Abstract
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.
Highlights
Concrete is a widely used building material in engineering constructions [1]
Pearson correlation analysis [43] and variance analysis were performed to analyze the between mix proportion and compressive strength, the linear regression method cannot reflect the compressive strength of the composites obtained from the linear regression equation above and the relationship between mix proportion and compressive strength well enough to accurately predict the actual compressive strength to assess the degree of interdependence between the two variables
Analysis shown in Table 4 shows that the significance P > 0.05, i.e., when the error is 0.05 [46], no significant difference appears between the xpredicted and actual compressive strength values. x is where, x is the normalized cement dosage; is the amount of quartz sand after normalization; the amount of polyvinyl alcohol (PVA) fiber after normalization; x6 is the amount of Nano-SiO2 after normalization
Summary
Concrete is a widely used building material in engineering constructions [1]. With the large-scale construction of long-span bridges, super-high-rise buildings, high-grade highways, large-scale water conservancy facilities, and cross harbor tunnels, concrete materials are endowed with higher expectations [2]. Polyvinyl alcohol (PVA) fiber-reinforced engineering cementitious composite is a kind of new high-performance cementitious material which exhibits the features of strain hardening, multiple-cracking high durability [17], and narrow crack width [18,19,20]. Neural networks have been widely applied to the research and prediction of concrete material properties to study the nonlinear and complex relationships between concrete material properties and mix proportion. A few studies reported the model establishment, prediction, and optimization for the mix proportions and compressive strength of PVA fiber-reinforced cementitious composites containing nano-SiO2. The BP neural network will be used to propose a method for compressive strength prediction of PVA fiber-reinforced cementitious composites containing nano-SiO2. The genetic algorithm was applied to optimize the mix proportion of PVA fiber-reinforced cementitious composites containing nano-SiO2. The results of this study can effectively guide the mix proportion test of composite materials, reduce the human and material consumption, and improve the test efficiency
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