Research graph
References from Reliability-informed intelligent shield attitude control for underground tunnelling using MTGCN spatiotemporal prediction and NSGA-II optimization. Local targets link to admitted publications; unresolved targets remain external evidence.
Total power prediction of shield machine cutterhead based on hybrid neural networks and its confidence interval prediction
10.1016/j.engappai.2026.114375 · 2026 · External reference
Micro-disturbed construction control technology system for shield driven tunnels and its application
2014 · External reference
Theoretical model of shield behavior during excavation. I: theory
10.1061/(asce)1090-0241(2002)128:2(138) · 2002 · External reference
Kinematic behaviour of a tunnel boring machine in soft soil: theory and observations
10.1016/j.tust.2015.03.007 · 2015 · External reference
Pose and trajectory control of shield tunneling machine in complicated stratum
10.1016/j.autcon.2018.05.020 · 2018 · External reference
Study on inner force and dislocation of segments caused by shield machine attitude
10.1016/j.tust.2007.06.007 · 2008 · External reference
Machine learning-based automatic control of tunneling posture of shield machine
10.1016/j.jrmge.2022.06.001 · 2022 · External reference
Prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling
10.1109/access.2019.2952649 · 2019 · External reference
Real-time analysis and regulation of EPB shield steering using random forest
10.1016/j.autcon.2019.102860 · 2019 · External reference
Dynamic prediction for attitude and position in shield tunneling: a deep learning method
10.1016/j.autcon.2019.102840 · 2019 · External reference
Prediction of shield machine attitude based on various artificial intelligence technologies
10.3390/app112110264 · 2021 · External reference
Prediction of shield machine posture using the GRU algorithm with adaptive boosting: a case study of Chengdu Subway project
10.1016/j.trgeo.2022.100837 · 2022 · External reference
Real-time prediction of shield moving trajectory during tunnelling
10.1007/s11440-022-01461-4 · 2022 · External reference
Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network
10.1007/s11440-021-01319-1 · 2022 · External reference
Attitude deviation prediction of shield tunneling machine using Time-Aware LSTM networks
10.1016/j.trgeo.2024.101195 · 2024 · External reference
Dynamic prediction for attitude and position of shield machine in tunneling: a hybrid deep learning method considering dual attention
10.1016/j.aei.2023.102032 · 2023 · External reference
Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling
10.1016/j.undsp.2023.05.006 · 2023 · External reference
Data-driven real-time prediction for attitude and position of super-large diameter shield using a hybrid deep learning approach
10.1016/j.undsp.2023.08.014 · 2024 · External reference
Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-Informer method
2025 · External reference
Prediction of super-large diameter shield attitude based on LSTM-Transformer
2025 · External reference
Integrated transformer approach for shield attitude prediction in marine soft soils considering small curvature effects
10.1016/j.autcon.2026.106992 · 2026 · External reference
Shield attitude prediction based on bayesian-LGBM machine learning
10.1016/j.ins.2023.03.004 · 2023 · External reference
Connecting the dots: multivariate time series forecasting with graph neural networks
2020 · External reference
A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatiotemporal patterns
10.1016/j.eswa.2022.118721 · 2023 · External reference
Fuzzy spatiotemporal graph learning for uncertainty modeling in shield attitude prediction
10.1016/j.autcon.2026.106827 · 2026 · External reference
Data-driven predictions of shield attitudes using bayesian machine learning
10.1016/j.compgeo.2023.106002 · 2024 · External reference
Uncertainty-aware shield attitude prediction and intelligent correction based on a hybrid deep learning framework and UA-DNSGA-III
10.1016/j.autcon.2026.107149 · 2026 · External reference
Multisource information fusion for real-time prediction and multiobjective optimization of large-diameter slurry shield attitude
10.1016/j.ress.2024.110305 · 2024 · External reference
Physics-data driven multi-objective optimization for parallel control of TBM attitude
2025 · External reference
Real-time safety control of shield attitude considering tunneling efficiency
10.1007/s11709-026-1255-2 · 2026 · External reference
The graph neural network model
10.1109/tnn.2008.2005605 · 2009 · External reference
Reliability-informed inverse design of dual tunnels with deep evidential regression
10.1016/j.ress.2025.112134 · 2026 · External reference
Tunnel design in rock masses under uncertainty with reliability constraints and natural gradient boosting-based surrogates
10.1002/nag.70285 · 2026 · External reference
Interpretable and optimized TabNet-TPE for predicting bond strength in corroded reinforced concrete using a global database
10.1016/j.istruc.2025.109707 · 2025 · External reference
An interpretable surrogate modeling framework for rice husk ash concrete using copula-based virtual sampling
10.1038/s41598-026-55368-1 · 2026 · External reference
Hybrid NSGA-III and surrogate model framework for sustainable concrete mix design: balancing strength, energy, and carbon
10.1016/j.asoc.2025.114018 · 2025 · External reference
Tunnel design in rock masses under uncertainty with reliability constraints and natural gradient boosting-based surrogates
10.1002/nag.70285 · ExternalCitation · doi-reference
Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network
10.1007/s11440-021-01319-1 · ExternalCitation · doi-reference
Real-time prediction of shield moving trajectory during tunnelling
10.1007/s11440-022-01461-4 · ExternalCitation · doi-reference
Real-time safety control of shield attitude considering tunneling efficiency
10.1007/s11709-026-1255-2 · ExternalCitation · doi-reference
Dynamic prediction for attitude and position of shield machine in tunneling: a hybrid deep learning method considering dual attention
10.1016/j.aei.2023.102032 · ExternalCitation · doi-reference
Hybrid NSGA-III and surrogate model framework for sustainable concrete mix design: balancing strength, energy, and carbon
10.1016/j.asoc.2025.114018 · ExternalCitation · doi-reference
Pose and trajectory control of shield tunneling machine in complicated stratum
10.1016/j.autcon.2018.05.020 · ExternalCitation · doi-reference
Dynamic prediction for attitude and position in shield tunneling: a deep learning method
10.1016/j.autcon.2019.102840 · ExternalCitation · doi-reference
Real-time analysis and regulation of EPB shield steering using random forest
10.1016/j.autcon.2019.102860 · ExternalCitation · doi-reference
Fuzzy spatiotemporal graph learning for uncertainty modeling in shield attitude prediction
10.1016/j.autcon.2026.106827 · ExternalCitation · doi-reference
Integrated transformer approach for shield attitude prediction in marine soft soils considering small curvature effects
10.1016/j.autcon.2026.106992 · ExternalCitation · doi-reference
Uncertainty-aware shield attitude prediction and intelligent correction based on a hybrid deep learning framework and UA-DNSGA-III
10.1016/j.autcon.2026.107149 · ExternalCitation · doi-reference
Data-driven predictions of shield attitudes using bayesian machine learning
10.1016/j.compgeo.2023.106002 · ExternalCitation · doi-reference
Total power prediction of shield machine cutterhead based on hybrid neural networks and its confidence interval prediction
10.1016/j.engappai.2026.114375 · ExternalCitation · doi-reference
A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatiotemporal patterns
10.1016/j.eswa.2022.118721 · ExternalCitation · doi-reference
Shield attitude prediction based on bayesian-LGBM machine learning
10.1016/j.ins.2023.03.004 · ExternalCitation · doi-reference
Interpretable and optimized TabNet-TPE for predicting bond strength in corroded reinforced concrete using a global database
10.1016/j.istruc.2025.109707 · ExternalCitation · doi-reference
Machine learning-based automatic control of tunneling posture of shield machine
10.1016/j.jrmge.2022.06.001 · ExternalCitation · doi-reference
Multisource information fusion for real-time prediction and multiobjective optimization of large-diameter slurry shield attitude
10.1016/j.ress.2024.110305 · ExternalCitation · doi-reference
Reliability-informed inverse design of dual tunnels with deep evidential regression
10.1016/j.ress.2025.112134 · ExternalCitation · doi-reference
Prediction of shield machine posture using the GRU algorithm with adaptive boosting: a case study of Chengdu Subway project
10.1016/j.trgeo.2022.100837 · ExternalCitation · doi-reference
Attitude deviation prediction of shield tunneling machine using Time-Aware LSTM networks
10.1016/j.trgeo.2024.101195 · ExternalCitation · doi-reference
Study on inner force and dislocation of segments caused by shield machine attitude
10.1016/j.tust.2007.06.007 · ExternalCitation · doi-reference
Kinematic behaviour of a tunnel boring machine in soft soil: theory and observations
10.1016/j.tust.2015.03.007 · ExternalCitation · doi-reference
Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling
10.1016/j.undsp.2023.05.006 · ExternalCitation · doi-reference
Data-driven real-time prediction for attitude and position of super-large diameter shield using a hybrid deep learning approach
10.1016/j.undsp.2023.08.014 · ExternalCitation · doi-reference
An interpretable surrogate modeling framework for rice husk ash concrete using copula-based virtual sampling
10.1038/s41598-026-55368-1 · ExternalCitation · doi-reference
Theoretical model of shield behavior during excavation. I: theory
10.1061/(asce)1090-0241(2002)128:2(138) · ExternalCitation · doi-reference
Prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling
10.1109/access.2019.2952649 · ExternalCitation · doi-reference
The graph neural network model
10.1109/tnn.2008.2005605 · ExternalCitation · doi-reference
Prediction of shield machine attitude based on various artificial intelligence technologies
10.3390/app112110264 · ExternalCitation · doi-reference
