Strength prediction of desert sand concrete based on genetic algorithm optimized BP neural network
Desert sand concrete needs to do a large number of tests for verification before application in engineering construction,which not only affects the construction cycle,but also consumes a large amount of construction materials.For the desert sand concrete strength by a variety of influencing factors coupled role,the traditional prediction model has certain defects,this study with the help of global search ability of genetic algorithm to improve the neural network,the input layer parameters for the water-cement ratio,sand rate,desert sand substitution rate,the amount of fly ash mixing,the amount of water-reducing agent to establish genetic algorithm optimisation of the BP neural network of the desert sand concrete strength prediction model.And the strength of desert sand concrete predicted by BP neural network is compared with the prediction result of genetic algorithm optimised BP neural network through example verification.The results show that the desert sand concrete strength prediction model based on genetic algorithm optimised BP neural network has better operability and prediction accuracy,which opens up a new way to improve the prediction accuracy of desert sand concrete strength.
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