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Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses

https://doi.org/10.1016/j.aei.2020.101097

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

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Prediction of shield tunneling-induced ground settlement using machine learning techniques

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inferredcites →https://doi.org/10.1016/j.tust.2019.03.021openalex-snapshot

Deformation behaviors of existing tunnels caused by shield tunneling undercrossing with oblique angle

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Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study

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Analysis of variance designs for model output

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Neural network based prediction of ground surface settlements due to tunnelling

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Modular Neural Networks for Predicting Settlements during Tunneling

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inferredcites →https://doi.org/10.1061/(asce)gm.1943-5622.0000817openalex-snapshot

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inferredcites →https://doi.org/10.1061/(asce)gm.1943-5622.0001748openalex-snapshot

Analytical and Semi-Analytical Solutions for Describing Tunneling-Induced Transverse and Longitudinal Settlement Troughs

inferredcites →https://doi.org/10.1061/(asce)gt.1943-5606.0000948openalex-snapshot

Ground Movements due to Shallow Tunnels in Soft Ground. I: Analytical Solutions

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inferredcites →https://doi.org/10.1139/t10-023openalex-snapshot

Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling

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Analysis of undrained soil deformation due to ground loss

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Surface settlements due to deformation of a tunnel in an elastic half plane

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Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest

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