Neural network-based performance prediction of marine UHPC with coarse aggregates

In order to improve bearing capacity and service life of marine structure using marine UHPC with coarse aggregate (UHPC-CA), it is necessary to reasonably predict the performance of UHPC-CA. The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal component approach neural network (PCA-BP), and improved neural network based on genetic algorithm (GA-BP). Seven influencing factors were taken as input, such as coarse aggregate type, coarse aggregate content, steel fiber type, steel fiber content, water-binder ratio, rubber particle sand replacement rate and curing system. Mechanical and long-term performance of UHPC-CA were taken as outputs. The results show that artificial neural network can be applied to predict performance of UHPC-CA with multi-parameter input and multi-index output. In terms of the prediction accuracy of mechanical properties and long-term performance of UHPC-CA, the order is GA-BP > PCA-BP > T-BP > multiple nonlinear regression > multiple linear regression. The GA-BP neural network has the highest goodness of fit for the prediction of mechanical properties and long-term performance of UHPC-CA, which is 93.87%, 37.34%, 5.13% and 3.21% averagely higher than that of multiple linear regression, multiple nonlinear regression, T-BP and PCA-BP, respectively. Furthermore, GA-BP neural network has the lowest error index for each performance prediction. MAE, MSE and RMSE are 18.13%, 77.26% and 52.31% lower than PCA-BP on average.