Artificial Neural Network for Strength Prediction of Concrete incorporating Nano silica
Paper ID : 1440-ICNS
Mohammadreza Moshtaghi1, Abbas Pourdeilami *2, Reza Arabsarhangi3
1Shahrood University of Technology
2School of Engineering, Damghan University, Semnan, Iran
3Semnan University
Nano-particles have been gaining increasing attention and have been applied in many fields to fabricate new materials with novelty functions due to their unique physical and chemical properties. The use of nano-particle materials in concrete can add many benefits that are directly related to the durability of various cementitious materials, besides the fact that it is possible to reduce the quantities of cement in the composite. This paper investigates the applicability of an artificial neural network model for strength prediction of nano concrete under compression. The available 25 experimental data samples of concrete containing nano-silica were used in this research work. In this paper, computational-based research is carried for predicting the strength of concrete under compression, and the model was developed using ANN with five input nodes and feed-forward three-layer back-propagation neural networks with ten hidden nodes were examined using a learning algorithm. ANN model proposed analytically was verified, and it gives more compatible results. Hence, the ANN model is proposed to predict the strength of concrete containing nano-silica under compression. The validity of the proposed neural network-based technique was proven by comparing the estimated strength with the compressive testing results of the concrete specimen. The maximum error between the estimated and tested results was 8.9% in the specified strengths.
Neural Network ; Mechanical properties ; Concrete ; Nano-silica
Status : Abstract Accepted (Poster Presentation)