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… Click to show full abstract
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.
               
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