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

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CONSTRUCTION AND BUILDING MATERIALS 2024年 411卷

作者： Teymen, Ahmet Nigde Omer Halisdemir Univ Dept Min Engn TR-51240 Nigde Turkiye

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

关键词： Bohme surface abrasion Simple regression nonlinear multiple regression Natural stones Rock properties

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Simple models for the estimation of shearing resistance angle of uniform sands

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NEURAL COMPUTING & APPLICATIONS 2013年第1期22卷 111-123页

作者： Sezer, Alper Ege Univ Dept Civil Engn TR-35100 Izmir Turkey Ege Univ Insaat Muhendisligi Bolumu TR-35100 Izmir Turkey

Angle of shearing resistance is the key for the strength analysis of soils, since this parameter is commonly used for the description of shear strength of a soil. Many factors including soil mineralogy, particle shape, grain size distribution, void ratio, organic content, as well as water existence are effective on this parameter. Use of shear box tests for the determination of angle of shearing resistance is prevalent in geotechnical engineering practice, since it is rather successful in identification of shear strength of granular media. However, for cases in which shear box tests exhibit unreliable outcomes, alternative methods for the determination of this parameter could be beneficial. In this investigation, a number of nonlinear multiple regression, adaptive neuro-fuzzy inference systems, and artificial neural network (ANN) models are employed for the estimation of estimating the angle of shearing resistance of uniform sands by means of several grain size distribution, particle shape, and density parameters. Data including results of 132 shear box tests, particle shape identifiers, and grain size distribution parameters on uniform sands are used in the models. In training sessions, results of 104 tests are selected randomly and the results of remaining 28 tests are considered for testing sessions. The results revealed that the performance of a simple ANN architecture is sufficient for pre-evaluation of shearing resistance angle of uniform sands with the help of selected parameters. Since generalization of these models necessitates vast amount of experiments, great care should be dedicated on the assessment of similarity of training as well as testing data.

关键词： Artificial neural networks ANFIS nonlinear multiple regression Angle of shearing resistance Particle shape

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Prediction of cementitious grout take for a mine shaft permeation by adaptive neuro-fuzzy inference system and multiple regression

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ENGINEERING GEOLOGY 2017年 228卷 238-248页

作者： Oge, Ibrahim Ferid Mugla Sitki Kocman Univ Fac Engn Dept Min Engn TR-48000 Kotekli Mugla Turkey

Cement grouting is a common technique implemented for permeation and ground improvement in civil and mining engineering projects. Basically, it is the injection of cement and water mixture into a fractured rock mass. Due to the presence of water bearing and permeable rock mass, permeation grouting was applied prior to the shaft sinking operation in an underground mine, located in Soma coal basin, Turkey. The Drill-Grout-Drill (DGD) method was used in permeation grouting for a flood prone mine shaft project with a circular pattern covering the proposed shaft opening. Data collection was mainly based on recording borehole data, however, during shaft sinking, field observations were continued to check and validate data, especially the rock mass properties. Widely used classification systems, such as RQD and RMR discontinuity condition rating were selected to define rock mass parameters. The rock mass parameters and the grout take data were pre-processed and cleaned to be used as input for multiple regression modelling and Adaptive Neuro Fuzzy Inference System (ANFIS). Linear, nonlinear, and Box-Cox multiple regression models provided accurate results. ANFIS with subtractive clustering and with manual dictation resulted in improved predictions compared to the regression analysis. Since grouting has great complexity and dependence on numerous variables, particular limitations and omissions had to be defined within the scope of the research. All influential factors could not be interpreted. The methodology and variable conditions are the main novelties of this study and enhance the implementation of the method specifically in the mine project where the study was carried out.

关键词： Grouting Grout take Rock mass ANFIS nonlinear multiple regression

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A power-law multivariable regression equation for salt intrusion length in the Bouregreg estuary, Morocco

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MARINE GEORESOURCES & GEOTECHNOLOGY 2020年第4期38卷 417-422页

作者： Haddout, Soufiane Ibn Tofail Univ Dept Phys Fac Sci BP 133 Kenitra Morocco

Prediction of salt intrusion length in estuaries is a challenge for managers as well as scientists in this field. Several numerical and empirical models have been developed for the prediction of salt intrusion length in recent decades. However, all these models require large data set on estuary geometry, tide, stratification turbulence which demands experimental cost and time, and which is not always available as in our case. Thus, for reducing the complexity of analysis, a new simple equation was derived to predict the salinity intrusion length using nonlinear multivariable regression in the Bouregreg estuary, Morocco. The equation relates salt intrusion length with freshwater discharge and tidal range using a power law. The salt intrusion length predicted by the developed equation was in good agreement (R-2 = 0.72) with that obtained using a numerical salinity transport model (HEC-RAS software "Hydrologic Engineering Center River Analysis System"). This simple formula is completely transparent and practical for Bouregreg estuary, allowing direct assessment of the parameters on the salt intrusion. Therefore, the proposed model can either be used to predict the salt intrusion for a given freshwater discharge and tidal range, or can used as a tool to design the minimum daily discharge to regulate the salt intrusion into the estuary, thereby providing assistance for management plans.

关键词： Bouregreg estuary salt-intrusion length nonlinear multiple regression numerical model (HEC-RAS)

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Glyphosate and AMPA adsorption in soils: laboratory experiments and pedotransfer rules

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2016年第6期23卷 5733-5742页

作者： Sidoli, Pauline Baran, Nicole Angulo-Jaramillo, Rafael Bur Rech Geol & Minieres 3 Ave Claude GuilleminBP 36009 F-45060 Orleans 2 France Univ Lyon 1 Lab Ecol Hydrosyst Nat & Anthropises UMR5023 CNRS ENTPE 3 Rue Maurice F-69518 Vaulx En Velin France

Adsorption of the herbicide glyphosate and its main metabolite AMPA (aminomethylphosphonic acid) was investigated on 17 different agricultural soils. Batch equilibration adsorption data are shown by Freundlich adsorption isotherms. Glyphosate adsorption is clearly affected by equilibration concentrations, but the nonlinear AMPA adsorption isotherms indicate saturation of the adsorption sites with increasing equilibrium concentrations. pH(CaCl2) (i.e. experimental pH) is the major parameter governing glyphosate and AMPA adsorption in soils. However, considering pH(CaCl2) values, available phosphate amount, and amorphous iron and aluminium oxide contents by using a nonlinear multiple regression equation, obtains the most accurate and powerful pedotransfer rule for predicting the adsorption constants for these two molecules. As amorphous iron and aluminium oxide contents in soil are not systematically determined, we also propose a pedotransfer rule with two variables-pH(CaCl2) values and available phosphate amount-that remains acceptable for both molecules. Moreover, the use of the commonly measured pH(water) or pH(KCl) values gives less accurate results compared to pH(CaCl2) measurements. To our knowledge, this study is the first AMPA adsorption characterization for a significant number of temperate climate soils.

关键词： Glyphosate AMPA Sorption nonlinear multiple regression Chromic cambisol

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Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2016年第6期13卷 1453-1464页

作者： Faradonbeh, R. Shirani Armaghani, D. Jahed Majid, M. Z. Abd Tahir, M. Md Murlidhar, B. Ramesh Monjezi, M. Wong, H. M. Tarbiat Modares Univ Fac Engn Dept Min Tehran 14115143 Iran Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Civil Engn ISIIC Construct Res Ctr Skudai 81310 Johor Malaysia Islamic Azad Univ South Tehran Branch Fac Engn Tehran Iran Univ Malaya Dept Mech Engn Kuala Lumpur Malaysia

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

关键词： Blasting Ground vibration Gene expression programming nonlinear multiple regression

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

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年第1期26卷 1263-1280页

作者： Chen, Di Sun, Qizhen Ocean Univ China Qingdao Peoples R China Natl Marine Environm Forecasting Ctr 8 Dahui Temple Rd Beijing Peoples R China

Food 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 developm

关键词： Arctic sea ice Arctic Oscillation (AO) Crop yield nonlinear multiple regression

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Predicting the Height of the Water-conducting Fractured Zone Based on a multiple regression Model and Information Entropy in the Northern Ordos Basin, China

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MINE WATER AND THE ENVIRONMENT 2022年第1期41卷 225-236页

作者： Yang, Peng Yang, Weifeng Nie, Yuxu Saleem, Fahad Lu, Fei Ma, Rongkun Li, Ruipeng China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Jiangsu Peoples R China Shendong Coal Grp Co Ltd Jinjie Coal Mine Yulin 719315 Shaanxi Peoples R China

Predicting the water-conducting fractured zone (WCFZ) height is the key to roof water disaster prevention and environmental protection. Field WCFZ data were collected from 24 working faces of Shendong, Shenfu, Yushen, and Yuheng mining areas in the northern Ordos Basin. A fitting formula was obtained by a multiple regression model and the weight of each factor was calculated by information entropy theory, allowing a prediction of the WCFZ height in the area. The formula was improved by applying a safety factor of 1.33, which met the 95.8% reliability requirements of the samples. Finally, the 31,114 working face of the Jinjie coal mine was selected as a case study. The spatial characteristics of the WCFZ in the study area were analyzed using a geographic information system (GIS) and the WCFZ height was measured using borehole TV. The predicted value was 49.6 m and the field measured value exceeded 42.4 m, making the model-predicted value much closer that predictions calculated with other empirical formulas. The results indicate that this method quantitatively predicted the WCFZ height in the study area and enabled the WCFZ height in the study area to be visualized with GIS technology. It is an effective tool for analyzing and predicting the height of the WCFZ and for preventing mine water inrush.

关键词： Mine inrush safety Jinjie coal mine nonlinear multiple regression Geographic information system Field measurements

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An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第3期40卷 921-928页

作者： Yesiloglu-Gultekin, N. Sezer, E. A. Gokceoglu, C. Bayhan, H. Hacettepe Univ Dept Geol Engn TR-06800 Ankara Turkey Aksaray Univ Dept Geol Engn Aksaray Turkey Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey

The uniaxial compressive strength (UCS) of rocks is an important intact rock parameter, and it is commonly used for various engineering applications. This parameter is mainly controlled by the mineralogical and textural characteristics of rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic rocks selected from Turkey;nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of rocks. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Granitic rock nonlinear multiple regression Adaptive neuro fuzzy inference system Uniaxial compressive strength

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multiple regression for environmental data: nonlinearities and prediction bias

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 1999年第2期47卷 165-173页

作者： Geladi, P Hadjiiski, L Hopke, P Umea Univ Dept Organ Chem S-90187 Umea Sweden Clarkson Univ Dept Chem Potsdam NY 13699 USA

multiple regression models are often tested by making plots of predicted against measured values. In these plots, all observations are supposed to fall on the diagonal. Points not positioned on the diagonal show unmodeled behaviour. Some of these deviations are caused by random noise. Environmental data have quite some measurement and sampling noise and one is not supposed to model or predict this noise. However, there can also be a systematic variation, a bias. This bias is often expressed as systematically low predictions for high values. The high values fall below the diagonal in the plot. A kind of bias is a contraction around the diagonal. The high values are predicted too low and the low values are predicted too high: the predictions are contracted around the center of the data set. One factor contributing to bias or contraction is nonlinearities in the true physical relationship. The data set consists of hourly ozone measurements and parallel measurements of nitrogen oxides, temperature, UV radiation and more than 50 organic chemicals. The measurements were made on surface air in an urban environment. It may be assumed that the ozone concentrations are influenced by all the other variables, so a multivariate regression model may be made with 57 predictor variables and ozone concentration as the response variable. Because of expected collinearities and large noise, a partial least squares (PLS) regression model is chosen. The total set of 717 objects is split into a calibration and a test set of 358 and 359 objects, respectively. The data are noisy and the relationship is very nonlinear. It is shown how contraction and prediction bias occur and how extra steps of reducing and nonlinearizing the data remove these effects until only a substantial random noise is left. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： nonlinear multiple regression partial least squares regression prediction bias local bias environmental data

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