Rheological and buildability characterization of PVA fiber-reinforced cementitious composites for additive construction

2021 ◽  
Author(s):  
Justine M. Schulte ◽  
Ugur Kilic ◽  
Ji Ma ◽  
Osman E. Ozbulut
Keyword(s):  
Cementitious Composites ◽  
Fiber Reinforced ◽  
Pva Fiber ◽  
Fiber Reinforced Cementitious Composites

Experimental Study on Mechanical Properties of PVA Fiber Reinforced Tailings Cementitious Composites

2012 ◽  
Vol 598 ◽  
pp. 618-621 ◽  
Author(s):  
Wen Bo Bao ◽  
Cheng Hong Wang ◽  
Shao Feng Zhang ◽  
Zhi Qiang Huang
Keyword(s):  
Fiber Length ◽  
Iron Ore ◽  
Cementitious Composites ◽  
Fiber Reinforced ◽  
Flexural Test ◽  
Multiple Cracking ◽  
Iron Ore Tailings ◽  
Mix Proportion ◽  
Pva Fiber ◽  
Fiber Reinforced Cementitious Composites

A type of ecological engineered cementitious composites, which use iron ore tailings to replace fine grinding quartz sand in PVA fiber reinforced cementitious composites, was developed. The flexural strength and toughness of this material were studied by four-point flexural test with samples of beam and sheet. The results show that the fiber reinforced tailings cementitious composites exhibit the characteristics of multiple cracking, high ductility and flexural toughness. The studies indicate that the mix proportion and the fiber length have a significant influence on the properties of this material, particularly for tensile toughness.

scholarly journals Compressive Strength Prediction of PVA Fiber-Reinforced Cementitious Composites Containing Nano-SiO2 Using BP Neural Network

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 521 ◽  
Author(s):  
Ting-Yu Liu ◽  
Peng Zhang ◽  
Juan Wang ◽  
Yi-Feng Ling
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Compressive Strength ◽  
Bp Neural Network ◽  
Cementitious Composites ◽  
Cementitious Composite ◽  
Fiber Reinforced ◽  
Nano Sio2 ◽  
Pva Fiber ◽  
Fiber Reinforced Cementitious Composites

In this study, a method to optimize the mixing proportion of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites and improve its compressive strength based on the Levenberg-Marquardt backpropagation (BP) neural network algorithm and genetic algorithm is proposed by adopting a three-layer neural network (TLNN) as a model and the genetic algorithm as an optimization tool. A TLNN was established to implement the complicated nonlinear relationship between the input (factors affecting the compressive strength of cementitious composite) and output (compressive strength). An orthogonal experiment was conducted to optimize the parameters of the BP neural network. Subsequently, the optimal BP neural network model was obtained. The genetic algorithm was used to obtain the optimum mix proportion of the cementitious composite. The optimization results were predicted by the trained neural network and verified. Mathematical calculations indicated that the BP neural network can precisely and practically demonstrate the nonlinear relationship between the cementitious composite and its mixture proportion and predict the compressive strength. The optimal mixing proportion of the PVA fiber-reinforced cementitious composites containing nano-SiO2 was obtained. The results indicate that the method used in this study can effectively predict and optimize the compressive strength of PVA fiber-reinforced cementitious composites containing nano-SiO2.

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