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
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