Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams

Mohammad Mohammadhassani , Hossein Nezamabadi-pour , Mohd Zamin Jumaat , Mohammed Jameel , Arul M S Arumugam

Abstract

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN

Key Words

deflection; deep beams; artificial neural network; high strength self compacting concrete; linear regression

Address

Mohammad Mohammadhassani — Department of Civil Engineering, University of Malaya, Malaysia
Hossein Nezamabadi-pour — Department of Electrical Engineering, Shahid Bahonar University of Kerman-Iran
Mohd Zamin Jumaat, Mohammed Jameel and Arul M S Arumugam — Department of Civil Engineering, University of Malaya, Malaysia

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