio, claim frequencies and severity, in the Korean automobile insurance. Also the possibilities of generalizations and applications of empirical models are discussed.

• Journal of Ecology and Environment
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• v.33 no.2
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• pp.165-173
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• 2010

Soil respiration ($R_S$) is a critical component of the annual carbon balance of forests, but few studies thus far have attempted to evaluate empirical regression models in $R_S$. The principal objectives of this study were to evaluate the relationship between $R_S$ rates and soil temperature (ST) and soil water content (SWC) in soil from a cool-temperate deciduous broad-leaved forest, and to evaluate empirical regression models for the prediction of $R_S$ using ST and SWC. We have been measuring $R_S$, using an open-flow gas-exchange system with an infrared gas analyzer during the snowfree season from 1999 to 2001 at the Takayama Forest, Japan. To evaluate the empirical regression models used for the prediction of $R_S$, we compared a simple exponential regression (flux = $ae^{bt}$Eq. [1]) and two polynomial multiple-regression models (flux = $ae^{bt}{\times}({\theta}{\nu}-c){\times}(d-{\theta}{\nu})^f:$ Eq. [2] and flux = $ae^{bt}{\times}(1-(1-({\theta}{\nu}/c))^2)$: Eq. [3]) that included two variables (ST: t and SWC: ${\theta}{\nu}$) and that utilized hourly data for $R_S$. In general, daily mean $R_S$ rates were positively well-correlated with ST, but no significant correlations were observed with any significant frequency between the ST and $R_S$ rates on periods of a day based on the hourly $R_S$ data. Eq. (2) has many more site-specific parameters than Eq. (3) and resulted in some significant underestimation. The empirical regression, Eq. (3) was best explained by temporal variations, as it provided a more unbiased fit to the data compared to Eq. (2). The Eq. (3) (ST $\times$ SWC function) also increased the predictive ability as compared to Eq. (1) (only ST exponential function), increasing the $R^2$ from 0.71 to 0.78.