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

Yunhao Luan ¹ Dongbo Cai ^2* Deming Wang ^3* Changqing Luo ^2* Anni Wang ^1* Chao Wang ^2* Degao Kong ^2* Chaohui Xu ^2* Sining Huang ^1*

• ¹ China University of Petroleum, Qingdao, China
• ² CCCC First Highway Engineering Group Co., Ltd, Bei jing, China
• ³ Shandong Jiaotong University, Jinan, Shandong, China

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.