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Analysis and Prediction of Shield-Tunneling Parameters Under Complex Risk Factors

https://doi.org/10.2139/ssrn.4757480
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Development of web-based system for safety risk early warning in urban metro construction
inferredcites →https://doi.org/10.1016/j.compstruc.2017.03.020openalex-extracted
Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes
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Research on a hybrid model for cooling load prediction based on wavelet threshold denoising and deep learning: A study in China
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MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios’ risks and opportunities on multi-scale infrastructure systems
inferredcites →https://doi.org/10.1016/j.neucom.2015.04.032openalex-extracted
Common nature of learning between BP-type and Hopfield-type neural networks
inferredcites →https://doi.org/10.1016/j.trgeo.2022.100837openalex-extracted
Prediction of shield machine posture using the GRU algorithm with adaptive boosting: A case study of Chengdu Subway project
inferredcites →https://doi.org/10.1016/j.tust.2014.07.003openalex-extracted
Prediction of roadheader performance by artificial neural network
inferredcites →https://doi.org/10.1016/j.tust.2023.105249openalex-extracted
Artificial intelligence for tunnel boring machine penetration rate prediction
inferredcites →https://doi.org/10.1016/j.undsp.2022.11.002openalex-extracted
TBM performance prediction using LSTM-based hybrid neural network model: Case study of Baimang River tunnel project in Shenzhen, China
inferredcites →https://doi.org/10.1016/j.ymssp.2020.107386openalex-extracted
Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network
inferredcites →https://doi.org/10.1088/1755-1315/459/2/022033openalex-extracted
Control of Environmental Safety in the Construction of Subway in Megapolis
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