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Computers and Concrete Volume 15, Number 2, February 2015 , pages 167-181 DOI: https://doi.org/10.12989/cac.2015.15.2.167 |
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Modeling shotcrete mix design using artificial neural network |
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Khan Muhammad, Noor Mohammad and Fazal Rehman
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| Abstract | ||
| \"Mortar or concrete pneumatically projected at high velocity onto a surface\" is called Shotcrete. Models that predict shotcrete design parameters (e.g. compressive strength, slump etc) from any mixing proportions of admixtures could save considerable experimentation time consumed during trial and error based procedures. Artificial Neural Network (ANN) has been widely used for similar purposes; however, such models have been rarely applied on shotcrete design. In this study 19 samples of shotcrete test panels with varying quantities of water, steel fibers and silica fume were used to determine their slump, cost and compressive strength at different ages. A number of 3-layer Back propagation Neural Network (BPNN) models of different network architectures were used to train the network using 15 samples, while 4 samples were randomly chosen to validate the model. The predicted compressive strength from linear regression lacked accuracy with R2 value of 0.36. Whereas, outputs from 3-5-3 ANN architecture gave higher correlations of R2 = 0.99, 0.95 and 0.98 for compressive strength, cost and slump parameters of the training data and corresponding R2 values of 0.99, 0.99 and 0.90 for the validation dataset. Sensitivity analysis of output variables using ANN can unfold the nonlinear cause and effect relationship for otherwise obscure ANN model. | ||
| Key Words | ||
| ANN; shotcrete design; admixtures; sensitivity analysis | ||
| Address | ||
| Khan Muhammad, Noor Mohammad and Fazal Rehman: Department of Mining Engineering,University of Engineering & Technology, Peshawar, KP, Pakistan | ||