Reinforcement response prediction of composite‐concrete beams with crack patterns and deep learning

Using fiber-reinforced polymer reinforcement in concrete (FRP-concrete) to form composite-concrete structures is increasingly important in civil and infrastructure applications because of its high strength, durability, and corrosion resistance. However, one of the main challenges of the FRP-concrete structure is the brittle failure mode, which might result in sudden structural collapse. It is critical to closely monitor the strain of composite reinforcement for potential structural damage. This study develops a deep learning-based framework for the reinforcement condition identification in terms of non-contact strain using the crack image data from FRP-concrete beam system. The framework contained two stages: first, using crack pattern and strain data obtained from simulation to train the Kolmogorov–Arnold networks (KANs)-based convolutional neural network strain prediction model, which added a new KAN channel in convolution to enhance performance. Subsequently, the actual crack patterns from you only look once v11 segmentation results are used to generate the input of the prediction model. A three-bending test of an FRP-concrete beam is presented to validate this method. The developed framework achieves an average R2 of 0.713, compared to the actual sensor data in reinforcement strain prediction. The results indicate that the intelligent framework has superior performance in strain prediction, addressing challenges in FRP-concrete structure applications.