Carbon fiber reinforced polymer (CFRP) is broadly used in the defense and aerospace industries due to its su-perior performance, and the real-time monitoring of its structural damage can enhance the reliability and safety of utilization. This paper is aimed at collecting the surface strain of CFRP laminates in real-time using FBG sensor network and constructing a structural damage identification model based on strain characteristics to identify the location of hole damage and the length and direction of crack damage. To investigate the impact of damage on the surface strain of CFRP laminates, this research conducted an extensive finite element analysis of laminates without damage and with various degrees of damage, combined with the genetic algorithm and linear regression to identify and optimize the parameters in the damage identification model. Then two CFRP laminates with comparable properties were fabricated, and the FBG sensor network was arranged on the surface to verify the efficiency of the damage identification model through experiments. Results of experiments show that the damage identification model proposed in this paper can accomplish accurate identification of hole damage location and crack damage length and direction within the error range, also proving the feasibility and effectiveness of combining finite element analysis, FBG sensor network, and intelligent algorithm to establish CFRP structural damage identification system proposed in this paper.
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