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ARCSENN

ANALYSIS OF RC STRUCTURES EMPLOYING NEURAL NETWORKS

Total Cost €

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EC-Contrib. €

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Partnership

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Project "ARCSENN" data sheet

The following table provides information about the project.

Coordinator
HERIOT-WATT UNIVERSITY 

Organization address
address: Riccarton
city: EDINBURGH
postcode: EH14 4AS
website: www.hw.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website https://www.egis.hw.ac.uk/arcsenn/
 Total cost 183˙454 €
 EC max contribution 183˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2015
 Duration (year-month-day) from 2015-09-01   to  2017-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    HERIOT-WATT UNIVERSITY UK (EDINBURGH) coordinator 183˙454.00

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 Project objective

The primary objective of the proposed project is to develop a radically new structural analysis procedure capable of accurately predicting the nonlinear behaviour of reinforced concrete structures. The proposed approach will be developed within the Soft Computing framework and as a result will require significantly less computational resources than those of more traditional methods of structural analysis. The proposed procedure will simulate each RC element, the beam-column joints included, with a single neural network, which has first to be appropriately trained. The training process will be based on the combined use of published test data, numerical predictions obtained from nonlinear finite-element analyses and the predicted behaviour of published physical models of RC structural elements at their ultimate limit state. In order to model intricate structures, the individual Neural Networks will be combined through a new solution strategy so as to provide a representative model of the structure considered. The stability and robustness of the proposed structural analysis method, as well as the validity and objectivity of its predictions, will be ensured through a comparative study of the predicted behaviour of RC frames with its counterparts established experimentally and numerically via nonlinear finite element analysis. Throughout these studies, attention will be focussed on identifying parameters affecting the overall structural response of RC frames (such as the effect of crack-formation within the joint regions) as well as their implications on practical structural analysis and design. Overall, the proposed work will lead to a stable, robust and computationally efficient numerical procedure capable of realistically and objectively predicting the nonlinear response of RC structures and suitable, not only for research and practical applications, but also for solving design optimization and reliability problems which require extensive parametric investigations.

 Publications

year authors and title journal last update
List of publications.
2017 Gregoria Kotsovou, Demitrios Cotsovos
SHEAR FAILURE CRITERION FOR RC T-BEAMS
published pages: , ISSN: , DOI:
Engineering Structures (Under Review) 2019-07-24
2017 Afaq Ahmad, Gregoria Kotsovou, Demitrios Cotsovos, Nikos Lagaros
ASSESSING THE ACCURACY OF RC DESIGN CODE PREDICTIONS THROUGH THE USE OF ARTIFICIAL NEURAL NETWORKS
published pages: , ISSN: , DOI:
Engineering Structures (under review) 2019-07-24
2017 Gregoria Kotsovou, Afaq Ahmad, Demitrios Cotsovos, Nikos Lagaros
\"Reappraisal of methods for calculating flexural capacity of RC members\"\" has been received safely by Structures and Buildings\"
published pages: , ISSN: , DOI:
Structures and Buildings (under review) 2019-07-24
2016 Afaq Ahmad,Gregoria Kotsovou, Demetrios M. Cotsovos, Nikos Lagaros
ASSESSING THE LOAD CARRYING CAPACITY OF RC MEMBERS THROUGH THE USE OF ARTIFICIAL NEURAL NETWORKS
published pages: , ISSN: , DOI:
HSTAM International Congress on Mechanics 2019-07-24
2017 Gregoria Kotsovou (Cotsovos)
Report on Work Packages 1 to 6
published pages: 1 to 400, ISSN: , DOI:
2019-07-24

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