Research graph
References from Prototype-guided learning for geological condition inference from TBM operational data. Local targets link to admitted publications; unresolved targets remain external evidence.
Neural networks in civil engineering: 1989–2000
10.1111/0885-9507.00219 · 2001 · External reference
10.56952/arma-2024-0784
10.56952/arma-2024-0784 · External reference
10.1061/9780784484982.001
10.1061/9780784484982.001 · External reference
Development of correlations between various engineering rockmass classification systems using railway tunnel data in Garhwal Himalaya
10.1038/s41598-024-60289-y · 2024 · External reference
TBM tunneling strata automatic identification and working conditions decision support
10.1016/j.autcon.2024.105425 · 2024 · External reference
Data-driven real-time advanced geological prediction in tunnel construction using a hybrid deep learning approach
10.1016/j.autcon.2022.104672 · 2023 · External reference
A modified rock mass classification system for TBM tunnels and tunneling based on the HC method of China
10.1016/j.ijrmms.2020.104551 · 2021 · External reference
Towards reinforcement learning—Driven TBM cutter changing policies
10.1016/j.autcon.2024.105505 · 2024 · External reference
Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: A case history of Nowsood water conveyance tunnel
10.1080/17486020903174303 · 2009 · External reference
Some aspects of rock machining
10.1016/0148-9062(72)90023-x · 1972 · External reference
Modified Q-index for prediction of rock mass quality around a tunnel excavated with a tunnel boring machine (TBM)
10.1007/s10064-018-1257-y · 2019 · External reference
Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system
10.1111/mice.12519 · 2020 · External reference
Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine
10.1016/j.jrmge.2023.02.013 · 2023 · External reference
10.1016/j.neunet.2024.106919
10.1016/j.neunet.2024.106919 · External reference
An improved categorical cross entropy for remote sensing image classification based on noisy labels
10.1016/j.eswa.2022.117296 · 2022 · External reference
Classification and prediction of rock mass boreability based on daily advancement during TBM tunneling
10.3390/buildings14071893 · 2024 · External reference
Prediction of geological parameters during tunneling by time series analysis on in situ data
10.1155/2021/3904273 · 2021 · External reference
Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network
10.1016/j.autcon.2021.103647 · 2021 · External reference
Scale-teaching: robust multi-scale training for time series classification with noisy labels
2023 · External reference
Unresolved reference
External reference
10.1007/978-981-96-0840-9_21
10.1007/978-981-96-0840-9_21 · External reference
Classification for tunnel surrounding rock based on multiple geological methods and extension model
10.1007/s10064-023-03097-4 · 2023 · External reference
Unresolved reference
External reference
Factors affecting Tbm penetration rates in sedimentary rocks
1983 · External reference
10.3390/rs11050523
10.3390/rs11050523 · External reference
U-Time: A fully convolutional network for time series segmentation applied to sleep staging
2019 · External reference
TBM tunnel surrounding rock classification method and real-time identification model based on tunneling performance
10.1061/(asce)gm.1943-5622.0002379 · 2022 · External reference
Review of rock-mass rating and tunneling quality index systems for tunnel design: development, refinement
2018 · External reference
Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining
10.3389/feart.2024.1518844 · 2024 · External reference
Co-learning: Learning from Noisy Labels with Self-supervision
2021 · External reference
An integrated parameter prediction framework for intelligent TBM excavation in hard rock
10.1016/j.tust.2021.104196 · 2021 · External reference
Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method
10.1016/j.jrmge.2021.07.012 · 2021 · External reference
A dynamic rock mass classification method for TBM tunnel
2018 · External reference
An intelligent method for TBM surrounding rock classification based on time series segmentation of rock-machine interaction data
10.1016/j.tust.2023.105317 · 2023 · External reference
Why the future of rock mass classification systems requires revisiting their empirical past
10.1144/qjegh2021-039 · 2021 · External reference
Automated recognition model of geomechanical information based on operational data of tunneling boring machines
10.1007/s00603-021-02723-5 · 2022 · External reference
Characterizing the as-encountered ground condition with tunnel boring machine data using semi-supervised learning
10.1016/j.compgeo.2022.105159 · 2023 · External reference
Rock mass type prediction for tunnel boring machine using a novel semi-supervised method
10.1016/j.measurement.2021.109545 · 2021 · External reference
End-to-end generation of structural topology for complex architectural layouts with graph neural networks
10.1111/mice.13098 · 2024 · External reference
Real-time prediction of TBM penetration rates using a transformer-based ensemble deep learning model
10.1016/j.autcon.2024.105793 · 2024 · External reference
Prediction of geological conditions for a tunnel boring machine using big operational data
10.1016/j.autcon.2018.12.022 · 2019 · External reference
A confident learning-based support vector machine for robust ground classification in noisy label environments
10.1016/j.tust.2024.106128 · 2025 · External reference
TBM cutterhead load prediction model based on the two-stage attention mechanism
2025 · External reference
Predicting tunneling-induced ground collapse based on TBM operational data and geological data
10.1142/s0219876221420159 · 2022 · External reference
