Assessment of RC exterior beam-column Joints based on artificial neural networks and other methods

•Development of a database comprising experimental information of 153 RC beam-column elements.•Development of an ANN model so as to predict the failure mode and strength of the subject elements.•Verification of the proposed ANN by comparing its predictions with the available test data.•Comparative s...

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Bibliographic Details
Published in:Engineering structures Vol. 144; pp. 1 - 18
Main Authors: Kotsovou, Gregoria M., Cotsovos, Demitrios M., Lagaros, Nikos D.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.08.2017
Elsevier BV
Subjects:
Artificial neural networks
Beam-columns
Carrying capacity
Codes of practice
Design
External beam-column joints
Load carrying capacity
Neural networks
Reinforced concrete
Design
External beam-column joints
Codes of practice
Artificial neural networks
Reinforced concrete
ISSN:0141-0296, 1873-7323
Online Access:Get full text
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Summary:•Development of a database comprising experimental information of 153 RC beam-column elements.•Development of an ANN model so as to predict the failure mode and strength of the subject elements.•Verification of the proposed ANN by comparing its predictions with the available test data.•Comparative study of the predictions of the ANN with those obtained from codes and other methods. A database on the behaviour of reinforced concrete external beam-column joint sub-assemblages established from the results of over 150 tests is developed and used for the development, training and validation of an artificial neural network (ANN) based model. The ANN model predictions on the mode of failure and load-carrying capacity of the joints, together with the predictions of widely used code methods and those of a recently proposed method, which does not require calibration through the use of test data, are compared with their counterparts stored in the database developed herein. The comparison confirms the already reported shortcomings of current code methods and demonstrates that both ANN model and the recently proposed method can provide reliable alternatives to the code methods.
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ISSN:0141-0296
1873-7323
DOI:10.1016/j.engstruct.2017.04.048