Data-driven ultimate conditions prediction and stress-strain model for FRP-confined concrete. (15th June 2020)

• Record Type: Journal Article
• Authors: Jiang, Kejie; Han, Qiang; Bai, Yulei; Du, Xiuli
• Is Part Of: Composite structures. Volume 242(2020)
• Publication Date: 2020-06-15
• Subjects: FRP-confined concrete -- Ultimate conditions -- Stress–strain model -- ANN -- Data-driven prediction -- Design; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2020.112094
• Languages: English
• ISSNs: 0263-8223

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.