• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4A
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• pp.267-279
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• 2009

A numerical model that can simulate the nonlinear behavior of ultra high strength fiber reinforced concrete (UHSFRC) structures subjected to monotonic loading is introduced. The material properties of UHSFRC, such as compressive and tensile strength or elastic modulus, are different from normal strength reinforced concrete. The uniaxial compressive stress-strain relationship of UHSFRC is designed on the basis of experimental result, and the equivalent uniaxial stress-strain relationship is introduced for proper estimation of UHSFRC structures. The steel is uniformly distributed over the concrete matrix with particular orientation angle. In advance, this paper introduces a numerical model that can simulate the tension-stiffening behavior of tension part of the axial member on the basis of the bond-slip relationship. The reaction of steel fiber is considered for the numerical model after cracks of the concrete matrix with steel fibers are formed. Finally, the introduced numerical model is validated by comparison with test results for idealized UHSFRC beams.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.2
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• pp.112-121
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• 2013

The calibration of resistance factor in reliability theory for limit state design of gravel compaction piles (GCP) requires a reliable estimate of ultimate bearing capacity. The static load test is commonly used in geotechnical engineering practice to predict the ultimate bearing capacity. Many graphical methods are specified in the design standard to define the ultimate bearing capacity based on the load-settlement curve. However, it has some disadvantages to ensure reliability to obtain an uniform ultimate load depend on engineering judgement. In this study, a well-fitting nonlinear regression model is proposed to estimate the ultimate bearing capacity, for which a nonlinear regression analysis is applied to estimate the ultimate bearing capacity of GCP and the results are compared with those calculated using previous graphical method. Affect the resistance factor of the estimate method were analyzed. To provide a database in the development of limit state design, the load test conditions for predicting the ultimate bearing capacity from static load test are examined.

• 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.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.6 s.144
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• pp.601-607
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• 2005

This paper describes a hydrodynamic modelling study based on the Feldman's equation to predict the nonlinear and coupled maneuvering characteristics of high speed submarine. The hydrodynamic coefficients set is obtained from the modeling of the cross flow drag force and sail induced vorticity, and the captive model experiments(VPMM and RA test) results used to improved the accuracy. The results contained in this paper will be helpful to predict the behavior of tight turn maneuver and to improve the SOE(Safety Operational Envelope) analysis in case of emergency maneuver.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.2
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• pp.97-103
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• 2008

Determination of stress-strain relationship in torsional tests is complicated due to nonuniform stress-strain variation occurring linearly with the radius in a soil specimen in torsion. The equivalent radius approach is adequate when calculating strain at low to intermediate strains, however, the approach is less accurate when performing the test at higher strain levels. The modified equivalent radius approach was developed to account for the problem more precisely. This approach was extended to generate the plots of equivalent radius ratio versus strain using modified hyperbolic and Ramberg-Osgood models. Results showed the effects of soil nonlinearity on the equivalent radius ratio curves were observed. Curve fitting was also performed to find the stress-strain relationship by fitting the theoretical torque-rotation relationship to measured torque-rotation relationship.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.399-412
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• 2010

The goal of this research is to apply the neural networks models for the temporal disaggregation of the yearly pan evaporation (PE) data, Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model (MLP-NNM) and generalized regression neural networks model (GRNNM), respectively. And, for the performances evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. From this research, we evaluate the application of MLP-NNM and GRNNM for the temporal disaggregation of nonlinear time series data. We should, furthermore, construct the credible monthly PE data from the temporal disaggregation of the yearly PE data, and can suggest the available data for the evaluation of irrigation and drainage networks system.

• Steel and Composite Structures
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• v.28 no.4
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• pp.483-494
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• 2018

In this research the effect of high strength concrete (HSC) on shear capability of the channel shear connectors (CSC) in the steel concrete composite floor system was estimated experimentally and analytically. The push-out test was carried out for assessing the accurateness of the proposed model (nonlinear and finite element model) for the test specimens. A parametric analysis was conducted for predicting the shear capacity of the connectors (CSC) in the HSC. Eight push-out specimens of different sizes with different strength levels were tested under the monotonic loading system. The aim of this study was to evaluate the efficacy of the National Building Code of Canada (NBC) of Canada for analysing the loading abilities of the CSC in the HSC. Using the experimental tests results and verifying the finite element results with them, it was then confirmed by the extended parametric studies that the Canadian Design Code was less efficient for predicting the capacity of the CSC in the HSC. Hence, an alternative equation was formulated for predicting the shear capacity of these connectors during the inclusion of HSC for designing the codes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.21-28
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• 2010

This paper describes the mathematical model and controller design for Manta-type Unmanned Underwater Test Vehicle (MUUTV) with 6 DOF nonlinear dynamic equations. The mathematical model contains hydrodynamic forces and moments expressed in terms of a set of hydrodynamic coefficients which were obtained through the PMM (Planar Motion Mechanism) test. Based on the 6 DOF dynamic equations, numerical simulations have been performed to analyze the dynamic performances of the MUUTV. In addition, using the mathematical model PID and sliding mode controller are constructed for the diving and steering maneuver. Simulation results show that the control performances of the MUUTV and compared with these of NPS (Naval Postgraduate School) AUV II.

• Computers and Concrete
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• v.28 no.5
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• pp.521-532
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• 2021

Present paper focuses on the modeling of size effect on the compressive strength of normal concrete with the application of Discrete Element Method (DEM). Test specimens with different size and shape were cast and uniaxial compressive strength test was performed on each sample. Five different concrete mixes were used, all belonging to a different normal strength concrete class (C20/25, C30/37, C35/45, C45/55, and C50/60). The numerical simulations were carried out by using the PFC 5 software, which applies rigid spheres and contacts between them to model the material. DEM modeling of size effect could be advantageous because the development of micro-cracks in the material can be observed and the failure mode can be visualized. The series of experiments were repeated with the model after calibration. The relationship of the parallel bond strength of the contacts and the laboratory compressive strength test was analyzed by aiming to determine a relation between the compressive strength and the bond strength of different sized models. An equation was derived based on Bazant's size effect law to estimate the parallel bond strength of differently sized specimens. The parameters of the equation were optimized based on measurement data using nonlinear least-squares method with SSE (sum of squared errors) objective function. The laboratory test results showed a good agreement with the literature data (compressive strength is decreasing with the increase of the size of the specimen regardless of the shape). The derived estimation models showed strong correlation with the measurement data. The results indicated that the size effect is stronger on concretes with lower strength class due to the higher level of inhomogeneity of the material. It was observed that size effect is more significant on cube specimens than on cylinder samples, which can be caused by the side ratios of the specimens and the size of the purely compressed zone. A limit value for the minimum size of DE model for cubes and cylinder was determined, above which the size effect on compressive strength can be neglected within the investigated size range. The relationship of model size (particle number) and computational time was analyzed and a method to decrease the computational time (number of iterations) of material genesis is proposed.