Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in Predicting the Compressive Strength of POFA Concrete

Ahmad Nurfaidhi Rizalman, Chen Choon Lee

Abstract

This study presents a comparative study between Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in predicting the compressive strength of palm oil fuel ash (POFA) concrete. The comparison was made based on the same experimental datasets. The inputs investigated in this study were percentage of POFA replacement and water-to-cement ratio. The methods employed in ANN and RSM were feedforward neural network and face-centered central composite, correspondingly. The comparison between the two models showed that RSM performed better than ANN with coefficient of determination (R2) closer to 1 with 0.9959. In addition, all the predicted results by RSM against the experimental results fell within 10% margin. For ANN model, however, three of its predicted results were outside the 10% margin. Percentage of POFA as cement replacement was also found to have greater impacts on the compressive strength of concrete than water-to-cement ratio. Lastly, the optimization of the proportions using RSM predicted that the maximum strength of POFA concrete is 32.19 MPa.

Keywords

Artificial neural network; Compressive strength; Palm oil fuel ash concrete; Response surface methodology; Workability.

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References

A. Hammoudi, K. Moussaceb, C. Belebchouche and F. Dahmoune, Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled aggregates, Construction and Building Materials, 209, 2019, 425-436.

T. F. Awolusi, O. L. Oke, O. O. Akinkurolere and O. D. Atoyebi, Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres, Cogent engineering, 6, 2019, 1649852.

F. Khademi, S. M. Jamal, N. Deshpande and S. Londhe, Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adapative neuro-fuzzy inference system and multiple linear regression, International Journal of Sustainable Built Environment, 5, 2016, 355-369.

B. Lian, T. Sun and Y. Song, Parameter sensitivity analysis of a 5-DoF parallel manipulator, Robotics and Computer-Integrated Manufacturing, 46, 2017, 1-4.

K. S. Kumar and K. Baskar, Response surfaces for fresh and hardened properties of concrete with e-waste (HIPS), Journal of Waste Management, 2014, 517219, 14 pages.

B. Simsek, Y. T. Ic and E. H. Simsek, A RSM-based multi-response optimization application for determining optimal mix proportions of standard ready-mixed concrete, Arabian Journal of Science and Engineering, 41, 2016, 1435-1450.

T. F. Awolusi, O. L. Oke, O. O. Akinkurolere and A. O. Sojobi, Application of response surface methodology: predicting and optimizing the properties of concrete containing steel fibre extracted from waste tires with limestone powder as filler, Case studies in Construction materials, 10, 2019, 1-21.

P. B. Sakthivel, A. Ravichandran and N. Alagumurthi, Modeling and prediction of flexural strength of hybrid mesh and fiber reinforced cement-based composites using artificial neural network (ANN), International Journal of GEOMATE, 10(1), 2016, 1623-1635.

A. Khashman and P. Akpinar, Non-destructive prediction of concrete compressive strength using neural networks, Procedia Computer Science, 108, 2017, 2358-2362.

K. J. T. Elevado, J. G. Galupino and R. S. Gallardo, Artificial neural network (ANN) modelling of concrete mixed with waste ceramic tiles and fly ash, International Journal of GEOMATE, 15(51), 2018, 154-159.

S. Kim, H-B. C., Y. Shin, G-H. K. and D-S. Seo, Optimizing the mixing proportion with neural networks based on genetic algorithms for recycled aggregate concrete, Advances in Materials Science and Engineering, 2013, 527089, 10 pages.

S. M. A. B. Hacene, F. Ghomari and F. Schoefs, Probabilistic modelling of compressive strength of concrete using response surface methodology and neural networks, Arabian Journal of Science and Engineering, 39, 2014, 4451-4460.

H. Golizadeh and S. B. Namini, Predicting the significant characteristics of concrete containing palm oil fuel ash, Journal of Construction in Developing Countries, 20(1), 2015, 85-98.

M. Safiuddin, S. N. Raman, M. A. Salam and M. Z. Jumaat, Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash, Materials, 9(5), 2016, 396.

M. A. A. Aldahdooh, N. M. Bunnori, and M. A. M. Johari, Development of green ultra-high performance reinforced concrete containing ultrafine palm oil fuel ash, Construction and Building Materials, 48, 2013, 379-389.

Z. Emdadi, N. Asim, M. H. Amin, M. A. Yarmo, A. Maleki, M. Azizi and K. Sopian, Development of green geopolymer using agricultural and industrial waste materials with high water absorbency, Applied Sciences, 7(5), 2017, 514.

W. N. F. W. Hassan, M. A. Ismail, H-S. Lee, M. S. Meddah, J. K. Singh, M. W. Hussin and M. Ismail. Mixture optimization of high-strength blended concrete using central composite design, Construction and Building Materials, 243, 2020, 118251.

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