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ANALYSIS OF RC STRUCTURES EMPLOYING NEURAL NETWORKS

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.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

MSCA-IF-EF-ST - Standard EF

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-MSCA-IF-2014

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Coordinator

HERIOT-WATT UNIVERSITY
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 183 454,80
Address
RICCARTON
EH14 4AS Edinburgh
United Kingdom

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Region
Scotland Eastern Scotland Edinburgh
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 183 454,80
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