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Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Bayesian Inference: An Introduction to Principles and Practice in Machine Learning
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Smooth relevance vector machine: a smoothness prior extension of the RVM
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Statistical downscaling of GCM simulations to streamflow using relevance vector machine
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Application of the relevance vector machine to canal flow prediction in the Sevier River Basin
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Geomechanical parameters identification by particle swarm optimization and support vector machine
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A numerical study of the effect of soil and grout material properties and cover depth in shield tunnelling
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A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays
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Support vector machine applied to settlement of shallow foundations on cohesionless soils
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Integrating relevance vector machines and genetic algorithms for optimization of seed-separating process
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Wavelet support vector machine for induction machine fault diagnosis based on transient current signal
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Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
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The forecasting model based on wavelet ν-support vector machine
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Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines
inferredcites →https://doi.org/10.1016/j.tust.2005.02.001openalex-snapshot
Estimating wall deflections in deep excavations using Bayesian neural networks
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Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling
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.2008.10.007openalex-snapshot
Stability analysis of large slurry shield-driven tunnel in soft clay
inferredcites →https://doi.org/10.1016/j.tust.2011.03.005openalex-snapshot
Wavelet Analysis for tunneling-induced ground settlement based on a stochastic model
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/10.1016/s0926-5805(03)00018-9openalex-snapshot
Neural-network-based regression model of ground surface settlement induced by deep excavation
inferredcites →https://doi.org/10.1016/s0926-5805(96)00165-3openalex-snapshot
Application of neural networks to automatic soil pressure balance control for shield tunneling
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Evaluation of Soil Response to EPB Shield Tunneling
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Neural Network Forecast Model in Deep Excavation
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Bayesian Method for Groundwater Quality Monitoring Network Analysis
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Bayesian Wavelet Analysis with a Model Complexity Prior
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Time series prediction based on the Relevance Vector Machine with adaptive kernels
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Sparse Bayesian Modeling With Adaptive Kernel Learning
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Prediction of Ground Settlements Associated with Shield Tunnelling
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Gaussian Processes for Machine Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Artificial Intelligence Prediction of Surface Settlement Inducd by Twin Shields Tunnelling
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Influence of double-line large-slope shield tunneling on settlement of ground surface and mechanical properties of surrounding rock and segment
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Artificial intelligence driven tunneling-induced surface settlement prediction
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Multi-objective optimization of TBM-induced building settlement control considering physical constraints
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Comparison of machine learning methods for ground settlement prediction with different tunneling datasets
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Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets
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