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Classification of surface settlement levels induced by TBM driving in urban areas using random forest with data-driven feature selection

https://doi.org/10.1016/j.autcon.2021.104109
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781118548387openalex-snapshot
Applied Logistic Regression
inferredcites →https://doi.org/10.1002/nag.395openalex-snapshot
A 3D finite element simulation model for TBM tunnelling in soft ground
inferredcites →https://doi.org/10.1007/s00521-020-05006-2openalex-snapshot
Forecasting tunnel geology, construction time and costs using machine learning methods
inferredcites →https://doi.org/10.1007/s12665-012-2214-xopenalex-snapshot
Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes
inferredcites →https://doi.org/10.1016/j.autcon.2018.11.013openalex-snapshot
Recurrent neural networks for real-time prediction of TBM operating parameters
inferredcites →https://doi.org/10.1016/j.autcon.2019.102860openalex-snapshot
Real-time analysis and regulation of EPB shield steering using Random Forest
inferredcites →https://doi.org/10.1016/j.autcon.2020.103208openalex-snapshot
White-box regression (elastic net) modeling of earth pressure balance shield machine advance rate
inferredcites →https://doi.org/10.1016/j.autcon.2020.103375openalex-snapshot
Forecasting maximum surface settlement caused by urban tunneling
inferredcites →https://doi.org/10.1016/j.autcon.2021.103779openalex-snapshot
Machine learning forecasting models of disc cutters life of tunnel boring machine
inferredcites →https://doi.org/10.1016/j.enggeo.2012.10.002openalex-snapshot
Movements caused by the excavation of tunnels using face pressurized shields — Analysis of monitoring and numerical modeling results
inferredcites →https://doi.org/10.1016/j.enggeo.2017.06.014openalex-snapshot
Bayesian prediction of TBM penetration rate in rock mass
inferredcites →https://doi.org/10.1016/j.ins.2009.06.010openalex-snapshot
Feature selection for multi-label naive Bayes classification
inferredcites →https://doi.org/10.1016/j.trgeo.2020.100497openalex-snapshot
Dynamic prediction models of rock quality designation in tunneling projects
inferredcites →https://doi.org/10.1016/j.trgeo.2020.100499openalex-snapshot
Artificial intelligence forecasting models of uniaxial compressive strength
inferredcites →https://doi.org/10.1016/j.tust.2004.02.128openalex-snapshot
Modelling TBM performance with artificial neural networks
inferredcites →https://doi.org/10.1016/j.tust.2005.06.007openalex-snapshot
Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling
inferredcites →https://doi.org/10.1016/j.tust.2005.07.001openalex-snapshot
Prediction of tunneling-induced ground movement with the multi-layer perceptron
inferredcites →https://doi.org/10.1016/j.tust.2007.07.002openalex-snapshot
Artificial neural networks analysis of São Paulo subway tunnel settlement data
inferredcites →https://doi.org/10.1016/j.tust.2015.04.021openalex-snapshot
Maximum surface settlement based classification of shallow tunnels in soft ground
inferredcites →https://doi.org/10.1016/j.tust.2017.03.011openalex-snapshot
Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method
inferredcites →https://doi.org/10.1016/j.tust.2019.103046openalex-snapshot
Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms
inferredcites →https://doi.org/10.1016/j.tust.2019.103097openalex-snapshot
Application and outlook of information and intelligence technology for safe and efficient TBM construction
inferredcites →https://doi.org/10.1016/j.tust.2020.103466openalex-snapshot
MSAC: Towards data driven system behavior classification for TBM tunneling
inferredcites →https://doi.org/10.1016/j.tust.2020.103514openalex-snapshot
Decision-making in tunneling using artificial intelligence tools
inferredcites →https://doi.org/10.1016/j.tust.2020.103595openalex-snapshot
Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data
inferredcites →https://doi.org/10.1016/j.tust.2020.103699openalex-snapshot
Prediction of tunnel boring machine operating parameters using various machine learning algorithms
inferredcites →https://doi.org/10.1016/s0266-352x(01)00011-8openalex-snapshot
Neural network based prediction of ground surface settlements due to tunnelling
inferredcites →https://doi.org/10.1061/(asce)1090-0241(1998)124:5(389)openalex-snapshot
Modular Neural Networks for Predicting Settlements during Tunneling
inferredcites →https://doi.org/10.1061/(asce)1090-0241(1998)124:9(846)openalex-snapshot
Analytical Prediction for Tunneling-Induced Ground Movements in Clays
inferredcites →https://doi.org/10.1109/72.298224openalex-snapshot
Using mutual information for selecting features in supervised neural net learning
inferredcites →https://doi.org/10.1111/j.2517-6161.1974.tb00994.xopenalex-snapshot
Cross-Validatory Choice and Assessment of Statistical Predictions
inferredcites →https://doi.org/10.12989/gae.2020.21.2.153openalex-snapshot
Determination of effective parameters on surface settlement during shield TBM
inferredcites →https://doi.org/10.1680/geot.1987.37.3.301openalex-snapshot
Analysis of undrained soil deformation due to ground loss
inferredcites →https://doi.org/10.1680/geot.1996.46.4.753openalex-snapshot
Surface settlements due to deformation of a tunnel in an elastic half plane
inferredcites →https://doi.org/10.5281/zenodo.4091344openalex-snapshot
8080labs/ppscore: zenodo release

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4531852openalex-extracted
Data- and Experience-Driven Neural Networks with Limited Features for Long-Term Settlement Prediction of Tunnel
inferred← citeshttps://doi.org/10.2139/ssrn.4750349openalex-extracted
Intelligent Support System Implementing Machine Learning and Statistical Analysis for Predicting Ground Settlement Induced by Tbm Excavation
inferred← citeshttps://doi.org/10.1007/978-3-031-50920-9_27openalex-snapshot
Evolutionary Approach to Feature Elimination in House Price Estimation
inferred← citeshttps://doi.org/10.1007/s10584-023-03529-wopenalex-snapshot
Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques
inferred← citeshttps://doi.org/10.1007/s11431-025-3011-9openalex-snapshot
AWCNet: A TimesNet-based model for real-time TBM thrust and torque prediction over extended durations
inferred← citeshttps://doi.org/10.1007/s11440-022-01779-zopenalex-snapshot
Prediction of rock mass class ahead of TBM excavation face by ML and DL algorithms with Bayesian TPE optimization and SHAP feature analysis
inferred← citeshttps://doi.org/10.1007/s11440-023-01936-yopenalex-snapshot
Real-time prediction of attitude and moving trajectory in shield tunneling based optimal input parameter combination using random forest deep learning method
inferred← citeshttps://doi.org/10.1007/s12517-024-11979-4openalex-snapshot
A case study of tunnel boring machines advance rate prediction using meta-heuristic techniques
inferred← citeshttps://doi.org/10.1007/s12665-024-11729-5openalex-snapshot
A multi-factor-driven approach for predicting surface settlement caused by the construction of subway tunnels by undercutting method
inferred← citeshttps://doi.org/10.1007/s40098-025-01238-yopenalex-snapshot
Role of Artificial Intelligence (AI) Techniques in Tunnel Engineering—A Scientific Review
inferred← citeshttps://doi.org/10.1007/s42107-024-01173-yopenalex-snapshot
Optimization seismic resilience: a machine learning approach for vertical irregular buildings
inferred← citeshttps://doi.org/10.1007/s43503-026-00101-0openalex-snapshot
Physics-guided gradient boosting regression tree for predicting tunnel deformation induced by foundation pit excavation
inferred← citeshttps://doi.org/10.1016/j.aej.2025.12.056openalex-snapshot
Predicting the condition of small and medium-span bridges using hybrid machine learning
inferred← citeshttps://doi.org/10.1016/j.applthermaleng.2023.120386openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.autcon.2022.104331openalex-snapshot
Surface settlement prediction for urban tunneling using machine learning algorithms with Bayesian optimization
inferred← citeshttps://doi.org/10.1016/j.autcon.2022.104672openalex-snapshot
Data-driven real-time advanced geological prediction in tunnel construction using a hybrid deep learning approach
inferred← citeshttps://doi.org/10.1016/j.autcon.2023.105240openalex-snapshot
Digital twin enabled real-time advanced control of TBM operation using deep learning methods
inferred← citeshttps://doi.org/10.1016/j.autcon.2024.105516openalex-snapshot
Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning
inferred← citeshttps://doi.org/10.1016/j.autcon.2024.105819openalex-snapshot
Artificial intelligence driven tunneling-induced surface settlement prediction
inferred← citeshttps://doi.org/10.1016/j.autcon.2025.106725openalex-snapshot
Digital twin–enabled real-time control of tunnel boring machines using deep reinforcement learning for cumulative settlement management
inferred← citeshttps://doi.org/10.1016/j.autcon.2026.106941openalex-snapshot
BIM–FEM framework integrated with deep learning for tunnel construction safety
inferred← citeshttps://doi.org/10.1016/j.compgeo.2024.106149openalex-snapshot
Prediction of surface settlement around subway foundation pits based on spatiotemporal characteristics and deep learning models
inferred← citeshttps://doi.org/10.1016/j.compgeo.2024.106805openalex-snapshot
Probabilistic reliability assessment method for max ground settlement prediction of subway tunnel under uncertain construction information
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2023.130321openalex-snapshot
Optimized machine learning approaches for identifying vertical temperature gradient on ballastless track in natural environments
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2023.132754openalex-snapshot
Predicting the 28-day compressive strength by mix proportions: Insights from a large number of observations of industrially produced concrete
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112402openalex-snapshot
Development of data-driven predictive model and enhanced multiobjective optimization to improve the excavation performance of large-diameter slurry shields
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118721openalex-snapshot
A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.133429openalex-snapshot
A random forest prediction model for large deformation grades in stratified soft rock tunnels based on knowledge and data fusion
inferred← citeshttps://doi.org/10.1016/j.ghm.2023.11.004openalex-snapshot
Artificial intelligence in tunnel construction: A comprehensive review of hotspots and frontier topics
inferred← citeshttps://doi.org/10.1016/j.gsd.2025.101464openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2023.09.019openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jhydrol.2023.129862openalex-snapshot
Characterization of seawater intrusion based on machine learning and implications for offshore management under shared socioeconomic paths
inferred← citeshttps://doi.org/10.1016/j.jrmge.2024.05.027openalex-snapshot
Prediction of abnormal TBM disc cutter wear in mixed ground condition using interpretable machine learning with data augmentation
inferred← citeshttps://doi.org/10.1016/j.jrmge.2024.11.062openalex-snapshot
Interpretable CEEMDAN-SMA-LSSVM hybrid model for predicting shield tunnel-induced settlement
inferred← citeshttps://doi.org/10.1016/j.jrmge.2025.09.034openalex-snapshot
Tunnel boring machine jamming anticipation based on a new monitoring system for real-time perception of shield strain
inferred← citeshttps://doi.org/10.1016/j.nhres.2023.11.002openalex-snapshot
Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies
inferred← citeshttps://doi.org/10.1016/j.psep.2024.06.050openalex-snapshot
Development of forecasting of monthly SAR time series in river systems: A multivariate data decomposition-based hybrid approach
inferred← citeshttps://doi.org/10.1016/j.ress.2023.109172openalex-snapshot
Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI
inferred← citeshttps://doi.org/10.1016/j.rineng.2026.111700openalex-snapshot
Three-dimensional numerical investigation on spatial interaction and ground deformation of twin tunnels based on the convergence-confinement method
inferred← citeshttps://doi.org/10.1016/j.sandf.2023.101379openalex-snapshot
Application of tree-based methods in predicting the surface settlement arising from the tunnel excavation with large mix-shield
inferred← citeshttps://doi.org/10.1016/j.sue.2025.12.001openalex-snapshot
Research status and development trends of intelligent and digitalized disaster prevention and safety protection in tunneling
inferred← citeshttps://doi.org/10.1016/j.trd.2024.104479openalex-snapshot
Measurement and prediction of subway resilience under rainfall events: An environment perspective
inferred← citeshttps://doi.org/10.1016/j.trgeo.2022.100808openalex-snapshot
Tunneling-induced settlement prediction using the hybrid feature selection method for feature optimization
inferred← citeshttps://doi.org/10.1016/j.trgeo.2025.101681openalex-snapshot
The deformation prediction for airport concrete runway under aircraft loading with localized settlement in subsoil: a closed-form solution
inferred← citeshttps://doi.org/10.1016/j.trgeo.2025.101779openalex-snapshot
A meta-learning enhanced framework for prediction and control of shield-tunneling-induced ground deformation under data scarcity
inferred← citeshttps://doi.org/10.1016/j.trgeo.2025.101850openalex-snapshot
Spatiotemporal-information-driven surrogate modeling for predictive analysis of shield tunneling-induced settlement
inferred← citeshttps://doi.org/10.1016/j.trgeo.2026.102039openalex-snapshot
Uncertainty-aware prediction and decision support for disc-cutter consumption in hard-rock TBM tunnelling
inferred← citeshttps://doi.org/10.1016/j.tust.2024.106051openalex-snapshot
A novel structural deformation prediction method based on graph convolutional network during shield tunnel construction
inferred← citeshttps://doi.org/10.1016/j.tust.2025.106486openalex-snapshot
Intelligent prediction and optimization of ground settlement induced by shield tunneling construction
inferred← citeshttps://doi.org/10.1016/j.tust.2025.106632openalex-snapshot
Semi-supervised ensemble model for TBM rock mass classification
inferred← citeshttps://doi.org/10.1016/j.tust.2025.107266openalex-snapshot
Intelligent multi-class ground object subsidence susceptibility and visual risk mapping (GOSS-VRM) method for shield tunnel construction
inferred← citeshttps://doi.org/10.1016/j.tust.2025.107295openalex-snapshot
Data-physics dual-driven prediction of surface settlement induced by twin shield tunneling in composite strata
inferred← citeshttps://doi.org/10.1016/j.tust.2026.107449openalex-snapshot
Intelligent prediction of surface settlement troughs induced by twin shields tunnelling: Insights from a numerical modelling-empirical formulation-interpretable automated machine learning fusion method
inferred← citeshttps://doi.org/10.1016/j.tust.2026.108156openalex-snapshot
Integrated Transformer approach for immersed-tunnel settlement prediction considering spatial heterogeneity and temporal degradation of sand filling compactness
inferred← citeshttps://doi.org/10.1016/j.undsp.2025.04.008openalex-snapshot
Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line
inferred← citeshttps://doi.org/10.1080/13467581.2026.2694127openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/22797254.2024.2398108openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icrae59816.2023.10458482openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/adce/8960528openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/stc/3016498openalex-snapshot
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inferred← citeshttps://doi.org/10.1631/jzus.a2200573openalex-snapshot
Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4771476/v1openalex-snapshot
Intelligent support system for ground settlement management during TBM tunneling by combining machine learning with statistical analysis
inferred← citeshttps://doi.org/10.26599/rsm.2024.9435256openalex-snapshot
Long sequence time series model to predict uplift of segmental lining in shield tunnel based on LightGBM-Informer
inferred← citeshttps://doi.org/10.3389/feart.2025.1590203openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/feart.2025.1608468openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/app12199963openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/app16073451openalex-snapshot
Ensemble Machine Learning for Predicting TBM Penetration Rate with Limited Geotechnical Data
inferred← citeshttps://doi.org/10.3390/buildings13051335openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/geosciences13030082openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/pr11123309openalex-snapshot
Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination
inferred← citeshttps://doi.org/10.3390/s24082600openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/s24196320openalex-snapshot
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