Exploring the potential of neural network in assessing mechanical properties of geopolymer concrete incorporating fly ash and phosphogypsum in pavement applications

被引：0
作者：
Pratap B. [1 ]
Shubham K. [1 ]
Mondal S. [1 ]
Rao B.H. [2 ]
机构：
[1] Department of Civil Engineering, National Institute of Technology, Jharkhand, Jamshedpur
[2] School of Infrastructure, Indian Institute of Technology, Odisha, Bhubaneswar
来源：
Asian Journal of Civil Engineering | 2023年 / 24卷 / 8期
关键词：
ANN; Byproducts; Fly ash; Geopolymer concrete; Phosphogypsum; Sodium hydroxide;
D O I：
10.1007/s42107-023-00735-w
中图分类号：
学科分类号：
摘要：
The purpose of this research study is twofold: (1) to investigate the factors that influence the mechanical properties of Geopolymer Concrete (GPC) made from fly ash (FA) and phosphogypsum (PG), and (2) to develop a method for predicting the mechanical properties based on experimental data. To achieve this, the researchers prepared GPC samples with varying levels of PG powder as a replacement for FA at different sodium hydroxide (NaOH) molarities, and tested their mechanical properties. The optimum mix design was obtained at 30% partial replacement of FA with PG at 12 M NaOH, and maximum compressive strength achieved was 47.97 MPa. Three distinct artificial neural network (ANN) techniques were then employed to predict the mechanical properties (Compressive Strength, Flexural Strength, and Split Tensile Strength) of the GPC using the experimental data. The accuracy of the predictions was assessed using performance statistics based on coefficient of determination (R2), root mean square error (RMSE), and Taylor diagram. The ANN models show good prediction efficiency with R2 value above 0.85. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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页码：3575 / 3589
页数：14
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