NISCAIR Online Periodicals Repository

Research Journals >
Indian Journal of Engineering and Materials Sciences (IJEMS) >
IJEMS Vol.19 [2012] >
IJEMS Vol.19(2) [April 2012] >

Title: Predicting the flexural behaviour of reinforced concrete and lightweight concrete beams by ANN
Authors: Kamanli, Mehmet
Kaltakci, M Yasar
Bahadir, Fatih
Balik, Fatih S
Korkmaz, H Husnu
Donduren, M Sami
Cogurcu, M Tolga
Keywords: Mechanical properties
Reinforced concrete
Issue Date: Apr-2012
Publisher: NISCAIR-CSIR, India
Abstract: In this study, artificial neural network (ANN) method is used to predict the deflection values of beams and compared with the experimental results of a testing series. For this purpose six reinforced concrete beams with constant rectangular cross-section are prepared and tested under pure bending. The concrete of the test specimens is casted using the lightweight aggregates obtained from volcanic sediments. The lightweight concrete has some advantages comparing the traditional concrete, such as less self weight, less earthquake forces due to decreased mass, good sound and thermal insulation. The use of lightweight concrete in the construction industry is popular due to various advantages. The neural network procedure is applied to determine or predict the deflection values of 1/1 scaled model beams. The analytical results are compared with the test results and further predictions, including different mix designs can be possible at the end of the study. As a result, while the statistical values RMSE, R2 and MAE from training in ANN model are found as 0.266, 99.2% and 0.216, respectively, these values are found in testing as 0.370, 96.47% and 0.419, respectively.
Page(s): 87-94
CC License:  CC Attribution-Noncommercial-No Derivative Works 2.5 India
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Source:IJEMS Vol.19(2) [April 2012]

Files in This Item:

File Description SizeFormat
IJEMS 19(2) 87-94.pdf441.1 kBAdobe PDFView/Open
 Current Page Visits: 117 
Recommend this item


Online Submission of Articles |  NISCAIR Website |  National Knowledge Resources Consortium |  Contact us |  Feedback

Disclaimer: NISCAIR assumes no responsibility for the statements and opinions advanced by contributors. The editorial staff in its work of examining papers received for publication is helped, in an honorary capacity, by many distinguished engineers and scientists.

CC License Except where otherwise noted, the Articles on this site are licensed under Creative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India

Copyright © 2015 The Council of Scientific and Industrial Research, New Delhi. All rights reserved.

Powered by DSpace Copyright © 2002-2007 MIT and Hewlett-Packard | Compliant to OAI-PMH V 2.0

Home Page Total Visits: 171806 since 01-Sep-2015  Last updated on 30-Jun-2016Webmaster: