Multiple Nonlinear Regression Analysis Research Articles

Development and verification of rutting prediction model based on variable frequency load test

ABSTRACT An innovative rutting test method was developed based on the existing rutting prediction model to improve the rutting prediction model used in the asphalt pavements. The loading mould was improved based on test requirements and equipment characteristics, and small-scale and full-scale variable frequency load rutting tests were conducted. Nonlinear multiple regression analysis was conducted on the test results using numerical analysis software, converting driving speed into loading frequency and incorporating it into the rutting prediction model. This resulted in a permanent deformation prediction model for asphalt mixtures, incorporating factors such as temperature, pressure, loading cycles, loading frequency, and pavement thickness. Fourteen highways were selected as validation sections. The validation results showed that the average error of the prediction model established in this study was 15.48%, compared to 24.42% and 27.94% for the other two models. For different grades of highways, the load frequency parameter can distinguish the rutting conditions under different driving speeds, making the rutting prediction model established in this study more accurate compared to measured values.

A new strategy for manufacturing, modeling, and optimization of 3D printed polylactide based on multiple nonlinear neuro regression analysis and stochastic optimization methods

Although there are many studies related to design, modeling, and optimization of fused deposition modeling (FDM) process parameters in the literature, the absence of a systematic approach to increase the reliability of model selection and optimization results is an important shortcoming that must be addressed. To make up for this deficiency, a new strategy was proposed to obtain desired quality on mechanical properties by adjusting FDM process parameters. This attempt involves manufacturing, modeling, and optimization point of view. The D-optimal method was employed to form experiment set, including process parameters. A hybrid approach neuro regression combining artificial neural network (ANN) and regression analysis together was used for modeling of FDM process. The most significant advantage of the neuro-regression approach compared to ANN is that the mathematical models can be used directly without needing any transformation. This is not possible in neural networks and, therefore, significantly limits and complicates the use of models obtained using ANN. The present study aims at optimization of the FDM process parameters, including infill density, infill pattern, layer thickness, and print speed on ultimate strength, fracture strength, and fracture strain for polylactide (PLA). In this regard, modified versions of the optimization algorithms Differential Evolution, Nelder Mead, and Simulated Annealing were used to find the best or elite designs. Linear or nonlinear models consisting of polynomial, trigonometric, and logarithmic expressions and their hybrid forms were employed to define the strength and strain behavior of PLA. It is concluded that (a) implementations of the optimization algorithms provide a 19% improving the minimum strain value if it is compared with the experimental results, (b) infill pattern types ( x2) were found as honeycomb, triangle, and cubic for the designs in terms of maximum fracture strength, minimum strain, and maximum ultimate tensile strength respectively, (c) many alternatives near optimum local designs could be obtained based on Nelder Mead algorithm for fracture strength and ultimate strength parameters. Thus, this allows work in a wide range of applications without depending on a single result for production.

Estimation of foam (surfactant) consumption in earth pressure balance tunnel boring machine using statistical and soft-computing methods

The use of foam, as the most economical soil conditioning technique, in earth pressure balance tunnel boring machine (EPB-TBM) tunneling projects has significant effects on operation efficiency, excavation cost, and operation time. This study mainly focuses on developing models to predict the foam (surfactant) consumption. For this purpose, five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in Iran. This database includes the most effective machine operational parameters and soil geomechanical properties on the foam consumption. Multiple linear regression analysis, multiple non-linear regression analysis, M5Prime decision tree, artificial neural network, and least squares support vector machine techniques are used to construct the models. To evaluate the performance of developed models, three performance evaluation criteria (including normalized root mean square error, variance account for, and coefficient of determination) are used based on the training and testing datasets. The results show that the developed models have high performance and their validity is guaranteed according to the testing dataset. Furthermore, the M5Prime model, which demonstrates the best performance compared to other models, is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro, Iran. After conducting an excavation operation in this station and comparing the results, it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13%. Therefore, the developed models, particularly M5Prime model, can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.

Statistical Model for Compressive Strength of Metakaolin Concrete

In the present research work, investigations were carried out to improve the performance of concrete in terms of strength by incorporating metakaolin (MK) as mineral admixture in concrete. Parametric study was carried out by considering w/c ratio, various percentage of MK and age of concrete as parameters to understand the effect of each parameter. The study was conducted for different w/c 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 age 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 and pH of concrete were investigated, and the results of metakaolin concrete were compared with the conventional concrete. From the results, it was observed that MK concrete showed greater strength for higher water cement ratios (0.4 & 0.5). 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.

A NONLINEAR REGRESSION MODEL FOR EARLY LOC ESTIMATION OF OPEN-SOURCE KOTLIN-BASED APPLICATIONS

Context. The early lines of code (LOC) estimation in software projects holds significant importance, as it directly influences the prediction of development effort, covering a spectrum of different programming languages, and open-source Kotlin-based applications in particular. The object of the study is the process of early LOC estimation of open-source Kotlin-based apps. The subject of the study is the nonlinear regression models for early LOC estimation of open-source Kotlin-based apps. Objective. The goal of the work is to build the nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps based on the Box-Cox four-variate normalizing transformation to increase the confidence in early LOC estimation of these apps. Method. For early LOC estimation in open-source Kotlin-based apps, the model, confidence, and prediction intervals of nonlinear regression were constructed using the Box-Cox four-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating multivariate normalizing transformations, account for the dependencies between variables in non-Gaussian data scenarios. As a result, this method tends to reduce the mean magnitude of relative error (MMRE) and narrow confidence and prediction intervals compared to models utilizing univariate normalizing transformations. Results. An analysis has been carried out to compare the constructed model with nonlinear regression models employing decimal logarithm and Box-Cox univariate transformation. Conclusions. The nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps is constructed using the Box-Cox four-variate transformation. Compared to the other nonlinear regression models, this model demonstrates a larger multiple coefficient of determination, a smaller value of the MMRE, and narrower confidence and prediction intervals. The prospects for further research may include the application of other data sets to construct the nonlinear regression model for early LOC estimation of open-source Kotlin-based apps for other restrictions on predictors.

Modelling fuel oil transformation on geographically different seacoasts and assessing their self-cleansing capacity.

The present paper considers the results of long-term (up to 17years) in situ and laboratory research carried out on oiled French, Spanish, and Russian seacoasts. The objective of this research is to quantify the influence of geographical factors on the rates of natural transformation of the heavy fuel oil stranded ashore and to develop an empirical statistical model in order to evaluate the self-cleansing capacity of the coastal environment. In a number of field campaigns, 363 samples of weathered oil slicks and tar balls have been collected and analysed with the use of thin-layer chromatography combined with optical and gravimetric methods. The results obtained have been subjected to multiple nonlinear regression analyses. It has been shown that heavy fuel oil natural attenuation is more active in continental or estuarine environments influenced by nutrient-rich freshwater runoff and characterised by a higher number of sunny days, solar irradiation, and large temperature fluctuations. On the oceanic coasts, especially in sectors with low hydrodynamic energy, these processes take more time. The resulting model allows for the identification and mapping of the most vulnerable seacoasts, characterised by a low potential to degrade oil pollution. This information may be used in the contingency plans in order to optimise clean-up techniques and associated costs.

Tensile and shear capacity of post-installed chemical adhesive anchors in lightweight concrete

Due to construction efficiency and performance, chemical anchors have been widely applied to long-span or high-rise structures or infrastructures. This study investigates the capacity of post-installed anchors in concrete with lightweight aggregates. Extensive static experiments are conducted to investigate tensile and shear capacities of chemical adhesive anchors in lightweight concrete. Both confined and unconfined tensile conditions are considered. In total, 102 tests are performed with various anchor diameters (12–30 mm), embedment depths (80–300 mm), and types of concrete. The test results show that cone breakout and combined failures dominate the unconfined tensile tests, which have about 40% capacity for the anchors in normal-weight concrete. Chemical adhesive anchor failure models in lightweight concrete are also summarized with respect to the input parameters. Finally, multiple nonlinear regression analyses are further performed, and revised empirical and analytical equations are proposed for lightweight concrete to accommodate different failure modes.

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Nonlinear multiple regression analysis for predicting seasonal streamflow using climate indices for New South Wales

Assessment of Bohme surface abrasion resistance of natural stones by simple and nonlinear multiple regression methods

Natural stones are widely used in the construction sector and the physico-mechanical features are very important for them in terms of areas of use. Abrasion is an important phenomenon for causes to decrease in aesthetical and mechanical properties. The Bohme surface abrasion (BSA) test is the widely used test method to determine abrasion resistance, but it is time-consuming, very difficult, and expensive to apply. Because of such difficulties, this study has been conducted to indirectly determine the BSA value of natural stones by some practical methods. For this purpose, BSA, Point load index (Is50), Shore Schlerescope hardness (SSH), Brinell hardness (HB), Rock impact hardness number (RIHN), Coefficient of rock strength (CRS), Crushability index (CI), Impact strength index (ISI), Water absorption (WA), Unit weight (UW), and Porosity (Pg) properties of a total of up 119 rocks, mainly of Turkish rocks, were determined. The main objective of this article is to develop some estimation models for the BSA of different rocks. Two different methods have been utilized in the modeling of data, including simple regression analysis (SRA), and nonlinear multiple regression analysis (NMRA). In addition to the SRA performed using all data sets, the SRA analysis was deepened by classification according to origin, porosity, and strength. A large number of estimation equations were obtained by NMRA performed by applying three different models. The actual BSA values obtained from the laboratory study and the BSA values obtained from the models analyzed with SPSS software were compared using ANOVA analysis and various graphics. The most successful SRA equations derived using the entire dataset were obtained with HB, Is50, and SSH parameters, respectively. In general, there is a significant correlation between various rock properties and BSA, except CRS and CI. For NMRA models constructed with at least three independent variables, the highest predictive power belongs to the equations using (Is50 / HB / UW), (Is50 / HB / CRS), and (Is50 / UW / WA / Pg) parameters, respectively. Rock abrasion has a complex nature. The results of this study strongly support that it is an accurate approach to seek a solution to this complex problem by using parameters such as hardness, strength, and physical properties in the same model.

Thermal hazard assessment of tert-butyl perbenzoate using advanced calorimetric techniques and thermokinetic methods

Tert-butyl perbenzoate (TBPB) is a common organic peroxide initiator in the polymer sector, and its thermal behavior and potential dangers are explored in this quantitative study. Advanced calorimetric techniques and thermokinetic models were used to examine the thermal stability and breakdown features of TBPB. During the first stage of the TBPB synthesis, the process safety analysis revealed a very exothermic reaction. Peak temperatures for TBPB thermal decomposition ranged from 133.37 to 156.64 °C, with heat release values of roughly 1279 ± 135 J/g, as determined by differential scanning calorimetry and thermogravimetric analysis. Apparent activation energies ranged from 78.78 to 133.92 kJ/mol in a thermokinetic analysis employing the Kissinger, Starink, ASTM E698, Friedman, Kissinger-Akahira-Sunose, and Flynn-Wall-Ozawa models. Based on multiple nonlinear regression analysis, a two-step reaction mechanism was postulated, one involving an autocatalytic reaction and the other involving a reaction of the nth order. In order to evaluate the thermal dangers related with TBPB, the self-accelerating decomposition temperature (SADT) and thermal risk index (TRI) were calculated. Based on TRI calculations, TBPB was assessed as having low thermal risk, with SADT values ranging from 40 to 60 °C depending on package size. These numerical findings add to our knowledge of the thermal risks posed by TBPB and shed light on how best to handle, store, and transport the compound for use in industrial settings.

Experimental and modeling investigation of freezing behavior for frozen sand molds

The essence of the freezing process in frozen sand molds is the heat transfer issue within the water-containing porous medium. In this paper, the evolutionary mechanism of the water-ice phase interface in frozen sand molds was studied through simulations and experiments at the mesoscale. Firstly, the evolutions of the phase field and temperature field during the unidirectional freezing process of frozen sand molds with different pore structures were investigated through numerical simulations. Then, utilizing the low-temperature freezing in-situ characterization platform, the nucleation mechanism of ice crystal bonding bridges between sand particles of varying materials was explored. The influence of sand mold characteristics on the freezing behavior of frozen sand molds was evaluated by the unique variable principle. Additionally, power function relationships for freezing time of frozen sand molds with different scales and materials were established, enabling the prediction of the time required for complete freezing of frozen sand molds. According to the results of multiple nonlinear regression analysis, the model showed reliable accuracy in predicting the time for fully frozen. This work will provide a valuable reference for further research on the engineering application of frozen sand molds.

Computational Simulation of the Flow Induced by Water Leaks in Pipes

Despite recent advances in leak detection in water supply systems, previous studies focused mainly on material aspects or orifice opening characteristics. This study evaluated geometric, hydraulic, and hydrodynamic factors influencing on water leaks using three-dimensional (3D) computational fluid dynamics (CFD) simulations. The simulated discharge coefficient (Cd) had a maximum absolute deviation of 8% compared with experimental data. The results indicated that the variation of Cd as a function of the diameter and orifice size can be attributed to the effect of tube curvature on flow convergence through the orifice. The statistical analysis concluded that the orifice-to-pipe diameter ratio d/D is the best dimensionless parameter that explains this phenomenon, which agreed with the analyses of the streamlines and turbulent eddy dissipation rate in the simulations. Other important dimensionless parameter are the ratio of the pressure head differential at the orifice to the pipe diameter ΔH/D, Reynolds number (R), and the ratio of the pressure head differential at the orifice to the wall thickness ΔH/e, in order of relevance. A set of empirical correlations was elaborated through a nonlinear multiple regression analysis with Reynolds numbers ranging between 12,000 and 52,000, and pressure head differential ranging from 1 to 45 m. This study advances the knowledge of cross-flows through orifices, and the proposed correlations potentially can improve the prediction of water leaks in pipes.

New empirical scaling equations for oil recovery by free fall gravity drainage in naturally fractured reservoirs

Gas-oil gravity drainage is a recognized major contributor to production in fractured reservoirs. While various empirical and analytical methods have been proposed to model this process, many of them contain assumptions that are questionable or require parameters that are not accessible at the field level. The aim of this work is to provide new, easy-to-use scaling equations for estimating the recoverable oil through gravity drainage in naturally fractured reservoirs, considering the effects of resistance capillary pressure. To accomplish this, data from four oilfields undergoing gravity drainage, including rock properties (eight sets), block height (three sets), and fluid properties (four sets), were used to generate a wide range of recovery curves using a single porosity numerical simulation model. Aronofsky's and Lambert's functions were then utilized to match the generated recovery curves. Statistical analysis revealed that the Aronofsky's function is more accurate in replicating the recovery patterns, while the Lambert's function tends to overestimate the early-time oil recovery and underestimate the oil recovery at a later stage in the majority of cases. A sensitivity analysis was subsequently performed, revealing that parameters such as absolute permeability, viscosity of oil, height of block, gas and oil density, characteristics of relative permeability and capillary pressure curves and interfacial tension (IFT) influence the amount of time taken to achieve the final recovery. Of these parameters, absolute permeability has the most significant effect on the amount of time needed to attain the final recovery, while the effect of difference between oil and gas densities is the lowest. Consequently, two different expressions were developed using nonlinear multiple regression analysis of simulated gravity drainage data which can be combined with the Aronofsky model to substitute the rate convergence constant. The new scaling equations include the effects of capillary pressure and other relevant factors in gravity drainage simulations. Both forms show satisfactory accuracy, as evidenced by the statistical parameters obtained (R2 = 0.99 and MSE = 0.0019 for both established correlations). The new correlations were verified using a wide range of oilfield data and are expected to provide a better understanding of the recovery process in naturally fractured reservoirs.

Optimization and Improvement of the Projection Welding of Nut Based on Regression Analysis

Since it has the advantages of high production efficiency, no shunting effects, and the ability to arrange solder joints on narrow locations without the limitation of point distance, projection welding of nut has been widely used in the automotive industry. This paper presents a new welding quality prediction and analysis framework by using regression analysis techniques. The relationship between the welding parameters (welding time, welding current and electrode force) and the Pull-out load of the M6 welded square nut and the 1.5 mm thick pickled hot-rolled steel was obtained by multiple nonlinear regression analysis. The influence and interaction of welding parameters on the Pull-out load are discussed. Using this regression model, a better combination of process parameters can be obtained. The mechanical properties, microstructure and microhardness of welded joints were also investigated.

Statistical investigation of the effects of different origin aggre-gate properties on the mechanical properties of concrete

Aggregates are one of the most important components of the concrete mix and the aggregate properties have a great influence on the properties of the hardened concrete. From this point of view, the contribution of the nature of the aggregate to the general mechanical behavior of concrete should be better understood and investigated. The main purpose of this study is to present practical and useful equations for the rapid evaluation of the basic properties of concrete, especially during the preliminary design phase. A series of laboratory studies were conducted to determine the effects of twenty aggregates with very different origins and properties on various properties of concretes. Laboratory tests with the same or similar principles as aggregate rocks have been repeated for concrete samples produced using these aggregates. Nine SRA equations were generated to directly predict similar concrete properties with the help of aggregate properties. Equations with very high coefficients of determination were produced between various aggregate properties and concrete properties except for the SHH property. In addition, nonlinear multiple regression analysis (NMRA) was used for twenty-four equations to predict some basic properties of concrete (strength, abrasion, tensile, and impact). The performances of the derived equations were evaluated with a statistical tool developed by the author. Accordingly, the models with the strongest prediction capacity were obtained for STSC, LAC, CSC, and, IEC respectively. Results from NMRA showed that equations with the highest coefficient of determination were obtained with Model-3 for CSC (Equation 15), and STSC (Equation 18), and Model-1 for LAC (Equation 22), and IEC (Equation 31). Concrete has a complex structure that is affected by many parameters. In accordance with this complex nature, the approach to predict the main concrete properties with nonlinear multiple methods by including similar aggregate properties as well as non-destructive methods representing concrete/aggregate (hardness, ultrasonic pulse velocity and physical properties) has been successful. The extremely high coefficients of determination, ranging from 0.81 to 0.96, obtained with NMRA, indicate that basic concrete properties can be strongly predicted by aggregate properties and some concrete properties that can be tested non-destructively.

Statistical Analysis Approaches in Scour Depth of Bridge Piers

A local scour is the removal of bed material from around the pier of the bridge. This bed removal is considered a big problem and is of great concern for hydraulic engineers. They should find economic solutions for this problem. The exaggerated local scour around bridge piers leads to many problems for the whole bridge structure, such as stability problems that may lead to the bridge's destruction. This paper aims to verify the scour depth around different shapes of uniform bridge piers for different flow conditions than those done by previous researchers using different prediction models. Where the consistency of previous experimental investigations is verified by multiple nonlinear regression analysis (MNLR), Gene Expression Programming (GEP) and Artificial Neural Network (ANN) models. In the comparison of values that were measured and predicted by the four models (CFD, MNLR, ANN, and Gene), it is seen that the ANN model has the ability to predict the Ys/b values higher than other models used in relation to the measured values. This makes the ANN model superior in predicting the Ys/b value over the other used models, followed by the Gene model. In comparison, the values of the R2and RMSE for the four models that were used in this study, for the Ys/b model using the ANN had a value of 0.9978 and 0.0147, respectively, while those for the Ys/b model using the Gene model were 0.9800 and 0.0375, respectively. Doi: 10.28991/CEJ-2023-09-01-011 Full Text: PDF

Spatio-temporal Variation of Meteorological Influence on PM2.5 and PM10 over Major Urban Cities of Bangladesh

A detrended seasonal analysis on a 6-year (2013–2018) dataset of daily Particulate Matter (PM) concentration and meteorological parameters is performed to understand the spatio-temporal variation of PM and the seasonal influence of meteorological factors on PM pollution over 6 major urban cities of Bangladesh. Cross-correlation and multiple non-linear regression (MNLR) of air quality and meteorological data were used to explore the meteorology-PM interactions and their spatio-temporal variability. Meteorological influence on PM was found to be stronger in the southern part of the country relative to the northwestern part. MNLR analysis implied that meteorological parameters could explain up to 39% of daily PM variability during high pollution days. The deposition effect of relative humidity was prominent during the premonsoon season, while rainfall impact becomes dominant in the monsoon season, specifically in the northeastern region. Wind speed was observed to have a dilatory effect on PM variation, although wind seemed to carry sea aerosol in southern regions. In addition, the northwestern wind appeared to contribute to PM rise in most urban areas by carrying PM loading. In winter, the low temperature was found to favor PM accumulation, while in monsoon, high temperature causes PM rise, possibly by assisting atmospheric secondary aerosol formation. Solar radiation positively influenced atmospheric PM formation, and the influence was stronger in the northwestern region. It is shown that meteorological parameters influence the seasonal variability of PM, but the extent of this influence varies depending on temporal and regional factors.

Impact of rapid Arctic sea ice decline on China's crop yield under global warming

AbstractFood is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide.

Efektivitas Distribusi Pupuk Bersubsidi untuk Usahatani Padi Saat Pandemi Covid-19 di Kecamatan Baula Kabupaten Kolaka

This study aims to analyze the effectiveness of the distribution of subsidized fertilizers for rice crops during the COVID-19 pandemic. The study was conducted in Baula District, Kolaka Regency from October 2021 to December 2021. The population in this study were rice farmers. Determination of the sample is done by simple random sampling technique. The number of samples selected as many as 91 respondents. Data analysis used two approaches, namely a qualitative approach and a quantitative approach by calculating the effectiveness of the distribution of subsidized fertilizers based on six precise indicators, consisting of price accuracy, quantity, time, place, type and quality accuracy. Meanwhile, to determine the effect of land area, number of seeds, fertilizer, labor and the effectiveness of fertilizer distribution on rice production, multiple non-linear regression analysis was used. The results of the study show that the distribution of subsidized fertilizers in Baula District, Kolaka Regency was not effective before the pandemic, and very ineffective during the Covid-19 pandemic. Land area, number of fertilizer, seeds, and number of labor have a positive and significant effect on lowland rice production, while the effectiveness of subsidized fertilizer distribution has a positive and insignificant effect on lowland rice production.Keywords: distribution; subsidized fertilizer; Covid-19 pandemic