Probabilistic Estimation for Mechanical Properties of Self-Compacting Geopolymer Concrete Using Machine Learning Technique

Geopolymer concrete is environmentally friendly concrete as it relies on minor treated natural materials or industrial by-products like fly ash, GGBS, silica fumes etc,. which have high alumina (Al2O3) and silica (SiO2) content, significantly reducing carbon footprints. To overcome the challenge of compaction due to the highly viscous nature of geopolymer concrete, self-compacting geopolymer concrete (SCGC) has been developed to flow and compact under its weight, eliminating the need for additional compaction. Self-compacting geopolymer concrete is an innovative concrete that combines the benefits of geopolymer concrete and self-compacting concrete. In this study, mineral admixtures of fly ash, ultra fine ground granulated blast-furnace slag (GGBS), and micro silica were used in different mix proportions. For all mixes, the water-to-powder (binder content) mass ratio (w/p) was maintained as 0.35, the total powder content was 400 kg m−3, and glass fibre 1.5% of the binder content were used. The water to powder (binder content) mass ratio (w/p) selected after numerous trial mixes was 0.35. The test specimens were cured at 70 °C. In this study, to measure fresh properties, tests on concrete slump flow test, L-box test, V-Funnel test, and T50 V- Funnel test, J-ring were conducted. This paper illustrates the way an ANN (Artificial Neural Network) model may be employed to find the mix proportion of concrete mixes. The fresh and mechanical Properties of SCGC were conducted for different molarities of eight molarities, ten molarities, and twelve molarities. Microstructural studies such as x-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT-IR) analyses were carried out, and the results are presented.

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Probabilistic Estimation for Mechanical Properties of Self-Compacting Geopolymer Concrete Using Machine Learning Technique

Cited by 3 publications

References 51 publications

Fundamental machine learning algorithms and statistical models applied in strength prediction of geopolymers: a systematic review

Fundamental machine learning algorithms and statistical models applied in strength prediction of geopolymers: a systematic review

Prediction of fracture toughness of concrete using the machine learning approach

Experimental investigation on mechanical and microstructural properties of self-compacting geopolymer concrete with different mineral admixtures

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