ARCSENN · ANALYSIS OF RC STRUCTURES EMPLOYING NEURAL NETWORKS
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2015-09-01 → 2017-08-31
- EU contribution
- €183,455
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
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Results in brief
ANALYSIS OF RC STRUCTURES EMPLOYING NEURAL NETWORKS
Structural analysis software employed in practice for predicting the behavior of reinforced concrete (RC) structures is based on simplified assumptions concerning concrete material behavior and structural response. Such software is often developed on the basis that the code provisions effectively safeguard flexural types of failure. However, this is not always the case in practice. On the other hand, more advanced structural analysis tools, mainly used in research, employ complex constitutive laws and numerical procedures which are often dependent on case-sensitive parameters which detrimentally affect the generality and objectivity of the predictions obtained. Such packages require higher computational resources and longer analysis time. In view of the above there are concerns regarding the ability of the existing structural analysis packages to provide solutions capable of safeguarding structural integrity and resilience. To address the above issues, present work employs Artificial Neural Networks (ANNs) [see Fig.1] for predicting the load-carrying capacity of individual RC structural components. These ANNs are trained directly through the use of published test data. They are then used for the development of a new structural analysis procedure capable of accurately predicting the behavior of RC structures by employing hybrid artificial neural network finite element analysis (ANN-FEA) models [see Fig.2] to represent the structure at hand. The proposed procedure requires significantly less computational resources compared to more traditional structural analysis methods based purely on the finite element method. The proposed analysis procedure provides a way of enhancing the ability of current structural analysis tools employed in practice to accurately predict the behavior of RC structures even when characterized by brittle modes of failure [see Fig.3] without requiring high computational resources or lengthy analysis time. Research Objectives: - Create databases containing valid test data on RC beams, columns and exterior beam-column joints. These databases are enriched with numerical data where necessary. - Employ these databases to develop ANNs capable of realistically predicting the load-carrying capacity of the above elements. - Use the trained ANNs to objectively assess the ability of the current design codes and alternative assessment methods to accurately predict the load-carrying capacity and failure mode of the RC elements considered. - Develop a computationally efficient ANN-based structural analysis procedure capable of accurately predicting the nonlinear response of RC structures. - Validate the proposed structural analysis procedure by comparing its predictions concerning the behavior of RC frames with its counterparts established experimentally and numerically [see Fig.2].
Data: CORDIS, © European Union
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.
Original text from CORDIS.
Participants
- HERIOT-WATT UNIVERSITY · EdinburghCoordinatorUnited Kingdom
Links
Data: CORDIS, © European Union