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.

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsong Tu ◽  
Yuanzhen Liu ◽  
Ming Zhou ◽  
Ruixia Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Compressive Strength ◽  
Thermal Insulation ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Training Sample ◽  
Recycled Aggregate ◽  
Generalization Performance ◽  
Content Type

Purpose This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately. Design/methodology/approach The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform. Findings Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better. Originality/value The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

scholarly journals Durability Assessment of PVA Fiber-Reinforced Cementitious Composite Containing Nano-SiO2 Using Adaptive Neuro-Fuzzy Inference System

Crystals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 347
Author(s):  
Ting-Yu Liu ◽  
Peng Zhang ◽  
Qing-Fu Li ◽  
Shao-Wei Hu ◽  
Yi-Feng Ling
Keyword(s):  
Evaluation System ◽  
Fuzzy Inference ◽  
Cementitious Composites ◽  
Expert Evaluation ◽  
Cementitious Composite ◽  
Fiber Reinforced ◽  
Inference System ◽  
Nano Sio2 ◽  
Neuro Fuzzy ◽  
Durability Assessment

In this study, the durability of polyvinyl alcohol fiber-reinforced cementitious composite containing nano-SiO2 was evaluated using the adaptive neuro-fuzzy inference system (ANFIS). According to the structural characteristics of the cementitious composite material and some related standards, the classification criteria for the evaluation indices of cementitious composite materials were clarified, and a corresponding structural framework of durability assessment was constructed. Based on the hypothesis testing principle, the required test data capacity was determined under a certain degree of accuracy, and durability experimental data and expert evaluation results were simulated according to statistical principles to ensure that there were sufficient datasets for ANFIS training. Using an environmental factor submodule as an example, 14 sets of actual test data were used to verify that the ANFIS can quickly and effectively mimic the expert evaluation reasoning process to evaluate the durability of cementitious composites. Compared with other studies related to the durability of cementitious composites, a systematic evaluation system for the durability of concrete was established. We used a polyvinyl alcohol fiber-reinforced cementitious composite containing nano-SiO2 to conduct a comprehensive evaluation of cementitious composites. Compared with the traditional expert evaluation method, the durability evaluation system based on the ANFIS learned expert experience, stored the expert experience in fuzzy rules, and eliminated the subjectivity of expert evaluation, thereby making the evaluation more objective and scientific.

scholarly journals Effect of PVA fiber on durability of cementitious composite containing nano-SiO2

2019 ◽  
Vol 8 (1) ◽  
pp. 116-127 ◽  
Author(s):  
Peng Zhang ◽  
Qing-fu Li ◽  
Juan Wang ◽  
Yan Shi ◽  
Yi-feng Ling
Keyword(s):  
Volume Fraction ◽  
Fiber Content ◽  
Nano Particles ◽  
Cementitious Composites ◽  
Cementitious Composite ◽  
Fiber Reinforced ◽  
Cracking Resistance ◽  
Carbonation Resistance ◽  
Pva Fiber

Abstract In the current investigation, the influence of polyvinyl alcohol (PVA) fibers on flowability and durability of cementitious composite containing fly ash and nano-SiO2 was evaluated. PVA fibers were added into the composite at a volume fraction of 0.3%, 0.6%, 0.9%, and 1.2%. The flowability of the fresh cementitious composite was assessed using slump flow. The durability of cementitious composite includes carbonation resistance, permeability resistance, cracking resistance as well as freezing-thawing resistance, which were evaluated by the depth of carbonation, the water permeability height, cracking resistance ratio of the specimens, and relative dynamic elastic modulus of samples after freeze-thaw cycles, respectively. The results indicated that addition of PVA fibers had a little disadvantageous influence on flowability of cementitious composite, and the flowability of the fresh mixtures decreased with increases in PVA fiber content. Incorporation of PVA fibers significantly improved the durability of cementitious composites regardless of addition of nano-particles. When the fiber content was less than 1.2%, the durability indices of permeability resistance and cracking resistance increased with fiber content. However, the durability indices of carbonation resistance and freezing-thawing resistance began to decrease as the fiber dosage increased from 0.9% to 1.2%. The fiber reinforced cementitious composite exhibited better durability due to addition of nano-SiO2 particles. Nano-SiO2 particle improves microscopic structure of fiber reinforced cementitious composites, and the nano-particles are beneficial for PVA fibers to play the role of reinforcement in 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.

Compressive Strength and Flexural Properties of High Performance Nano-Binder Cementitious Composites

2013 ◽  
Vol 275-277 ◽  
pp. 2064-2068 ◽  
Author(s):  
Xiang Gao ◽  
Qing Hua Li ◽  
Shi Lang Xu
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
High Performance ◽  
Flexural Properties ◽  
Cementitious Composites ◽  
Nano Sio2 ◽  
The Matrix ◽  
Average Crack ◽  
Sio2 Particles ◽  
Pva Fiber

High performance nano-binder cementitious composites (HPNCC) are ultra-ductile fiber reinforced cementitious composites with special matrix. The compressive strength and flexural properties of HPNCC containing nano-SiO2 particles were investigated at age of 3d, 7d, 14d and 28d. According to the results, HPNCC exhibited excellent mechanical properties in the test. The compressive strength, flexural strength and first crack strain of HPNCC were all increased obviously at early age except the ultimate strain. In the flexural test, both crack extension width and the number of fine cracks decrease along with the curing age. However, the average crack spacing has no remarkable changes. Nano-SiO2 particles in HPNCC acted as ultra-fine fillers and catalyzers to strengthen the interfacial bond between the matrix and PVA fiber which improved the mechanical properties and would make HPNCC be widely used in the engineering.

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