检索结果-内蒙古大学图书馆

Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models

引用

ENGINEERING WITH COMPUTERS 2017年第1期33卷 13-21页

作者： Khandelwal, Manoj Faradonbeh, Roohollah Shirani Monjezi, Masoud Armaghani, Danial Jahed Bin Abd Majid, Muhd Zaimi Yagiz, Saffet Federat Univ Australia Fac Sci & Technol POB 663 Ballarat Vic 3353 Australia Islamic Azad Univ South Tehran Branch Young Researchers & Elite Club Tehran Iran Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran Islamic Azad Univ Qaemshahr Branch Young Researchers & Elite Club Qaemshahr Iran Univ Teknol Malaysia Inst Smart Infrastruct & Innovat Construct ISIIC UTM Construct Res Ctr Fac Civil Engn Skudai 81310 Johor Malaysia Pamukkale Univ Dept Geol Engn Fac Engn TR-20017 Denizli Turkey

Brittleness of rock is one of the most critical features for design of underground excavation project. Therefore, proper assessing of rock brittleness can be very useful for designers and evaluators of geotechnical applications. In this study, feasibility of genetic programming (GP) model and non-linear multiple regression (NLMR) in predicting brittleness of intact rocks is examined. For this purpose, a dataset developed by conducting various rock tests including uniaxial compressive strength, Brazilian tensile strength, unit weight and brittleness via punch penetration on rock samples gathered from 48 tunnels projects around the world is utilized herein. Considering multiple inputs, several GP models were constructed to estimate brittleness index of the rock and finally, the best GP model was selected. Note that, GP can make an equation for predicting output of the system using model inputs. To show applicability of the developed GP model, non-linear multiple regression (NLMR) was also applied and developed. Considering some model performance indices, performance prediction of the GP and NLMR models were evaluated and it was found that the GP model is superior to NLMR one. Based on coefficient of determination (R (2)) of testing datasets, by proposing GP model, it can be improved from 0.882 (obtained by NLMR model) to 0.904. It is worth mentioning that the proposed predictive models in this study should be planned and used for the similar types of rock and the established inputs ranges.

关键词： Brittleness Genetic programming non-linear multiple regression

来源：评论

学校读者我要写书评

暂无评论

Comparison of hot rolled steel mechanical property prediction models using linear multiple regression, non-linear multiple regression and non-linear artificial neural networks

引用

IRONMAKING & STEELMAKING 2005年第5期32卷 435-442页

作者： Jones, DM Watton, J Brown, KJ Corus Strip Prod UK ECM2 Port Talbot SA13 2EZ England Univ Cardiff Wales Sch Engn Cardiff S Glam Wales

In the manufacture of rolled steel from a hot strip mill, the final mechanical properties, such as yield strength, ultimate tensile strength and elongation to fracture, are important requirements specified by the customer. The use of mathematical modelling techniques such as multiple regression analysis, or computational developments such as artificial neural networks, can result in the creation of acceptably accurate predictive models. However, the accuracy of any predictive model will depend on the quality of data used in its creation, and thus a brief statistical analysis of the mechanical property data used for model development is discussed. In the present paper a comparison of the application of linear multiple regression, non-linear multiple regression and non-linear neural networks is made for various steel families using data taken from the Corus Port Talbot hot strip mill. A statistical summary of their relative predictive errors is given, and although all three are comparable, the non-linear, black box approach of a suitably structured neural network provides overall more accurate predictive models than the use of linear or non-linear multiple regression.

关键词： individual and moving range linear multiple regression non-linear multiple regression neural networks yield strength ultimate tensile strength elongation to fracture

来源：评论

学校读者我要写书评

暂无评论

Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation

引用

ENGINEERING WITH COMPUTERS 2016年第1期32卷 123-133页

作者： Faradonbeh, Roohollah Shirani Monjezi, Masoud Armaghani, Danial Jahed Tarbiat Modares Univ Dept Min Tehran 14115143 Iran Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Johor Baharu 81310 Johor Malaysia

In addition to all benefits of blasting in mining and civil engineering applications, it has some undesirable environmental impacts. Backbreak is an unwanted phenomenon of blasting which can cause instability of mine walls, decreasing efficiency of drilling, falling down of machinery, etc. Recently, the use of new approaches such as artificial intelligence (AI) is greatly recommended by many researchers. In this paper, a new AI technique namely genetic programing (GP) was developed to predict BB. To prepare a sufficient database, 175 blasting works were investigated in Sungun copper mine, Iran. In these operations, the most influential parameters on BB including burden, spacing, stemming length, powder factor and stiffness ratio were measured and used to develop BB predictive models. To demonstrate capability of GP technique, a non-linear multiple regression (NLMR) model was also employed for prediction of BB. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R-2) were used to control the capacity performance of the predictive models. The performance indices obtained by GP approach indicate the higher reliability of GP compared to NLMR model. RMSE and VAF values of 0.327 and 97.655, respectively, for testing datasets of GP approach reveal the superiority of this model in predicting BB, while these values were obtained as 0.865 and 81.816, respectively, for NLMR model.

关键词： Blasting Backbreak Genetic programing non-linear multiple regression

来源：评论

学校读者我要写书评

暂无评论

Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network

引用

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2019年第3期78卷 1501-1513页

作者： Darbor, Mohammad Faramarzi, Lohrasb Sharifzadeh, Mostafa Isfahan Univ Technol Dept Min Engn Esfahan *** Iran Curtin Univ WASM Dept Min Engn Bentley WA Australia

Cost and efficiency estimation for rotary drilling rigs is an essential step in the design of excavation projects. Due to the complexity of influencing factors on rotary drilling, sophisticated modeling methods are required for performance prediction. In this study, rate of penetration (ROP) of a rotary drilling machine using two developed modeling techniques, namely, non-linear multiple regression models (NLMR) and multilayer perceptron-artificial neural networks (MLP-ANN) were assessed. For this purpose, field and experimental data of various case studies were used. Several performance indexes, including determination coefficient (R-2), variance accounted for (VAF), and root mean square error (RMSE), were evaluated to check the prediction capacity of the developed models. Considering multiple inputs in various NLMR models, the most influencing factors on ROP were determined to be brittleness, rock quality designation (RQD) index, water content, and anisotropy index. Multivariate analysis results of developed models showed that the MLP-ANN model indicates higher precision in performance prediction than the NLMR model for both the training and testing datasets. Additionally, sensitivity analysis showed that RQD and water content have significant influence on the ROP. The models proposed in this study can successfully be applied to predict the ROP in rocks with similar characteristics.

关键词： Rate of penetration non-linear multiple regression Multilayer perceptron neural networks Uncertainty

来源：评论

学校读者我要写书评

暂无评论

Use of non-linear multiple regression models for setting water quality criteria for copper: Consider the effects of salinity and dissolved organic carbon

引用

JOURNAL OF HAZARDOUS MATERIALS 2023年 450卷 131107页

作者： Cui, Liang Wang, Yan Zhang, Hua Lv, Xubo Lei, Kun Chinese Res Inst Environm Sci Inst Water Ecol & Environm State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China Chinese Res Inst Environm Sci State Environm Protect Key Lab Simulat & Control G Beijing 100012 Peoples R China

Cu pollution is a global concern because of its high toxicity and persistence. Few investigations have been conducted on the effects of salinity and dissolved organic carbon (DOC) on Cu toxicity and water quality criteria (WQC). To analyze their impact on the WQC of Cu, non-linear multiple regression (NLMR) models based on salinity and DOC were constructed. The NLMR models demonstrated that when salinity increased, the toxicity values for Cu on fish, mollusca, rotifer, and echinodermata first increased and then declined, whereas those for arthropoda and algae increased. These findings demonstrate that salinity has a substantial impact on Cu toxicity, primarily owing to changes in physiological activity. The original and corrected WQC values in the upper, middle, and outer regions of the Yangtze Estuary were derived based on the species sensitivity distribution method. These values were 1.49, 3.49, 8.86, and 0.87 mu g center dot L-1. An important finding was that lower Cu concentrations in the outer areas posed the highest ecological risk owing to the effects of salinity and DOC. NLMR models are applicable to other coastal areas worldwide. This provides valuable information for the establishment of an accurate and protective estuary for Cu-related WQC.

关键词： Copper non-linear multiple regression Water quality criteria Salinity Dissolved organic carbon

来源：评论

学校读者我要写书评

暂无评论

Utilizing regression models to find functions for determining ripping production based on laboratory tests

引用

MEASUREMENT 2017年 111卷 216-225页

作者： Mohamad, Edy Tonnizam Armaghani, Danial Jahed Mahdyar, Amir Komoo, Ibrahim Kassim, Khairul Anuar Abdullah, Arham Abd Majid, Muhd Zaimi Univ Teknol Malaysia Ctr Trop Geoengn GEOTROPIK Fac Civil Engn Utm Johor Bahru 81310 Malaysia Amirkabir Univ Technol Dept Civil & Environm Engn Tehran 15914 Iran Univ Teknol Malaysia Fac Civil Engn Dept Struct & Mat Skudai 81310 Johor Malaysia Univ Kebangsaan Malaysia South East Asia Prevent Res Inst SEADPRI Bangi 43600 Selangor Malaysia Univ Teknol Malaysia Dept Geotech & Transportat Fac Civil Engn Utm Skudai 81310 Johor Malaysia Univ Teknol Malaysia UTM Construct Res Ctr ISIIC Fac Civil Engn Skudai 81310 Johor Malaysia

The selection of suitable overburden loosening method has a crucial importance in several applications of geotechnical engineering. Factors such as rock properties and environmental constrains play a significant role in the selection of the needed equipment for overburden loosening. This paper presents several new models/equations for prediction of ripping production (Q) using rock material properties. To this end, three sites in Malaysia were selected and a total of 52 direct ripping tests were conducted in Johor state on sandstone and shale rock types. In addition, using the collected block samples, point load test, Brazilian test, slake-durability test, p-wave velocity test and uniaxial compressive strength test were also carried out in the laboratory. Numerous equations have been proposed to predict Q considering simple regression, linear multiple regression (LMR), and non-linear multiple regression (NLMR) models. Simple regression analysis indicated that the relationships between rock material properties and Q were meaningful and acceptable. Furthermore, both LMR and NLMR equations indicated similar performance capacity in predicting Q. Nevertheless;the use of NLMR equations resulted in prediction performance with higher accuracy in estimating Q compared to LMR equations.

关键词： Ripping production Laboratory test Simple regression analysis linear multiple regression non-linear multiple regression

来源：评论

学校读者我要写书评

暂无评论

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

引用

ENGINEERING GEOLOGY 2015年 185卷 1-9页

作者： Kayabasi, A. Yesiloglu-Gultekin, N. Gokceoglu, C. Eskisehir Osmangazi Univ Dept Geol Engn TR-26480 Eskisehir Turkey Aksaray Univ Dept Geol Engn Aksaray Turkey Hacettepe Univ Dept Geol Engn TR-06800 Ankara Turkey

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

关键词： Rock mass permeability Lugeon non-linear multiple regression ANFIS

来源：评论

学校读者我要写书评

暂无评论

Application of two non-linear prediction tools to the estimation of tunnel boring machine performance

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2009年第4-5期22卷 808-814页

作者： Yagiz, S. Gokceoglu, C. Sezer, E. Iplikci, S. Pamukkale Univ Dept Geol Engn TR-20020 Denizli Turkey Hacettepe Univ Dept Geol Engn TR-06800 Ankara Turkey Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey Pamukkale Univ Dept Elect & Elect Engn TR-20020 Denizli Turkey

Predicting tunnel boring machine (TBM) performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling machine. Many models and equations have previously been introduced to estimate TBM performance based on properties of both rock and machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring machine performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM performance as a function of rock properties. For this purpose, rock properties and machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Artificial neural networks non-linear multiple regression Rock properties TBM prognosis Tunneling

来源：评论

学校读者我要写书评

暂无评论

Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system

引用

ENVIRONMENTAL EARTH SCIENCES 2016年第9期75卷 1-12页

作者： Hasanipanah, Mahdi Armaghani, Danial Jahed Monjezi, Masoud Shams, Samira Univ Kashan Dept Min Engn Kashan Iran Islamic Azad Univ Sci & Res Branch Young Researchers & Elite Club Tehran Iran Tarbiat Modares Univ Fac Engn Dept Min Tehran 14115143 Iran

Poor fragmentation is one of the most side effects induced by blasting operations. Therefore, risk assessment and prediction of rock fragmentation are essential to reduce the mentioned effects. In the present study, an attempt has been made to evaluate the risk associated with rock fragmentation as well as its prediction at Sarcheshmeh copper mine, Iran, proposing the rock engineering system (RES) technique. A total number of 52 blasting events were collected and considered and the values of 10 key effective parameters in rock fragmentation were carefully measured in the mine. These 10 key parameters were only related to blasting design and rock mass properties were not considered in the analysis of this study due to some limitations regarding their measurements in the mine. The RES result showed that the level of overall risk, based on the considered blast events, is in the range of medium-high. Furthermore, it was found that the burden is the most interaction factor in the rock fragmentation. In case of rock fragmentation prediction, all of datasets were divided randomly to training and testing datasets for proposing RES model. For comparison purpose, non-linear multiple regression (NLMR) was also employed for estimating rock fragmentation. The performances of the proposed predictive models were examined according to three performance indices, i.e. coefficient of determination (R-2), root mean square error (RMSE) and variance account for (VAF). The obtained results of this study indicated that the RES is a reliable method to predict rock fragmentation with a higher degree of accuracy in comparison to NLMR model. For instance, RMSE values of 1.95 and 4.002 for testing datasets of RES and NLMR models, respectively, suggest the superiority of the RES model in predicting rock fragmentation compared to other developed model.

关键词： Blasting Rock fragmentation Risk assessment Rock engineering system non-linear multiple regression

来源：评论

学校读者我要写书评

暂无评论

Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第6期38卷 7476-7485页

作者： Dagdelenler, Gulseren Sezer, Ebru Akcapinar Gokceoglu, Candan Hacettepe Univ Dept Geol Engn TR-06800 Ankara Turkey Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey

Granitic rocks are commonly used as building and ornamental stones and pavement material in various civil engineering structures. However, the weathered material should not be used for these purposes. For this reason, determination of weathering degree of the granitic rocks is one of the important issues in rock engineering and engineering geology. In literature, it is possible to find some approaches for the determination of weathering degree of granitic rocks. Additionally, some soft computing methods have been used for the determination of the weathering degree of the granitic rocks. However, in literature, the non-linear multiple regression and the adaptive neuro-fuzzy inference system have not been used for the weathering classification yet. For this reason, the purpose of this study is to apply some statistical and soft computing methods such as artificial neural networks and adaptive neuro-fuzzy inference system on the determination of weathering degree of a granitic rock selected from Turkey by using some index and mechanical properties. The study includes four main stages such as sampling, testing, modeling and assessment of the model performances. During the modeling stage, three weathering prediction models with multi-inputs are developed with two soft computing techniques such as artificial neural networks and the adaptive neuro-fuzzy inference system, and a non-linear regression technique. The general performances of models developed in this study are close;however the adaptive neuro-fuzzy inference system exhibits the best performance considering the performance index and the degree of consistency. Finally, all models developed in the present study can be used when determining the weathering degree. However, the models developed in this study should be controlled by using the data at hand, before the use them in the practical purposes. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Granite non-linear multiple regression Artificial neural network Adaptive neuro-fuzzy inference system Weathering degree

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：