Research graph
References from A deep multimodal fusion model for real-time identification of mixed strata in super-large-diameter shield tunnelling. Local targets link to admitted publications; unresolved targets remain external evidence.
A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
10.1007/s00521-020-05244-4 · 2021 · External reference
Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
10.1016/j.cemconres.2021.106449 · 2021 · External reference
Structural damage identification using ensemble deep convolutional neural network models
10.32604/cmes.2022.020840 · 2023 · External reference
Prediction of chloride resistance level in concrete using optimized tree-based machine learning models
10.53941/bci.2025.100007 · 2025 · External reference
LOF: Identifying density-based local outliers
2000 · External reference
Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete
10.1016/j.conbuildmat.2022.129504 · 2022 · External reference
Multisource information fusion for real-time optimization of shield construction parameters
10.1016/j.knosys.2024.111413 · 2024 · External reference
Optimised prediction of tunnel fire heat release rate using the ResNet18_2CLSTM model with bagging for multimodal data
10.1016/j.csite.2024.105268 · 2024 · External reference
Optimization of TBM tunneling parameters for deep buried tunnel based on rock cluster grading and strata intelligent identification
10.1007/s00603-024-04210-z · 2025 · 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
Deep multimodal representation learning: A survey
10.1109/access.2019.2916887 · 2019 · External reference
Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning
10.1016/j.jrmge.2021.05.004 · 2022 · External reference
Multimodal federated learning: Concept, methods, applications and future directions
10.1016/j.inffus.2024.102576 · 2024 · External reference
Interpretable machine learning for predicting the derived cetane number of jet fuels using compact TD-NMR
10.1016/j.snb.2024.137018 · 2025 · External reference
Face failure analysis of a shield tunnel with slurry penetration into the ground
10.1016/j.tust.2022.104554 · 2022 · External reference
Assessment of bearing capacity of concrete piles in alluvial soils using bio and swarm-optimized artificial neural network models
10.53941/bci.2025.100004 · 2025 · External reference
Experiment study on rock mass classification based on RCM-equipped sensors and diversified ensemble-learning model
10.3390/s24196320 · 2024 · External reference
Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods
10.1016/j.undsp.2023.01.001 · 2023 · External reference
Vibration response and parameter influence of TBM cutterhead system under extreme conditions
10.1007/s12206-018-0944-8 · 2018 · External reference
Field monitoring of TBM vibration during excavating changing stratum: Patterns and ground identification
10.1007/s00603-021-02714-6 · 2022 · External reference
Tunnel boring machine vibration-based deep learning for the ground identification of working faces
10.1016/j.jrmge.2021.09.004 · 2021 · External reference
Deep time series forecasting models: A comprehensive survey
10.3390/math12101504 · 2024 · External reference
Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study
10.3389/fped.2022.899954 · 2022 · External reference
Enhancing ground classification models for TBM tunneling: Detecting label errors in datasets
10.1016/j.compgeo.2024.106301 · 2024 · External reference
Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction
10.1016/j.gsf.2022.101519 · 2023 · External reference
Communication in the presence of noise
10.1109/jrproc.1949.232969 · 1949 · External reference
Estimating locations of soil-rock interfaces based on vibration data during shield tunnelling
10.1016/j.autcon.2023.104813 · 2023 · External reference
Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms
10.1016/j.tust.2022.104852 · 2023 · External reference
Identification of geological characteristics from construction parameters during shield tunnelling
10.1007/s11440-022-01590-w · 2023 · External reference
Data augmentation-assisted muck image recognition during shield tunnelling
10.1016/j.undsp.2024.10.001 · 2025 · External reference
Vibration characteristics of cutter-head in soft-hard mixed stratum: An experimental case study on Su’ai tunnel
10.1007/s12205-020-0966-5 · 2020 · External reference
A novel identification technology and real-time classification forecasting model based on hybrid machine learning methods in mixed weathered mudstone-sand-pebble formation
10.1016/j.tust.2024.106045 · 2024 · External reference
Deep learning technologies for shield tunneling: Challenges and opportunities
10.1016/j.autcon.2023.104982 · 2023 · External reference
