LAUSR

Abstract Fiber Reinforced Polymer has been widely used in the retrofit of existing structures and the construction of new structures. The ultimate conditions and stress–strain model of FRP-confined composites are critical to structural design and prediction of structural response, especially under extreme loads such as earthquakes. In this paper, a data-driven neural network prediction model for ultimate conditions and stress–strain constitutive relation of FRP-confined concrete is proposed, and the validity and accuracy of the model are verified. A uniaxial compression database containing 169 FRP-confined normal concrete cylinders is collected from the open literature, and the quality of the database is examined and evaluated in detail. Based on the feed forward neural network technology, a prediction model for the ultimate conditions of FRP-confined normal concrete cylinders is established. Configurations and hyper-parameters of the network are carefully analyzed, and the optimal model is used for prediction and comparison. Besides, a uniaxial stress–strain model for FRP-confined concrete is established using a neural network with a recursive structure. The prediction accuracy of the proposed model is proven to be superior to the existing design-oriented models. The data-driven neural network prediction models developed in this paper can provide a rapid prediction and design for FRP-confined composites.