The Experts below are selected from a list of 59256 Experts worldwide ranked by ideXlab platform
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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TBM performance estimation using a classification and Regression Tree (CART) technique
Bulletin of Engineering Geology and the Environment, 2018Co-Authors: Alireza Salimi, Roohollah Shirani Faradonbeh, Masoud Monjezi, Christian MoormannAbstract:With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict the advance rate of an excavation. In this study, a database of actual machine performance from T05 and T06 tunnels of the deep tunnel sewerage system (DTSS) project in Singapore which include: rock mass uniaxial compressive strength, brittleness index ( B _i), volumetric joint account ( J _v), joint orientation ( J _o), TBM specifications and corresponding TBM performance has been compiled. Then, for prediction of specific rock mass boreability index (SRMBI), two different models including classification and Regression Tree (CART) analysis and multivariate Regression analysis (MVRA) have been developed. As statistical indices, correlation coefficient ( R ^2), root mean square error (RMSE) and variance accounted for (VAF) were used to evaluate the efficiency of the developed models for determining the SRMBI of TBMs. According to the obtained results, it was observed that the performance of the CART model is better than the MVRA.
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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.
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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.
Michael P Grant - One of the best experts on this subject based on the ideXlab platform.
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vision survival after open globe injury predicted by classification and Regression Tree analysis
Ophthalmology, 2008Co-Authors: G W Schmidt, A T Broman, H B Hindman, Michael P GrantAbstract:Objective To assist ophthalmologists in treating ocular trauma patients, this study developed and validated a prognostic model to predict vision survival after open globe injury. Design Retrospective cohort review. Participants Two hundred fourteen patients who sought treatment at the Wilmer Ophthalmological Institute with open globe injuries from January 1, 2001, through December 31, 2004, were part of the data set used to build the classification Tree model. Then, to validate the classification Tree, 51 patients were followed up with the goal to compare their actual visual outcome with the outcome predicted by the Tree grown from the classification and Regression Tree analysis. Methods Binary recursive partitioning was used to construct a classification Tree to predict visual outcome after open globe injury. The retrospective cohort treated for open globe injury from January 1, 2001, through December 31, 2004, was used to develop the prognostic Tree and constitutes the training sample. A second independent sample of patient eyes seen from January 1, 2005, through October 15, 2005, was used to validate the prognostic Tree. Main Outcome Measures Two main visual outcomes were assessed: vision survival (range, 20/20–light perception) and no vision (included no light perception, enucleation, and evisceration outcomes). Results A prognostic model for open globe injury outcome was constructed using 214 open globe injuries. Of 14 predictors determined to be associated with a no vision outcome in univariate analysis, presence of a relative afferent pupillary defect and poor initial visual acuity were the most predictive of complete loss of vision; presence of lid laceration and posterior wound location also predicted poor visual outcomes. In an independent cohort of 51 eyes, the prognostic model had 85.7% sensitivity to predict no vision correctly and 91.9% specificity to predict vision survival correctly. Conclusions The open globe injury prognostic model constructed in this study demonstrated excellent predictive accuracy and should be useful in counseling patients and making clinical decisions regarding open globe injury management.
A Awata - One of the best experts on this subject based on the ideXlab platform.
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data mining of electricity price forecasting with Regression Tree and normalized radial basis function network
Systems Man and Cybernetics, 2007Co-Authors: Hiroyuki Mori, A AwataAbstract:This paper proposes a new method for electricity price forecasting. The proposed method is based on the Regression Tree and NRBFN (Normalized Radial Basis Function Network) of ANN. The former is used to evaluate if-then rules and classify input data into some clusters. The latter is employed to calculate more accurate predicted values. The Regression Tree is one of data-mining techniques that extract if-then rules from database. NRBFN is an extension of RBFN (Radial Basis Function Network) that improves the generalization ability of RBFN. The effectiveness of the proposed method is demonstrated for real data of on-step ahead electricity price forecasting.
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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TBM performance estimation using a classification and Regression Tree (CART) technique
Bulletin of Engineering Geology and the Environment, 2018Co-Authors: Alireza Salimi, Roohollah Shirani Faradonbeh, Masoud Monjezi, Christian MoormannAbstract:With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict the advance rate of an excavation. In this study, a database of actual machine performance from T05 and T06 tunnels of the deep tunnel sewerage system (DTSS) project in Singapore which include: rock mass uniaxial compressive strength, brittleness index ( B _i), volumetric joint account ( J _v), joint orientation ( J _o), TBM specifications and corresponding TBM performance has been compiled. Then, for prediction of specific rock mass boreability index (SRMBI), two different models including classification and Regression Tree (CART) analysis and multivariate Regression analysis (MVRA) have been developed. As statistical indices, correlation coefficient ( R ^2), root mean square error (RMSE) and variance accounted for (VAF) were used to evaluate the efficiency of the developed models for determining the SRMBI of TBMs. According to the obtained results, it was observed that the performance of the CART model is better than the MVRA.
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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.
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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.
G W Schmidt - One of the best experts on this subject based on the ideXlab platform.
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vision survival after open globe injury predicted by classification and Regression Tree analysis
Ophthalmology, 2008Co-Authors: G W Schmidt, A T Broman, H B Hindman, Michael P GrantAbstract:Objective To assist ophthalmologists in treating ocular trauma patients, this study developed and validated a prognostic model to predict vision survival after open globe injury. Design Retrospective cohort review. Participants Two hundred fourteen patients who sought treatment at the Wilmer Ophthalmological Institute with open globe injuries from January 1, 2001, through December 31, 2004, were part of the data set used to build the classification Tree model. Then, to validate the classification Tree, 51 patients were followed up with the goal to compare their actual visual outcome with the outcome predicted by the Tree grown from the classification and Regression Tree analysis. Methods Binary recursive partitioning was used to construct a classification Tree to predict visual outcome after open globe injury. The retrospective cohort treated for open globe injury from January 1, 2001, through December 31, 2004, was used to develop the prognostic Tree and constitutes the training sample. A second independent sample of patient eyes seen from January 1, 2005, through October 15, 2005, was used to validate the prognostic Tree. Main Outcome Measures Two main visual outcomes were assessed: vision survival (range, 20/20–light perception) and no vision (included no light perception, enucleation, and evisceration outcomes). Results A prognostic model for open globe injury outcome was constructed using 214 open globe injuries. Of 14 predictors determined to be associated with a no vision outcome in univariate analysis, presence of a relative afferent pupillary defect and poor initial visual acuity were the most predictive of complete loss of vision; presence of lid laceration and posterior wound location also predicted poor visual outcomes. In an independent cohort of 51 eyes, the prognostic model had 85.7% sensitivity to predict no vision correctly and 91.9% specificity to predict vision survival correctly. Conclusions The open globe injury prognostic model constructed in this study demonstrated excellent predictive accuracy and should be useful in counseling patients and making clinical decisions regarding open globe injury management.
