Feed forward back propagation artificial neural network modeling of compressive strength of self-compacting concrete
College
Gokongwei College of Engineering
Document Type
Conference Proceeding
Source Title
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)
First Page
1
Last Page
5
Publication Date
2018
Publisher
IEEE
Abstract
Predicting the compressive strength of self-compacting concrete (SCC) is one of the complicated tasks because of its complex behavior due to the reaction of chemical and mineral content and the hydration process of cement. The distinct difference of SCC to normal concrete is its improved workability or also known as rheology that is divided into four categories namely viscosity, flow ability, passing ability, and resistance to segregation. It was proposed in this study to include the rheological behavior of SCC to the prediction of its compressive strength. Neural network was utilized for predicting the 28 th day compressive strength of SCC. A 97.78% prediction rate was achieved using a feed-forward back-propagation ANN with 1 hidden layer and 8 hidden nodes. Tansig transfer function was used as activation function. The model has a Pearson R value of 0.991 and mean square error (MSE) of 3.42.
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Recommended Citation
Clemente, S. C., Calilung, M. V., Oreta, A. C., & Dadios, E. P. (2018). Feed forward back propagation artificial neural network modeling of compressive strength of self-compacting concrete. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 1-5. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/15346
Disciplines
Civil Engineering
Keywords
Self-consolidating concrete—Compression testing; Neural networks (Computer science)
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