A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques
There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model developmentinvolved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor.
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques
There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model developmentinvolved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor.
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques
Awoyera, Paul (author) / Kirgiz, Mehmet S. (author) / amelec, viloria (author) / Ovallos, David (author)
2020-06-24
Journal of Materials Research and Technology ; https://www.sciencedirect.com/science/article/pii/S2238785420314095
Article (Journal)
Electronic Resource
English
DDC:
690
Self-compacting Geopolymer Concrete: A Critical Review
| Springer Verlag | 2024
Investigative Learning on Behavioral of Ternary Blended Self-Compacting Geopolymer Concrete
| BASE | 2019
Development of Self-Compacting Geopolymer Concrete Using Fayalite Slag Aggregates
| DOAJ | 2024
Geopolymer self-compacting concrete, concrete product and preparation method thereof
| European Patent Office | 2021
Mechanical Properties of Self-Compacting Geopolymer Concrete Using Fly Ash and GGBS
| BASE | 2018