Prediction of Rheological Properties of PVA Fiber and Nano-SiO2-Reinforced Geopolymer Mortar Based on Back Propagation Neural Network Model Optimized by Genetic Algorithm DOI Open Access

Guo Zhang,

Peng Zhang, Juan Wang

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(8), P. 1046 - 1046

Published: April 12, 2025

The rheological properties of mortar are vital importance to ensure the quality and durability engineering structures, improving construction efficiency adapting different environments. This research focused on examining geopolymer (GM) with incorporation metakaolin (MK), nano-SiO2 (NS) polyvinyl alcohol (PVA) fibers. varying concentrations PVA fiber ranging from 0 1.2% (interval 0.2%) NS 2.5% 0.5%). As mix proportion optimization GM is normally carried out experimentally, a significant amount labor material resources was consumed. Based large amounts authentic operation data, prediction model for NS- PVA-fiber-reinforced developed using back propagation (BP) neural network. Subsequently, parameters were refined genetic algorithm (GA) predict reinforced dosages fiber. Three parameters, including static yield stress, plastic viscosity dynamic used evaluate GM. Moreover, Root Mean Square Error (RMSE), Absolute Percentage (MAPE) (MAE) applied assess capability algorithms. When GA–BP network used, compared BP network, coefficient determination (R2) viscosity, stress increased by 4.40%, 2.11% 15.28%, respectively, provided superior fitting effect, higher accuracy faster convergence. outputs model, can be adopted as precise method forecast

Language: Английский

Single and synergistic effects of nano-SiO2 and hybrid fiber on rheological property and compressive strength of geopolymer concrete DOI
Yufeng Zhang, Peng Zhang, Jinjun Guo

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 472, P. 140945 - 140945

Published: March 26, 2025

Language: Английский

Citations

1
A state-of-the-art review on frost resistance of fiber-reinforced geopolymer composites DOI
Peng Zhang, Zhi Wen, Han Xu

et al.

Sustainable Chemistry and Pharmacy, Journal Year: 2025, Volume and Issue: 45, P. 102006 - 102006

Published: March 29, 2025

Language: Английский

Citations

1
CDW-Based Geopolymers: Pro and Cons of Using Unselected Waste DOI Open Access
Ilaria Capasso, Gigliola D’Angelo, Mercedes del Río Merino

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(5), P. 570 - 570

Published: Feb. 21, 2025

Geopolymer technology is considered a strategic alternative for recycling construction and demolition waste (CDW) to produce new products which meet the requirements of environmental energy sustainability. The separation management CDW fractions still technological complex process and, even if large-scale quite common, necessity perform this treatment may reduce economic benefits reuse. So, very promising option represented by manufacturing geopolymers using unseparated CDW. In aim, deriving from cement-based mortars, bricks natural stones have been selected widely characterized mineralogical, chemical morphological point view. Then, geopolymer mortars were produced several amounts either single fraction or mixture waste. chemical, physical, mechanical, microstructural characterization geopolymer-produced was carried out assess how combination different quantities mixed affected final properties. particular, geopolymeric unselected showed higher mechanical properties, despite lower apparent density, when compared improvement features seems be not amount used, providing encouraging findings promote actual use with resulting enhancement benefits.

Language: Английский

Citations

0
Enhanced mechanical and thermal performance of high-strength engineered geopolymer composites reinforced by hybrid polyethylene fibres and carbon nanotubes DOI Creative Commons
Yuekai Xie

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 472, P. 140884 - 140884

Published: March 19, 2025

Language: Английский

Citations

0
Prediction of Rheological Properties of PVA Fiber and Nano-SiO2-Reinforced Geopolymer Mortar Based on Back Propagation Neural Network Model Optimized by Genetic Algorithm DOI Open Access

Guo Zhang,

Peng Zhang, Juan Wang

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(8), P. 1046 - 1046

Published: April 12, 2025

The rheological properties of mortar are vital importance to ensure the quality and durability engineering structures, improving construction efficiency adapting different environments. This research focused on examining geopolymer (GM) with incorporation metakaolin (MK), nano-SiO2 (NS) polyvinyl alcohol (PVA) fibers. varying concentrations PVA fiber ranging from 0 1.2% (interval 0.2%) NS 2.5% 0.5%). As mix proportion optimization GM is normally carried out experimentally, a significant amount labor material resources was consumed. Based large amounts authentic operation data, prediction model for NS- PVA-fiber-reinforced developed using back propagation (BP) neural network. Subsequently, parameters were refined genetic algorithm (GA) predict reinforced dosages fiber. Three parameters, including static yield stress, plastic viscosity dynamic used evaluate GM. Moreover, Root Mean Square Error (RMSE), Absolute Percentage (MAPE) (MAE) applied assess capability algorithms. When GA–BP network used, compared BP network, coefficient determination (R2) viscosity, stress increased by 4.40%, 2.11% 15.28%, respectively, provided superior fitting effect, higher accuracy faster convergence. outputs model, can be adopted as precise method forecast

Language: Английский

Citations

0