Research graph
References from Insights into cutterhead opening type selection: A data-driven study of TBM industry best practices. Local targets link to admitted publications; unresolved targets remain external evidence.
Statistical and fuzzy analysis of rock burst occurrence in Alborz Tunnel
2019 · External reference
10.1109/aici.2010.82
10.1109/aici.2010.82 · External reference
Research in soil conditioning for EPB tunneling through difficult soils
2009 · External reference
10.1007/978-981-10-6747-1_4
10.1007/978-981-10-6747-1_4 · External reference
A study on soil conditioning for cohesive soils with high content of clay minerals: microscopic simulation and laboratory experiment
2023 · External reference
Systematic performance evaluation of shield TBM cutters for excavating multiple soft rock
2021 · External reference
Introduction to the special section on intelligent systems and pattern recognition (SS: ISPR20)
10.1016/j.patrec.2022.03.013 · 2022 · External reference
Dynamic and probabilistic multi-class prediction of tunnel squeezing intensity
2020 · External reference
Classification and regression trees with Gini index
2020 · External reference
Design aspects governing disc cutters and cutterheads of hard rock TBM—A review
2024 · External reference
Prediction accuracy of underground blast variables: decision tree and artificial neural network
10.1504/ijeie.2020.105382 · 2020 · External reference
Full-scale test of disc cutter rotary cutting in TBM tunnelling: A case study of Mawan granite in Shenzhen, China
2024 · External reference
An efficient AdaBoost algorithm with the multiple thresholds classification
10.3390/app12125872 · 2022 · External reference
Uniformly distributed lace design for hard rock TBMs
10.1016/j.tust.2021.103829 · 2021 · External reference
Optimum design of the peripheral cutters’ specification on the head profile for hard-rock TBMs
10.1016/j.tust.2020.103668 · 2021 · External reference
Using field data and operational constraints to maximize hard rock TBM penetration and advance rates
10.1016/j.tust.2022.104506 · 2022 · External reference
Decision tree analysis of cutter selection for tunnel boring machines: A study of geological conditions and machine types in high-performing TBM projects
10.1016/j.tust.2025.106612 · 2025 · External reference
Decision tree approach for soil liquefaction assessment
10.1155/2013/346285 · 2013 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests
10.3390/app12094565 · 2022 · External reference
On the loads for strength design of cutterhead of full face rock tunnel boring machine
10.1186/s10033-019-0411-1 · 2019 · External reference
Decision Trees, Random Forests and Boosting
2023 · External reference
A comparative study of decision tree ID3 and C4.5
2014 · External reference
Application of tree-based methods in predicting the surface settlement arising from the tunnel excavation with large mix-shield
10.1016/j.sandf.2023.101379 · 2023 · External reference
Comparison of ensemble machine learning methods for automated classification of focal and non-focal epileptic EEG signals
10.3390/math8091481 · 2020 · External reference
Bootstrap aggregating and random forest
2020 · External reference
Unresolved reference
External reference
Cutterhead and cutting tools configurations in coarse grain soils
10.2174/1874836801711010182 · 2017 · External reference
Classification and regression tree methods
2014 · External reference
Analysis of C4.5 algorithm of water quality dataset
10.1088/1742-6596/1898/1/012002 · 2021 · External reference
10.1680/jgeen.22.00254
10.1680/jgeen.22.00254 · External reference
Toward the automation of mechanized tunneling exploring the use of big data analytics for ground forecast in TBM tunnels
10.1016/j.tust.2024.105643 · 2024 · External reference
Evaluating the efficiency of anew cutterhead design to counteract the drawbacks of excavation through very hard rocks
10.1007/s00501-021-01120-3 · 2021 · External reference
EPB machine excavation of mixed soils – Laboratory characterisation
10.1002/geot.201900014 · 2019 · External reference
Regression tree models
2017 · External reference
10.52202/085713-0183
10.52202/085713-0183 · External reference
An optimized system of random forest model by global harmony search with generalized opposition-based learning for forecasting TBM advance rate
10.32604/cmes.2023.029938 · 2023 · External reference
Induction of decision trees
10.1023/a:1022643204877 · 1986 · External reference
Aspects of TBM cutterhead design and performance in very hard rock mass
2024 · External reference
A decision tree-assisted polynomial regression model with application in the cutting force analysis of cutters of a tunnel boring machine
10.1080/0305215x.2022.2039131 · 2022 · External reference
Clogging risks for TBM drives in clay
2004 · External reference
10.1007/978-3-031-17922-8_6
10.1007/978-3-031-17922-8_6 · External reference
Methodological progress note: Classification and regression tree analysis
10.12788/jhm.336610.12788/ · 2020 · External reference
Effect of structural parameters on the opening ratio of the atmospheric cutterhead
2024 · External reference
10.56952/arma-2024-0982
10.56952/arma-2024-0982 · External reference
10.1109/aeeca52519.2021.9574268
10.1109/aeeca52519.2021.9574268 · External reference
Research on rock-breaking characteristics of cutters and matching of cutter spacing and penetration for tunnel boring machine
10.3390/buildings14061757 · 2024 · External reference
Decision trees
2023 · External reference
EPB machine excavation of mixed soils – Laboratory characterisation
10.1002/geot.201900014 · ExternalCitation · doi-reference
10.1007/978-3-031-17922-8_6
10.1007/978-3-031-17922-8_6 · ExternalCitation · doi-reference
10.1007/978-981-10-6747-1_4
10.1007/978-981-10-6747-1_4 · ExternalCitation · doi-reference
Evaluating the efficiency of anew cutterhead design to counteract the drawbacks of excavation through very hard rocks
10.1007/s00501-021-01120-3 · ExternalCitation · doi-reference
Introduction to the special section on intelligent systems and pattern recognition (SS: ISPR20)
10.1016/j.patrec.2022.03.013 · ExternalCitation · doi-reference
Application of tree-based methods in predicting the surface settlement arising from the tunnel excavation with large mix-shield
10.1016/j.sandf.2023.101379 · ExternalCitation · doi-reference
Optimum design of the peripheral cutters’ specification on the head profile for hard-rock TBMs
10.1016/j.tust.2020.103668 · ExternalCitation · doi-reference
Uniformly distributed lace design for hard rock TBMs
10.1016/j.tust.2021.103829 · ExternalCitation · doi-reference
Using field data and operational constraints to maximize hard rock TBM penetration and advance rates
10.1016/j.tust.2022.104506 · ExternalCitation · doi-reference
Toward the automation of mechanized tunneling exploring the use of big data analytics for ground forecast in TBM tunnels
10.1016/j.tust.2024.105643 · ExternalCitation · doi-reference
Decision tree analysis of cutter selection for tunnel boring machines: A study of geological conditions and machine types in high-performing TBM projects
10.1016/j.tust.2025.106612 · ExternalCitation · doi-reference
Induction of decision trees
10.1023/a:1022643204877 · ExternalCitation · doi-reference
A decision tree-assisted polynomial regression model with application in the cutting force analysis of cutters of a tunnel boring machine
10.1080/0305215x.2022.2039131 · ExternalCitation · doi-reference
Analysis of C4.5 algorithm of water quality dataset
10.1088/1742-6596/1898/1/012002 · ExternalCitation · doi-reference
10.1109/aeeca52519.2021.9574268
10.1109/aeeca52519.2021.9574268 · ExternalCitation · doi-reference
10.1109/aici.2010.82
10.1109/aici.2010.82 · ExternalCitation · doi-reference
Decision tree approach for soil liquefaction assessment
10.1155/2013/346285 · ExternalCitation · doi-reference
On the loads for strength design of cutterhead of full face rock tunnel boring machine
10.1186/s10033-019-0411-1 · ExternalCitation · doi-reference
Methodological progress note: Classification and regression tree analysis
10.12788/jhm.336610.12788/ · ExternalCitation · doi-reference
Prediction accuracy of underground blast variables: decision tree and artificial neural network
10.1504/ijeie.2020.105382 · ExternalCitation · doi-reference
10.1680/jgeen.22.00254
10.1680/jgeen.22.00254 · ExternalCitation · doi-reference
Cutterhead and cutting tools configurations in coarse grain soils
10.2174/1874836801711010182 · ExternalCitation · doi-reference
An optimized system of random forest model by global harmony search with generalized opposition-based learning for forecasting TBM advance rate
10.32604/cmes.2023.029938 · ExternalCitation · doi-reference
Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests
10.3390/app12094565 · ExternalCitation · doi-reference
An efficient AdaBoost algorithm with the multiple thresholds classification
10.3390/app12125872 · ExternalCitation · doi-reference
Research on rock-breaking characteristics of cutters and matching of cutter spacing and penetration for tunnel boring machine
10.3390/buildings14061757 · ExternalCitation · doi-reference
Comparison of ensemble machine learning methods for automated classification of focal and non-focal epileptic EEG signals
10.3390/math8091481 · ExternalCitation · doi-reference
10.52202/085713-0183
10.52202/085713-0183 · ExternalCitation · doi-reference
10.56952/arma-2024-0982
10.56952/arma-2024-0982 · ExternalCitation · doi-reference
