Multiple Nonlinear Regression Analysis Research Articles

Use of non-linear prediction tools to assess rock mass permeability using various discontinuity parameters

Because of complex discontinuity patterns, it is almost impossible to determine the permeability of rock masses if no proper testing methodology is used. As available in the literature, many empirical approaches to estimate the permeability of a rock mass have been proposed. There is no publication, however, that uses regression analyses and ANFIS (Adaptive Neuro-Fuzzy Inference System) modeling to determine the rock mass permeability. The purpose of the study is to develop various ANFIS and multiple regression models to estimate the rock mass permeability. To this end, a dataset including 453 cases with Lugeon test results and corresponding RQD (Rock Quality Designation), spacing of discontinuities and SCR (Surface Condition Rating) properties is employed. The data were obtained from granite, diorite, volcanic breccia, andesite and agglomerate rock masses from various dam sites and a coal mine in Turkey. Whole data were randomly divided into two parts for training and testing. Two different models were developed to estimate the rock mass permeability. The inputs of the first model are RQD and SCR (Model 1), and the inputs of the second model are discontinuity spacing and SCR (Model 2). Simple regression analyses indicate that there is no statistically meaningful relationship between the Lugeon values with discontinuity spacing and SCR. There is a statistically meaningful relationship, however, between the Lugeon values and RQD. Non-linear multiple regression analyses were implemented for two independent variables and a dependent variable because of the non-linear relationships between the inputs and the output. ANFIS was employed as a second non-linear tool to construct prediction models. According to the performance assessments of the developed models, both of the models and all of the sets are successful. ANFIS is a more successful tool than NLMR. These results show that the models developed are reliable enough and, if there is no direct test result, these models can be used in engineering projects.

Quantitative structure–activity relationship studies of dibenzo[a,d]cycloalkenimine derivatives for non-competitive antagonists of N-methyl-d-aspartate based on density functional theory with electronic and topological descriptors

To establish a quantitative structure–activity relationship for non-competitive antagonists of the N-methyl-d-aspartate receptor, 48 substituted dibenzo[a,d]cycloalkenimine derivatives were analyzed by principal components, a descendant multiple regression analyses, multiple non-linear regression and an artificial neural network. We propose non-linear and linear quantitative structure–activity models and interpret the activity of the compounds by the multivariate statistical analysis. Density functional theory with Becke's three-parameter hybrid function and Lee–Yang–Parr exchange correlation functional calculations were performed to define the structure, chemical reactivity and properties of the study compounds. The topological and the electronic descriptors were computed with ACD/ChemSketch and Gaussian 03W programs, respectively. The study shows that multiple regression and multiple non-linear regression analyses predict activity; however, predictions made with a 6-2-1 artificial neural network model were more accurate. This model gave statistically significant results and showed good stability to data variation in leave-one-out cross-validation.

Some emprical equations for predicting standard penetration test blow counts in clayey soils: a case study in Mersin, Turkey

The standard penetration test (SPT) is a widespread test used in situ. Soil mechanical properties such as grain-size content, moisture content, atterberg limits, activity of clay, and consistency of clay determine the SPT blow count. In addition, the SPT blow count reflects the soil mechanical and compressibility parameters. The purpose of this study is to determine the clayey soil states that determine the SPT blow count number; 220 data points derived from a foundation investigation of a sewerage project in Mersin City were used in the study. The Mersin sewerage project includes the sewerage station, central pumping station, and west pumping station in Mersin city, Turkey. The corrected SPT blow counts (N60) and corresponding parameters (fine-grain size percent, liquid limit, plastic limit, plasticity index, moisture content, activity, and consistency) of the clayey soil from these locations were used in a series of simple and nonlinear multiple regression analyses. Based on the analyses, the acceptable results have been determined by a simple regression analyses between the corrected SPT blow counts and the following input parameters: activity, moisture content (in percent), fine-grained percent (in percent). The simple regression analyses for N60-LL (in percent), N60-PL (in percent), and N60-PI (in percent) are considered unacceptable so these parameters are not evaluated as input parameters in the multiple regression analyses. Multiple regression analyses with two independent input parameters produces acceptable results. The optimal equation is determined with multiple regression analyses using activity and moisture content as the input parameters; this equation has a coefficient of determination of R2 = 0.78. The prediction capability of the developed equations is also acceptable. However, the prediction equations proposed in this study are developed with a limited number of data points. For this reason, a cross-check should be performed before using these empirical equations for design purposes.

Intermittent experimental study of a vertical ground source heat pump system

In this paper, the intermittent experiment of a vertical ground source heat pump (GSHP) system is investigated and the corresponding geo-temperature variations are studied. The performance of the GSHP system under intermittent operation and the comparisons of different intermittent modes are presented in the paper. The parameters of soil backfill material, air temperature and inlet volume flow rate are also investigated. Experimental results suggest that, due to the recovery in ground thermal energy in intermittent time, the heat exchange rate and the operation performance coefficient (COP) of the heat pump increases, and the compressor power decreases in the successive working. But an insufficient soil recovery time leads to a rapid decline of the performance parameters and the soil temperature. The temperature transports faster under large soil thermal conductivity conditions and the soil temperature decreases more quickly and recovers more slowly with larger inlet flow rate and lower weather temperature for different soil thermal diffusivities. Through multiple nonlinear regression analysis, a curve formula can be fitted to predict the soil temperature variations under intermittent operation of the ground source heat pump in winter, Dalian. It can be found that the soil temperature increases at an exponential function with each factor.

Study of the Interaction Model of PDC Cutters with Rock

The existing PDC cutters with rock interaction models does not match the actual drilling condition, because WOB can't be loaded on their equipment, the influence of the cutting velocity on the force of cutters doesn't be considered. A drilling experiment was carried out in the experimental device in which cutters can be loaded WOB. The relationship of cutting area, cutting speed, back dip angle of PDC cutter and rock performance with the force of PDC cutter were considered. The multiple nonlinear regression analysis were done on the basis of the experimental data, a new cutter-rock interaction model was established. The cutter area is the principal component influencing the force of cutters though the cluster analysis and principal component analysis. It is a logarithmic relationship between cutting velocity and the force of cutter, the theory of particle dynamics confirms this relationship.

Influence of Ground Motion Duration on Damping Reduction Factor

This article investigates the influences of the effective ground motion duration (GMD) on damping reduction factor. The GMD are associated with 25 Chi-chi earthquake ground motion records and harmonic sine wave. The study shows that damping reduction factor decreases with the increasing of the damping ratio, and decreases with the increasing of the effective duration of the ground motion and the number of cycles of harmonic excitation. A nonlinear multiple regression analysis based on the statistical mean values of the present study is employed, and a modified damping reduction factor considering the effects of GMD is suggested.

A stochastic penetration rate model for rotary drilling in surface mines

Principal Component Analysis (PCA) is used to determine the most effective parameters on the rock mass penetrability by considering their variance ratio in the first principal component. A model is developed for the prediction of rotary drills penetration rate using non-linear multiple regression analysis. Distribution functions for the effective parameters are calculated using measured data from two case studies. Applying the developed penetration rate model, a stochastic analysis is carried out using the Monte Carlo simulation. The proposed method provides a simple and effective assessment of the variability of the penetration rate model and its dependent parameters. Results showed that the PCA and Monte Carlo are suitable techniques for modeling and assessing the variability of rock mass penetrability parameters. According to the developed distribution model, with 90% of confidence level the penetration rate values range 0.2–2.5m/min, which shows the wide possible range of penetration rates for rotary drilling especially in sedimentary (limestone and sandstone bearing magnetite mineral of Golgohar mine) and Sarcheshmeh igneous porphyry rock masses.

The Effects of Webpage Navigation on Digital Reading Performance

Nineteen OECD and partner economies participated in PISA 2009 digital reading literacy study. Drawing data from this sample survey, this study seeks to examine from a comparative education perspective the efficiency and effectiveness of webpage navigation on digital reading performance. First, the webpages of an exemplary test item and the associated scoring guide are analyzed to elucidate the significance of efficient and effective webpage navigation in order to score high marks in the assessment. Second, the authors make use of the three webpage navigation indicators developed and validated by the PISA 2009 Consortium to measure the student webpage navigation behaviors during the assessment. Third, non-linear multiple regression analyses are undertaken across the economies to examine: (1) whether the relationships of the three webpage navigation indicators with student digital reading performance are linear or not, and (2) the proportion of digital reading variance that can be explained by the webpage navigation indicators. By examining the domain and shape of the graph of the “navigation-performance” relationships, lights are thrown on the efficiency and effectiveness of digital reading webpage navigation behaviors of the students of the participating economies. Implications of the study are discussed for PISA digital reading assessment in the future.

Impact of Fine Sediment on TSS and Turbidity in Retention Structure

A study of the impact of fine sediment on various water quality parameters in retention structure such as sediment basin was conducted on stagnant and flowing condition. 7 water quality parameters (pH, TSS, turbidity, DO, BOD, COD, ammonium nitrogen) were measured and classified according to the Interim National Water Quality Standard (INWQS) for Malaysia. Results show higher fine sediment concentration causes higher TSS and turbidity. Besides, results show TSS and turbidity were influenced by soil type for the same amount of fine sediment. Soil Siri Rasau 1 showed highest TSS and turbidity value followed by soil Siri Bungor and soil alluvium. A good positive correlation of TSS versus turbidity and has been investigated in this study. Multiple nonlinear regression analysis revealed that parameter TSS is dependent on time, total soil mass, fine soil mass and flow rate.

Analysis and modeling of moisture sorption behavior for antimicrobial composite protein films.

The WPI-NaCas-GLY antimicrobial film takes full advantage of the controlled release of active or antimicrobial agents as well as demonstrates a great potential for functioning as an alternative biodegradable polymer in practical applications. The moisture sorption kinetics of the film as an important carrier of active agents was investigated at various relative humidities (RH). The results indicated that the moisture sorption characterization and procedure of this film can be described well by the empirical Peleg model with higher confidence and concordance. The model could predict the film's moisture content at any time (Mt), the time to reach any given level of R (tR), the equilibrium moisture at any RH condition (Me), and isotherm trend based upon experimental data and modeled constants k(1), k(2), a, b, c, and d without giving consideration to their physical meaning. The water vapor transmission rate of the WPI-NaCas-GLY antimicrobial film increased exponentially with increasing RH due to its hydrophilicity, which was primarily caused by the presence of glycerol in a higher content. The results also suggested that aw predominately affects the film's Me values compared with the temperature factor by fixed nonlinear multiple regression analyses.

Predicting the production rate of diamond wire saws using multiple nonlinear regression analysis

In this paper, the prediction of production rate of diamond wire saws was performed. Performance measurements of diamond wire saws were conducted on 14 different carbonate rocks in mines located in Iran. Rock samples were collected from the mines for laboratory tests. Elasticity modulus, quartz content, and uniaxial compressive strength (UCS) were determined in the laboratory. Production values were correlated with the corresponding elasticity modulus, quartz content, and UCS. The sawing speed and rock properties were evaluated using multiple nonlinear regression analysis, and the developed model was validated by statistical tests and actual data of sawing speed for four new mines. It was concluded that the sawing speed of carbonate rocks using diamond wire saws can reliably be estimated using the developed model.

Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R2) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements.

A New Empirical Correlation for Sonic Simultaneous Flow of Oil and Gas through Wellhead Chokes for Persian Oil Fields

Chokes are frequently installed in wells to restrict flow rates to desired levels. In most of the works involving sonic simultaneous flow of oil and gas through chokes, each of oil flow rate, choke upstream pressure, choke size, and producing gas to oil ratio could be calculated by having three other mentioned parameters. In this study, 132 production test data from 15 Persian oil fields with a wide range of parameters were used to develop a new choke performance relationship. The reported production test data included oil flow rate, choke upstream pressure, choke size, gas to oil ratio, and gas and oil specific gravities. After checking four forms of correlations against data with the aid of non-linear multiple regression analysis, it was observed that choke performance relationship is fairly insensitive to gas and oil specific gravities. An empirical correlation that relates oil flow rate to choke upstream pressure, choke size, and gas to oil ratio is presented. For the Persian oil fields, predicted oil flow rates by the new correlation are in good agreement with measured data. Statistical error analysis shows that results are statistically superior to those predicted by other published correlations considered in this work. In addition, it is emphasized that a linear relationship exists between choke upstream pressure and oil flow rate.

A multi-criteria optimization and decision-making approach for improvement of food engineering processes

The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demonstrated using experimental data obtained on osmotic dehydration of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses), namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality). Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP) and the Tabular Method (TM), were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.

A multi-criteria optimization and decision-making approach for improvement of food engineering processes

The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demonstrated using experimental data obtained on osmotic dehydration of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses), namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality). Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP) and the Tabular Method (TM), were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.

Fire spread prediction for deciduous forest fires in Northern Thailand

Predicting fire spread rates is essential in planning and deciding whether to conduct prescribed fires or suppressing forest fires. This study was conducted with the objective of developing a fire spread model for deciduous forest fires by using a simple statistical model. Test fires were conducted under a range of weather and fuel conditions to gather quantitative data on fire spreading. A series of 80 experimental fire plots were set in deciduous forests in the Northern Thailand during the forest fire seasons from 2008-2009. The factors influencing the fire spread rate, i.e., weather, fuel, and topography conditions, were measured to model the fire spread. According to the burning experiments, the fire spread rate was 0.51-2.55 m/min. Multiple nonlinear regression analyses of slope terrain, fuel load, and moisture content of fuels were found to be able to accurately predict the fire spread rate at a confidence level of 25-88%. The developed model can be applied to deciduous forest fuels in other regions of Thailand. However, its use should be restricted to typical weather conditions.

Sensitivity Analysis of Edge Sealing Process Parameters of Vacuum Glazing Panel

The edge sealing process of vacuum glazing panel manufacture is one that requires high reliability in order to maintain a vacuum state between two sheets of glass. In this study, a method of melting and sealing two sheets of glass using the hydrogen mixed gas torch during edge sealing was conducted. During melting and sealing, the deflection effect of the edge part is affected by process parameters such as flow rate of hydrogen mixed gas, moving speed of torch, and distance between the torch and the glass, among others. In order to analyze the correlative relationship of the shape prediction and the sealing shape of the edge part according to the process parameters, data was obtained by conducting sealing experiments and setting shape parameters for the lower melting part. A mathematical experiment equation that can predict the shape of the lower edge part according to the process parameters was developed by conducting nonlinear multiple regression analysis based on the data obtained for the shape parameters. In addition, sensitivity analysis according to the changes in the process parameters was carried out and the effects of the process parameters on the edge shape parameters were analyzed by using the developed mathematical equation.

Multiple Nonlinear Regression Analysis for the Prediction of Macroscopic Elastic Properties on Composite Containing Interphase

A convenient method to predict the macroscopic elastic performance of composite containing interphase was proposed in this paper. Firstly, a 3-D three-phase micromechanical model with randomly distributed fibers was established with the Moving Window Method (MWM), and the macroscopic elastic properties of T300/914C were predicted using energy method. Secondly, the multiple nonlinear regression correlation between the macroscopic elastic properties and micromechanical characteristic parameters of the interphase was established based on numerical data. Finally, the macroscopic elastic properties of T300/914C containing interphase were predicted using the regression model. Results indicate that the relative error for the longitudinal modulus is within ±1% while it is within ±3.5% for the transverse modulus, and shear modulus.

Compressive strength and chloride resistance of metakaolin concrete

In the present research work, investigations were carried out to improve the performance of concrete in terms of strength and resistance to chloride-ion-penetration by incorporating metakaolin (MK) as mineral admixture in concrete. Parametric study was carried out by considering w/cm ratio, various percentage of MK and age of concrete as parameters to understand the effect of each parameter. The study was conducted for different water-to-cement metakaolin ratio (w/cm) ratios of 0.32, 0.35, 0.4 and 0.5. The MK proportion was varied from 0 to 15% with an increment of 5% and ages of concrete from 3 to 90 days were considered and experiments performed accordingly. The effects of above said parameters on the various properties of concrete such as workability, compressive strength, chloride penetrability (Rapid chloride permeability test as per ASTM C 1202), pH of concrete and depth of penetration of chloride ions were investigated, and the results of MK concrete were compared with the conventional concrete. From the results, it was observed that MK concrete showed greater strength for higher w/cm ratios (0.4 and 0.5) and its resistance to chloride ion penetration was more or less consistent for all w/cm ratios and the optimal amount of MK resulted significant reduction in chloride ion penetration. A Multiple non-linear regression analysis was used to develop a statistical model to predict the strength and found to have good correlation between the observed and predicted values. It was concluded that the concrete developed in this study have significant potential for use on real time projects.

Calibration of an on-line sensor for measurement of topsoil bulk density in all soil textures

An on-line sensor for the prediction of bulk density (BD) has been previously developed, and calibrated for light textured soils. This study aims to expand the implementation of this sensor for all soil types, including clay soils. The on-line sensor consists of a single ended shear beam load cell to measure draught (D), a visible and near infrared (vis–NIR) sensor to measure moisture content (MC), and a wheel gauge equipped with a draw wire linear sensor to measure depth (d). This sensor was used to collect data from 14 fields across four European countries (Denmark, Czech Republic, the Netherlands and the UK) with different soil textures. The three measured parameters were substituted into a previously developed model to calculate BD. The calculated BD for 333 samples was compared with BD measured with a core sampling kit to validate the system and to calculate correction factors (CF) valid for each additional soil texture. A multiple linear regression (MLR), nonlinear multiple regression (NMR) and artificial neural network (ANN) analyses were carried out to establish models to predict CF, as a function of average field MC and soil texture fractions.For clay soils, the correlation between the measured and on-line predicted BD was satisfactory (R2=0.54–0.69), and suggested a large negative CF>−0.248Mgm−3. For other soil types with lighter soil textures, accuracy of measurements varied (R2=0.51–0.96), with lower CF values than those calculated for clay soils and ranged between negative and positive values (−0.007Mgm−3<CF<0.079Mgm−3). The ANN analysis resulted in the best model to predict CF as function of MC and soil texture fractions (R2=0.96), which allowed the utilisation of the on-line measurement system for any field having any texture and average MC. The on-line BD sensor was shown to be capable of predicting field dry BD rapidly for a large number of samples, as compared with traditional methods.