• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.161-173
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• 2020

This study examines the nonlinear relationship between financial development and economic growth in Pakistan using the threshold regression model for the period 1980-2017. We also employed quantile regression with 0.25, 0.50, and 0.75 quantiles of conditional distribution. The quantile regression is based on minimizing of sum of squared residuals. The result indicates that economic growth responds positively to financial development when the level of financial development surpasses the threshold value of 0.151. However, when financial development lies below the threshold value (that is, 0.151), its impact on economic growth is negative. Thus, when financial development of Pakistan surpasses the threshold level, it contributes more towards economic growth since greater level of financial development contributes more to boosts economic growth. This finding reveals that economic growth reacts differently to financial development, and the relationship between financial development and economic growth is U-shaped in Pakistan. Among the other variables, physical capital, labor force, and government expenditure exert a positive effect on economic growth. Furthermore, inflation rate and trade openness have an insignificant impact on economic growth. The results of quantile regression also confirm the non-linear relationship between financial development and economic growth in Pakistan. The finding of this study suggests revamping of financial sector policies in Pakistan.

• Computers and Concrete
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• v.13 no.1
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• pp.17-30
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• 2014

This research presents the effect of various ground pozzolanic materials in blended cement concrete on the strength and chloride penetration resistance. An experimental investigation dealing with concrete incorporating ground fly ash (GFA), ground bottom ash (GBA) and ground rice husk ash (GRHA). The concretes were mixed by replacing each pozzolan to Ordinary Portland cement at levels of 0%, 10%, 20% and 40% by weight of binder. Three different water to cement ratios (0.35, 0.48 and 0.62) were used and type F superplasticizer was added to keep the required slump. Compressive strength and chloride permeability were determined at the ages of 28, 60, and 90 days. Furthermore, using this experimental database, linear and nonlinear multiple regression techniques were developed to construct a mathematical model of chloride permeability in concretes. Experimental results indicated that the incorporation of GFA, GBA and GRHA as a partial cement replacement significantly improved compressive strength and chloride penetration resistance. The chloride penetration of blended concrete continuously decreases with an increase in pozzolan content up to 40% of cement replacement and yields the highest reduction in the chloride permeability. Compressive strength of concretes incorporating with these pozzolans was obviously higher than those of the control concretes at all ages. In addition, the nonlinear technique gives a higher degree of accuracy than the linear regression based on statistical parameters and provides fairly reasonable absolute fraction of variance ($R^2$) of 0.974 and 0.960 for the charge passed and chloride penetration depth, respectively.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.171-177
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• 2011

In order to construct approximation functions for real data, it is necessary to remove the outliers from the measured raw data before constructing the model. Conventionally, visualization and maximum residual error have been used for outlier detection, but they often fail to detect outliers for nonlinear functions with multidimensional input. Although the standard support vector regression based outlier detection methods for nonlinear function with multidimensional input have achieved good performance, they have practical issues in computational cost and parameter adjustments. In this paper we propose a practical approach to outlier detection using support vector regression that reduces computational time and defines outlier threshold suitably. We apply this approach to real data examples for validity.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.639-645
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• 2013

The aim of the study is to investigate the method to estimate a storage life of propelling charge on the decrease of muzzle velocity by stochastic gamma process model. It is required to establish criterion for state failure to estimate the storage life and it is defined in this paper as a muzzle velocity difference between reference value and maximum allowable standard deviation multiplied by 6. The relationship between storage time and muzzle velocity is investigated by nonlinear regression analysis. The stochastic gamma process model is used to estimated the state distribution and the life distribution for storage time for 155mm propelling charge KM4A2 because the regression analysis is a deterministic method and it can't describe the distribution of life for storage time.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.595-611
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• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.3
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• pp.112-126
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• 1996

This study was conducted to develop an optimal runoff bydrograph model by comparison of the peak discharge and time to peak between observed and simulated flows derived by four different models, that is, linear time-invariant, linear time-variant, nonlinear time-invariant and nonlinear time-variant models under the conditions of heavy rainfalls with regionally uniform rainfall intensity in short durations at nine small watersheds. The results obtained through this study can be summarized as follows. 1. Parameters for four models including linear time-invariant, linear time-variant, nonlinear time-invariant and nonlinear time-variant models were calibrated using a trial and error method with rainfall and runoff data for the applied watersheds. Regression analysis among parameters, rainfall and watershed characteristics were established for both linear time-invariant and nonlinear time-invariant models. 2. Correlation coefficients of the simulated peak discharge of calibrated runoff hydrographs by using four models were shown to be a high significant to the peak of observed runoff graphs. Especially, it can be concluded that the simulated peak discharge of a linear time-variant model is approaching more closely to the observed runoff hydrograph in comparison with those of three models in the applied watersheds. 3. Correlation coefficients of the simulated time to peak of calibrated runoff hydrographs by using a linear time-variant model were shown to be a high significant to the time to peak of observed runoff hydrographs than those of the other models. 4. The peak discharge and time to peak of simulated runoff hydrogaphs by using linear time-variant model are verified to be approached more closely to those of observed runoff hydrographs than those of three models in the applied watersheds. 5. It can be generally concluded that the shape of simulated hydrograph based on a linear time-variant model is getting closer to the observed runoff hydrograph than those of three models in the applied watersheds. 6. Simulated hydrographs using the nonlinear time-variant model which is based on more closely to the theoritical background of the natural runoff process are not closer to the observed runoff hydrographs in comparison with those of three models in the applied watersheds. Consequently, it is to be desired that futher study for the nonlinear time-variant model should be continued with verification using rainfall-runoff data of the other watersheds in addition to the review of analyical techniques.

• Computers and Concrete
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• v.20 no.1
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• pp.31-38
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• 2017

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

• Environmental Engineering Research
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• v.19 no.3
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• pp.247-253
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• 2014

The aim of this study was to characterize quintinite in fluoride removal from aqueous solutions, using batch experiments. Experimental results showed that the maximum adsorption capacity of fluoride to quintinite was 7.71 mg/g. The adsorption of fluoride to quintinite was not changed at pH 5-9, but decreased considerably in highly acidic (pH < 3) and alkaline (pH > 11) solution conditions. Kinetic model analysis showed that among the three models (pseudo-first-order, pseudo-second-order, and Elovich), the pseudo-second-order model was the most suitable for describing the kinetic data. From the nonlinear regression analysis, the pseudo-second-order parameter values were determined to be $q_e=0.18mg/g$ and $k_2=28.80g/mg/hr$. Equilibrium isotherm model analysis demonstrated that among the three models (Langmuir, Freundlich, and Redlich-Peterson), both the Freundlich and Redlich-Peterson models were suitable for describing the equilibrium data. The model analysis superimposed the Redlich-Peterson model fit on the Freundlich fit. The Freundlich model parameter values were determined from the nonlinear regression to be $K_F=0.20L/g$ and 1/n=0.51. This study demonstrated that quintinite could be used as an adsorbent for the removal of fluoride from aqueous solutions.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.31-38
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• 2000

Relationships between hydrologic variables are often nonlinear. Usually the functional form of such a relationship is not known a priori. A multivariate, nonparametric regression methodology is provided here for approximating the underlying regression function using locally weighted polynomials. Locally weighted polynomials consider the approximation of the target function through a Taylor series expansion of the function in the neighborhood of the point of estimate. The utility of this nonparametric regression approach is demonstrated through an application to nonparametric short term forecasts of the biweekly Great Salt Lake volume.volume.