The Experts below are selected from a list of 2391 Experts worldwide ranked by ideXlab platform
Alireza Salimi - One of the best experts on this subject based on the ideXlab platform.
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application of Rock Mass Classification systems for performance estimation of Rock tbms using regression tree and artificial intelligence algorithms
Tunnelling and Underground Space Technology, 2019Co-Authors: Alireza Salimi, Jamal Rostami, Christian MoormannAbstract:Abstract Existing Rock Mass Classification systems, such as Rock Quality Index “Q”, Geological Strength Index (GSI), and Rock Mass Rating (RMR) are often used in many empirical design practices in Rock engineering contrasting with their original application. For example, these models which were originally introduced for ground support design are being used in estimation of TBM performance in various ground conditions. Previous use of standard Rock Mass Classification systems in TBM performance prediction has had limited success due to the nature of the weights associated with the input parameters as evidenced by low correlations between their output and Penetration Rate (PR) of TBM in various field applications. This limitation can be mitigated by revising the weights assigned to input parameters, to better represent influence of Rock Mass properties on TBM performance using multivariate regression analysis and artificial intelligence algorithms, including regression tree and genetic programming. This paper offers a brief review of the applications of common Rock Mass Classification systems for performance prediction of TBMs and development of a new model which is based on the input parameters of RMR system for this purpose. The proposed model has been developed based on the analysis of a comprehensive database of TBM performance in various Rock types and offers higher accuracy and sensitivity to Rock Mass parameters in predicting machine performance.
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examining feasibility of developing a Rock Mass Classification for hard Rock tbm application using non linear regression regression tree and generic programming
Geotechnical and Geological Engineering, 2017Co-Authors: Alireza Salimi, Jamal Rostami, Christian Moormann, Jafar HassanpourAbstract:Geotechnical and geological parameters have the greatest impact on the performance of hard Rock tunnel boring machines (TBMs). This includes the Rock and Rock Mass properties that affect the rate of penetration (ROP) as well as the machine utilization that is heavily dependent on ground support type and related machine downtime and delays. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance is still a challenge, especially in complex geological conditions. The past studies have tried to use Rock Mass Classification systems for improving the accuracy of the machine performance prediction. Rock Mass Classifications has been primarily developed for design of ground support, and as such, have not offered a good fit for estimation of TBM performance. This paper will review performance of a hard Rock TBM in a 12.24 km long tunnel and offers analysis of field performance data to evaluate the relationship between various lithological units and TBM operation. The results of statistical analysis of the initial 5.83 km long tunnel indicate strong relationships between geomechanical parameters and TBM performance parameters. Site specific models, including Non-linear regression analysis (NLRA), Classification and regression tree (CART), and Genetic Programming (GP) have been used for analysis of a TBM performance relative to the ground condition data. The current study has looked at the possibility of developing a new Rock Mass Classification system for TBM application by using the above noted analysis. Preliminary results indicate that CART can be used for offering a proper rating scheme for a Rock Mass Classification system that can be used for TBM applications.
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evaluating the suitability of existing Rock Mass Classification systems for tbm performance prediction by using a regression tree
Procedia Engineering, 2017Co-Authors: Alireza Salimi, Jamal Rostami, Christian MoormannAbstract:Abstract Existing Rock Mass Classification systems, such as Rock Mass rating (RMR) are often used in many empirical design practices in Rock engineering contrasting with their original application, i.e. estimation of TBM performance in various ground conditions. However, the use of RMR or similar Classification systems in providing an accurate estimation of TBM penetration rate has had limited success due to the nature of the weights assigned to the input parameters. The results of many investigations on this topic have shown a weak correlation between TBM penetration rate and RMR. This limitation can be addressed by performing regression tree analysis which revises the weights assigned to input parameters to better represent influence of Rock Mass properties on TBM performance. This paper offers an overview of the impact of Rock Mass Classification on TBM performance and introduces a new model based on regression tree using the input parameters of RMR system to predict the performance of hard Rock TBMs. The results of the preliminary analysis show that the use of the proposed model can improve the accuracy of TBM performance estimates in various Rock Masses. This is based on the comparison between the estimated and actual rate of penetration of TBMs in two tunneling projects in igneous and sedimentary Rocks. This study shows the potential of regression tree approach to offer more suitable rating of input parameters for this application, if sufficiently diverse database of machine performance is used in the analysis.
Hani S Mitri - One of the best experts on this subject based on the ideXlab platform.
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application of Rock Mass Classification systems to Rock slope stability assessment a case study
Journal of rock mechanics and geotechnical engineering, 2017Co-Authors: Hassan Basahel, Hani S MitriAbstract:Abstract The stability of Rock slopes is considered crucial to public safety in highways passing through Rock cuts, as well as to personnel and equipment safety in open pit mines. Slope instability and failures occur due to many factors such as adverse slope geometries, geological discontinuities, weak or weathered slope materials as well as severe weather conditions. External loads like heavy precipitation and seismicity could play a significant role in slope failure. In this paper, several Rock Mass Classification systems developed for Rock slope stability assessment are evaluated against known Rock slope conditions in a region of Saudi Arabia, where slopes located in rugged terrains with complex geometry serve as highway road cuts. Selected empirical methods have been applied to 22 Rock cuts that are selected based on their failure mechanisms and slope materials. The stability conditions are identified, and the results of each Rock slope Classification system are compared. The paper also highlights the limitations of the empirical Classification methods used in the study and proposes future research directions.
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validation of empirical Rock Mass Classification systems for Rock slopes
3rd International Symposium on Mine Safety Science and Engineering, 2016Co-Authors: Hassan Basahel, Hani S MitriAbstract:ABSTRACT Many Classification systems have been proposed in the literature to identify the state of stability of Rock slopes. Most of these Classification systems involve factors relevant to the general condition of the Rock Mass, for example, intact Rock strength (UCS), geometry and condition of discontinuities, and groundwater condition. Such factors represent the basic part of most of the Classification systems, which refer to the well-known Bieniawski’s Rock Mass Rating or RMR system. However, these factors were initially developed for underground excavations. Therefore, these Classification systems have been subjected to many criticisms and were questioned for their suitability for Rock slopes. In this paper, some of the common Classification systems for Rock slopes are used to identify their suitability for Rock cuts. Twenty two sites of Rock cuts in mountainous roads affected by heavy rainfall in the southwestern part of Saudi Arabia have been selected as case studies, and four empirical methods are examined for these case studies. The selected methods are Slope Mass Rating or SMR (Romana, 1985), continuous SMR (Tomas, 2007), Chinese SMR (Chen, 1995), and a graphical SMR (Romana, 2012). The stability conditions for each site have been determined by each of these methods and a comparison between the results is made for the case of plane failure mode. It is shown that some of the empirical methods are not applicable such as Chinese SMR (for slopes less than 80 m high), and the graphical SMR method when the slope angle is more than 80°.
Christian Moormann - One of the best experts on this subject based on the ideXlab platform.
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application of Rock Mass Classification systems for performance estimation of Rock tbms using regression tree and artificial intelligence algorithms
Tunnelling and Underground Space Technology, 2019Co-Authors: Alireza Salimi, Jamal Rostami, Christian MoormannAbstract:Abstract Existing Rock Mass Classification systems, such as Rock Quality Index “Q”, Geological Strength Index (GSI), and Rock Mass Rating (RMR) are often used in many empirical design practices in Rock engineering contrasting with their original application. For example, these models which were originally introduced for ground support design are being used in estimation of TBM performance in various ground conditions. Previous use of standard Rock Mass Classification systems in TBM performance prediction has had limited success due to the nature of the weights associated with the input parameters as evidenced by low correlations between their output and Penetration Rate (PR) of TBM in various field applications. This limitation can be mitigated by revising the weights assigned to input parameters, to better represent influence of Rock Mass properties on TBM performance using multivariate regression analysis and artificial intelligence algorithms, including regression tree and genetic programming. This paper offers a brief review of the applications of common Rock Mass Classification systems for performance prediction of TBMs and development of a new model which is based on the input parameters of RMR system for this purpose. The proposed model has been developed based on the analysis of a comprehensive database of TBM performance in various Rock types and offers higher accuracy and sensitivity to Rock Mass parameters in predicting machine performance.
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examining feasibility of developing a Rock Mass Classification for hard Rock tbm application using non linear regression regression tree and generic programming
Geotechnical and Geological Engineering, 2017Co-Authors: Alireza Salimi, Jamal Rostami, Christian Moormann, Jafar HassanpourAbstract:Geotechnical and geological parameters have the greatest impact on the performance of hard Rock tunnel boring machines (TBMs). This includes the Rock and Rock Mass properties that affect the rate of penetration (ROP) as well as the machine utilization that is heavily dependent on ground support type and related machine downtime and delays. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance is still a challenge, especially in complex geological conditions. The past studies have tried to use Rock Mass Classification systems for improving the accuracy of the machine performance prediction. Rock Mass Classifications has been primarily developed for design of ground support, and as such, have not offered a good fit for estimation of TBM performance. This paper will review performance of a hard Rock TBM in a 12.24 km long tunnel and offers analysis of field performance data to evaluate the relationship between various lithological units and TBM operation. The results of statistical analysis of the initial 5.83 km long tunnel indicate strong relationships between geomechanical parameters and TBM performance parameters. Site specific models, including Non-linear regression analysis (NLRA), Classification and regression tree (CART), and Genetic Programming (GP) have been used for analysis of a TBM performance relative to the ground condition data. The current study has looked at the possibility of developing a new Rock Mass Classification system for TBM application by using the above noted analysis. Preliminary results indicate that CART can be used for offering a proper rating scheme for a Rock Mass Classification system that can be used for TBM applications.
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evaluating the suitability of existing Rock Mass Classification systems for tbm performance prediction by using a regression tree
Procedia Engineering, 2017Co-Authors: Alireza Salimi, Jamal Rostami, Christian MoormannAbstract:Abstract Existing Rock Mass Classification systems, such as Rock Mass rating (RMR) are often used in many empirical design practices in Rock engineering contrasting with their original application, i.e. estimation of TBM performance in various ground conditions. However, the use of RMR or similar Classification systems in providing an accurate estimation of TBM penetration rate has had limited success due to the nature of the weights assigned to the input parameters. The results of many investigations on this topic have shown a weak correlation between TBM penetration rate and RMR. This limitation can be addressed by performing regression tree analysis which revises the weights assigned to input parameters to better represent influence of Rock Mass properties on TBM performance. This paper offers an overview of the impact of Rock Mass Classification on TBM performance and introduces a new model based on regression tree using the input parameters of RMR system to predict the performance of hard Rock TBMs. The results of the preliminary analysis show that the use of the proposed model can improve the accuracy of TBM performance estimates in various Rock Masses. This is based on the comparison between the estimated and actual rate of penetration of TBMs in two tunneling projects in igneous and sedimentary Rocks. This study shows the potential of regression tree approach to offer more suitable rating of input parameters for this application, if sufficiently diverse database of machine performance is used in the analysis.
Sandeep Kumar - One of the best experts on this subject based on the ideXlab platform.
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Rock Mass Classification and Slope Stability Assessment of Road Cut Slopes in Garhwal Himalaya, India
Geotechnical and Geological Engineering, 2012Co-Authors: S. Sarkar, D. P. Kanungo, Sandeep KumarAbstract:There are many Rock Mass Classification schemes which are frequently used for different purposes such as estimation of strength and deformability of Rock Masses, stability assessment of Rock slopes, tunneling and underground mining operations etc. The Rock Mass Classification includes some inputs obtained from intact Rock and discontinuity properties which have major influence on assessment of engineering behaviour of Rock Mass. In the present study, detail measurements were employed on road cuts slope faces in Garhwal Himalayas to collect required data to be used for Rock Mass Classification of Rock Mass Rating (RMR) and Geological Strength Index (GSI). The stability assessment of Rock slopes were also done by using Slope Mass Rating. In addition the relation between RMR and GSI were also evaluated using 50 data pairs.
Jafar Hassanpour - One of the best experts on this subject based on the ideXlab platform.
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examining feasibility of developing a Rock Mass Classification for hard Rock tbm application using non linear regression regression tree and generic programming
Geotechnical and Geological Engineering, 2017Co-Authors: Alireza Salimi, Jamal Rostami, Christian Moormann, Jafar HassanpourAbstract:Geotechnical and geological parameters have the greatest impact on the performance of hard Rock tunnel boring machines (TBMs). This includes the Rock and Rock Mass properties that affect the rate of penetration (ROP) as well as the machine utilization that is heavily dependent on ground support type and related machine downtime and delays. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance is still a challenge, especially in complex geological conditions. The past studies have tried to use Rock Mass Classification systems for improving the accuracy of the machine performance prediction. Rock Mass Classifications has been primarily developed for design of ground support, and as such, have not offered a good fit for estimation of TBM performance. This paper will review performance of a hard Rock TBM in a 12.24 km long tunnel and offers analysis of field performance data to evaluate the relationship between various lithological units and TBM operation. The results of statistical analysis of the initial 5.83 km long tunnel indicate strong relationships between geomechanical parameters and TBM performance parameters. Site specific models, including Non-linear regression analysis (NLRA), Classification and regression tree (CART), and Genetic Programming (GP) have been used for analysis of a TBM performance relative to the ground condition data. The current study has looked at the possibility of developing a new Rock Mass Classification system for TBM application by using the above noted analysis. Preliminary results indicate that CART can be used for offering a proper rating scheme for a Rock Mass Classification system that can be used for TBM applications.
