Back Analysis of Brusnice Tunnel based on Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F17%3A00317879" target="_blank" >RIV/68407700:21110/17:00317879 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Back Analysis of Brusnice Tunnel based on Neural Networks

Popis výsledku v původním jazyce

The Brusnice tunnel is an important part of the Prague City Ring Road. The tunnel was excavated using NATM method (mostly in shales and deluvial sediments). The theotechnical monitoring was carried out during construction and its results give a good opportunity to perform a back analysis. After a short introduction, the authors briefly describe the selected cross-section, the geological conditions and geotechnical monitoring. In the following part there is mentioned the numerical model used to calculate stresses, tunnel deformation and surface settlement. The main part of the article is focused on the use of neural networks for back analysis of tunnel construction. The prediction of the tunnel deformation was determined using multi-layer neural network with back propagation. Its principles are described in the one of the chapters.

Název v anglickém jazyce

Back Analysis of Brusnice Tunnel based on Neural Networks

Popis výsledku anglicky

The Brusnice tunnel is an important part of the Prague City Ring Road. The tunnel was excavated using NATM method (mostly in shales and deluvial sediments). The theotechnical monitoring was carried out during construction and its results give a good opportunity to perform a back analysis. After a short introduction, the authors briefly describe the selected cross-section, the geological conditions and geotechnical monitoring. In the following part there is mentioned the numerical model used to calculate stresses, tunnel deformation and surface settlement. The main part of the article is focused on the use of neural networks for back analysis of tunnel construction. The prediction of the tunnel deformation was determined using multi-layer neural network with back propagation. Its principles are described in the one of the chapters.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/TE01020168" target="_blank" >TE01020168: Centrum pro efektivní a udržitelnou dopravní infrastrukturu (CESTI)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů