Prediction of shear strength of concrete using the artificial neural network / R. Rohim, S.F. Senin and N.F. Azman

Rohim, Rohamezan and Senin, Syahrul Fithry and Azman, N.F. (2022) Prediction of shear strength of concrete using the artificial neural network / R. Rohim, S.F. Senin and N.F. Azman. ESTEEM Academic Journal, 18 (March): 4. pp. 36-47. ISSN 2289-4934

Abstract

Artificial neural networks (ANN) are known to be increasingly popular and used in several engineering applications, such as in the civil engineering field. In this study, this method was used to develop an optimal model to predict the shear strength of concrete using the experimental data sets. All the data sets were trained and tested using ANN to obtain the prediction of the shear strength of concrete material. The model ANN was trained and tested using test data sets obtained from 51 concrete mixes from previous experimental data sets. 33 (65%) concrete mixes data sets were chosen randomly and used as input for training. The remaining 18 (35%) mixes data were divided equally into testing and validation data sets. Feed-forward backpropagation was chosen for the neural network design and LevenbergMarquardt was used as the learning algorithm. An S-shaped sigmoid function was used to predict the probability as output between the range 0 to 1. Ten different types of architecture networks with different types of structures and neurons number were used to obtain the best model. The optimal ANN architecture (33-10-1) was found to have the highest correlation coefficient (R) of 0.99888 and the lowest mean square error (MSE) 0.00085. The shear strength based on the ANN model perfectly matched the values of the experimental data sets.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Rohim, Rohamezan
rohamezan627@uitm.edu.my
Senin, Syahrul Fithry
UNSPECIFIED
Azman, N.F.
UNSPECIFIED
Contributors:
Contribution
Name
Email / ID Num.
Advisor
Kasolang, Salmiah
UNSPECIFIED
Chief Editor
Damanhuri, Nor Salwa
UNSPECIFIED
Subjects: T Technology > TA Engineering. Civil engineering > Shear (Mechanics)
T Technology > TA Engineering. Civil engineering > Materials of engineering and construction > Concrete
T Technology > TA Engineering. Civil engineering > Materials of engineering and construction > Concrete > Strength and testing
Divisions: Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus
Journal or Publication Title: ESTEEM Academic Journal
UiTM Journal Collections: UiTM Journals > ESTEEM Academic Journal (EAJ)
ISSN: 2289-4934
Volume: 18
Number: March
Page Range: pp. 36-47
Keywords: optimal network, shear strength, artificial neural network, prediction model, activation function
Date: March 2022
URI: https://ir.uitm.edu.my/id/eprint/62585
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