Article Sidebar
Keywords:
Shear strength reinforced concrete Artificial Neural Network design equations
Article Details
Issue
Section
Articles
Copyright of the journal's content belongs to Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
The copyright of a submitted article is only transferred to the publishers if and when the article is accepted for publication. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, electrostatic, mechanical, photocopying, recording or otherwise without the prior written permission of the publishers.
The copyright of a submitted article is only transferred to the publishers if and when the article is accepted for publication. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, electrostatic, mechanical, photocopying, recording or otherwise without the prior written permission of the publishers.
Main Article Content
Artificial Neural Network Model for Low Strength RC Beam Shear Capacity
Abstract
This research was to investigate how the shear strength prediction of low strength reinforced concrete beams will improve under an ANN model. An existing database of 310 reinforced concrete beams without web reinforcement was divided into three sets of training, validation and testing. A total of 224 different architectural networks were tried, considering networks with one hidden layer as well as two hidden layers. Error measures of strength ratios were used to select the best ANN model which was then compared with 3 conventional design code equations in predicting the shear strength of 26 low strength RC beams. Even though the ANN was the most accurate, it was less conservative compared with the design code equations. A model reduction factor based on the characteristic strength concept is derived in this research and used to modify the ANN output. The modified ANN model is conservative in terms of safety and economy but not overly conservative as the conventional design equations. The procedure has been automated such that when new experimental sets are added to the database, the model can be updated and a new model could be developed.
