RETURN

RETURN – Rethinking Tunnelling in Urban Neighbourhoods

 Coordinatore UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN 

Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.

 Nazionalità Coordinatore Ireland [IE]
 Totale costo 1˙500˙000 €
 EC contributo 1˙500˙000 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2012-StG_20111012
 Funding Scheme ERC-SG
 Anno di inizio 2013
 Periodo (anno-mese-giorno) 2013-01-01   -   2017-12-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN

 Organization address address: BELFIELD
city: DUBLIN
postcode: 4

contact info
Titolo: Dr.
Nome: Debra Fern
Cognome: Laefer
Email: send email
Telefono: +353 1 7163226
Fax: +353 1 7163297

IE (DUBLIN) hostInstitution 1˙500˙000.00
2    UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN

 Organization address address: BELFIELD
city: DUBLIN
postcode: 4

contact info
Titolo: Mr.
Nome: Donal
Cognome: Doolan
Email: send email
Telefono: +353 1 716 1656
Fax: +353 1 716 1216

IE (DUBLIN) hostInstitution 1˙500˙000.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

city    building    data   

 Obiettivo del progetto (Objective)

'This project addresses important challenges at the forefront of geotechnical engineering and building conservation by introducing an entirely new workflow and largely unexploited data source for the predic-tion of building damage from tunnel-induced subsidence. The project will also make fundamental and ground-breaking advances in the collection and processing of city-scale, aerial laser scanning by avoiding any reliance on existing data for building location identification, respective data affiliation, or building fea-ture recognition. This will create a set of techniques that are robust, scalable, and widely applicable to a broad range of communities with unreinforced masonry buildings. This will also lay the groundwork to rapidly generate and deploy city-scale, computational models for emergency management and disaster re-sponse, as well as for the growing field of environmental modelling.'

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