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Artificial Intelligence Prediction of Surface Settlement Inducd by Twin Shields Tunnelling

https://doi.org/10.2139/ssrn.4682948
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inferredcites →https://doi.org/10.1007/s11771-021-4628-7openalex-extracted
Settlement characteristics of large-diameter shield excavation below existing subway in close vicinity
inferredcites →https://doi.org/10.1016/j.aei.2020.101097openalex-extracted
Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses
inferredcites →https://doi.org/10.1016/j.autcon.2018.06.012openalex-extracted
A real-time interaction platform for settlement control during shield tunnelling construction
inferredcites →https://doi.org/10.1016/j.autcon.2020.103375openalex-extracted
Forecasting maximum surface settlement caused by urban tunneling
inferredcites →https://doi.org/10.1016/j.autcon.2023.105006openalex-extracted
Deep learning-based prediction of steady surface settlement due to shield tunnelling
inferredcites →https://doi.org/10.1016/j.compgeo.2013.07.004openalex-extracted
Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine
inferredcites →https://doi.org/10.1016/j.jrmge.2021.08.018openalex-extracted
Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets
inferredcites →https://doi.org/10.1016/j.rockmb.2023.100028openalex-extracted
Uncertainties of landslide susceptibility prediction: Influences of different spatial resolutions, machine learning models and proportions of training and testing dataset
inferredcites →https://doi.org/10.1016/j.trgeo.2022.100771openalex-extracted
Predicting earth pressure balance (EPB) shield tunneling-induced ground settlement in compound strata using random forest
inferredcites →https://doi.org/10.1016/j.tust.2014.10.001openalex-extracted
Effects of twin tunnels construction beneath existing shield-driven twin tunnels
inferredcites →https://doi.org/10.1016/j.tust.2020.103383openalex-extracted
Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study
inferredcites →https://doi.org/10.1016/j.tust.2020.103593openalex-extracted
A critical evaluation of machine learning and deep learning in shield-ground interaction prediction
inferredcites →https://doi.org/10.1016/j.tust.2020.103594openalex-extracted
Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements
inferredcites →https://doi.org/10.1016/j.tust.2023.105253openalex-extracted
Tunnelling-induced ground deformation subjected to the behavior of tail grouting materials
inferredcites →https://doi.org/10.1016/j.undsp.2023.02.004openalex-extracted
Analytical solutions of limit support pressure and vertical earth pressure on cutting face for tunnels
inferredcites →https://doi.org/10.1016/s0266-352x(01)00011-8openalex-extracted
Neural network based prediction of ground surface settlements due to tunnelling
inferredcites →https://doi.org/10.1061/(asce)1090-0241(2007)133:4(445)openalex-extracted
Describing Settlement Troughs over Twin Tunnels Using a Superposition Technique
inferredcites →https://doi.org/10.1139/t07-018openalex-extracted
Investigating ground movements caused by the construction of multiple tunnels in soft ground using laboratory model tests
inferredcites →https://doi.org/10.1139/t10-023openalex-extracted
Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling
inferredcites →https://doi.org/10.1680/geng.14.00054openalex-extracted
Twin-tunnelling-induced ground movements in clay
inferredcites →https://doi.org/10.2139/ssrn.4390697openalex-extracted
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-extracted
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
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