Abstract
Ultra-high performance concrete with coarse aggregates (UHPC-CA) has the advantages of high strength, strong shrinkage resistance and lower production cost, presenting a broad application prospect in the civil engineering construction. In view of the difficulty in establishing a mathematical model to accurately predict the mechanical properties of UHPC-CA, the back-propagation artificial neural network (BP-ANN) method is used to fully consider the various influential factors of the compressive strength (CS) and flexural strength (FS) of UHPC-CA in this paper. By taking the content of cement (C), silica fume (SF), slag, fly ash (FA), coarse aggregate (CA), steel fiber, water-binder ratio (w/b), sand rate (SR), cement type (CT), and curing method (CM) as input variables and the CS and FS as output objective, the BP-ANN model with three layers has been well-trained, validated and tested with 193 experimental data in the published literatures. The prediction accuracy of BP-ANN model has been evaluated by the evaluating indicators. A parametric study for the various influential factors on the CS and FS of UHPC-CA was conducted by the BP-ANN model and the corresponding influential mechanisms were analyzed. Finally, the inclusion levels for the CA, steel fiber, and the dimensionless parameters of w/b and sand rate were recommended to obtain the optimal strength of UHPC-CA.