Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling-Reference-Cited by-同舟云学术

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Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling

Published:2020-11 Issue: Volume:149 Page:102869
ISSN:0965-9978
Container-title:Advances in Engineering Software
language:en
Short-container-title:Advances in Engineering Software

Author:

Cao B.T.,Obel M.,Freitag S.,Mark P.,Meschke G.

Funder

German Research Foundation

Publisher

Elsevier BV

Subject

General Engineering,Software

Reference62 articles.

1. Deep excavations and tunneling in soft ground;Peck,1969

2. Soil movements induced by tunnelling and their effects on pipelines and structures;Attewell,1986

3. Allowable settlement of structures;Bjerrum,1963

4. Settlement of buildings and associated damage;Burland,1975

5. Building response to excavation–induced settlement;Boscardin;J Geotech Eng,1989

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2. Data-Based postural prediction of shield tunneling via machine learning with physical information;Computers and Geotechnics;2024-10

3. Data-driven and physics-informed neural network for predicting tunnelling-induced ground deformation with sparse data of field measurement;Tunnelling and Underground Space Technology;2024-10

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5. Study of the influence of spatial effects on the ground surface and buildings in a two-lane tunnel boring;Heliyon;2024-09

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