Evaluation of Optimal Substitute Ratio of Fly Ash Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity Study

Abstract:

Fly ash is widely used for replacing partial cement and producing high-performance concrete. The concrete production company is interested in the optimal substitute ratio of fly ash. This study presents a general procedure for evaluating the optimal substitute ratio of fly ash. First, the compressive strength of fly ash blended concrete is evaluated based on adaptive neuro-fuzzy inference system (ANFIS). The water-to-binder ratio, fly ash replacement ratio, and ages are used as input parameters of ANFIS. Strength is the output parameter of ANFIS. Second, sensitivity analysis is performed using ANFIS. The development of relative strength of fly ash blended concrete is calculated considering water-to-binder ratio, fly ash replacement ratio, and ages. The analysis results show that the optimal replacement fly ash is dependent on water-to-binder ratio of concrete. As thewater-to-binder ratio decreases from 0.5 to 0.3, the optimal substitute ratio of fly ash increases from 15% to 30%.

• Periodical: Materials Science Forum (Volume 1029)
• Pages: 105-110
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.1029.105
• Online since: May 2021
• Authors: Tae Wan Kim, Jong Yeon Lim, Xiao Yong Wang*, Yi Han
• Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), Fly Ash, Optimal Substitute Ratio, Sensitivity Analysis, Strength

References

[1] Wang XY, Luan Y. Modeling of Hydration, Strength Development, and Optimum Combinations of Cement-Slag-Limestone Ternary Concrete. International Journal of Concrete Structures and Materials 12 (2018) 1-13.

DOI: 10.1186/s40069-018-0241-z

[2] Wang XY. Impact of Climate Change on Proportional Design of Fly‑Ash‑Blended Low‑CO2 Concrete. ACI Materials Journal 116(2019) 141-151.

DOI: 10.14359/51714465

[3] Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Construction and Building Materials 24 (2010) 709-718.

DOI: 10.1016/j.conbuildmat.2009.10.037

[4] Yuan Z, Wang LN, Ji X. Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS. Advances in Engineering Software 67 (2014) 156-163.

DOI: 10.1016/j.advengsoft.2013.09.004

[5] Sadrmomtazi A, Sobhani J, Mirgozar MA. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction and Building Materials 42 (2013) 205-216.

DOI: 10.1016/j.conbuildmat.2013.01.016

[6] Nedushan BA. Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models. Construction and Building Materials 36 (2012) 665-673.

DOI: 10.1016/j.conbuildmat.2012.06.002

[7] Boga AR, Ozturk M, Topçu IB. Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI. Composites: Part B 45 (2013) 688-696.

DOI: 10.1016/j.compositesb.2012.05.054

[8] Amani J, Moeini R. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Scientia Iranica A 19(2012), 242–248.

DOI: 10.1016/j.scient.2012.02.009

[9] Nazari A, Khalaj G, Riahi S. ANFIS-based prediction of the compressive strength of geopolymers with seeded fly ash and rice husk–bark ash. Neural Comput & Applic 22(2013) 689–701.

DOI: 10.1007/s00521-011-0751-y

[10] Lam L, Wong YL, Poon CS. Effect of fly ash and silica fume on compressive and fracture behaviors of concrete. Cement and Concrete Research 28(1998) 271–283.

DOI: 10.1016/s0008-8846(97)00269-x

[11] www.mathworks.com.

[12] Z. Mo, X. Gao and A. Su, Mechanical performances and microstructures of metakaolin contained UHPC matrix under steam curing conditions, Construction and Building Materials, https://doi.org/10.1016/j.conbuildmat.2020.121112.

DOI: 10.1016/j.conbuildmat.2020.121112

[13] Ling Qin, Xiaojian Gao, Anshuang Su, QiyanLi. Effect of carbonation curing on sulfate resistance of cement-coal gangue paste. Journal of Cleaner Production 278(2021)123897.

DOI: 10.1016/j.jclepro.2020.123897