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基于AE和马尔可夫模型的CFRP加固钢管损伤性能研究

Research on the damage performance of CFRP-reinforced steel pipes based on ae and markov model

  • 摘要: 为研究碳纤维增强复合材料(Carbon Fiber Reinforced Polymer,CFRP)加固Q235钢管弯曲损伤性能及破坏机制,结合声发射(AE)技术、荷载延性系数与马尔可夫链模型开展损伤性能分析。依据声发射信号幅值、振铃计数等参数分析了“弹性变形-基体开裂-钢管屈服”三阶段损伤规律特征;构建以AE参数为空间的马尔可夫链模型,统计不同荷载下的状态转移次数,计算得到了各试件的转移概率矩阵。结果表明,改变CFRP工艺参数使钢管的极限承载力提高了3.75%~20.5%,应变提高了10.7%~18.9%。通过对比分析不同加固方式下试件的声发射参数信号,验证了该技术在揭示碳纤维复合材料钢管弯曲损伤演化规律中的有效性。马尔可夫链模型预测ST、0°C2S、0°C4S、30°C2S、60°C2S和90°C2S试件第一阶段概率由0.5996降至0.2839,第二阶段的概率由0.7712降至0.3887,第三阶段的概率由0.8557降至0.44129,说明该模型可有效表征钢管损伤演化的无后效性。声发射信号特征与马尔可夫链状态转移能有效检测CFRP复合钢管损伤变化规律

     

    Abstract: To investigate the bending damage behavior and failure mechanism of Q235 steel pipes strengthened with carbon fiber-reinforced polymer (CFRP), a damage behavior analysis was conducted by integrating acoustic emission (AE) technology, the load–ductility coefficient, and a Markov chain model. Based on AE parameters—including signal amplitude and ring-down count—the damage characteristics corresponding to the three-stage damage evolution law (“elastic deformation → matrix cracking → steel pipe yielding”) were analyzed. A Markov chain model was constructed using AE parameters as the state space; the number of state transitions under varying loads was counted, and transition probability matrices were calculated for each specimen. Results show that optimizing CFRP strengthening process parameters increased the ultimate bearing capacity of the steel pipes by 3.75%–20.5% and the ultimate strain by 10.7%–18.9%. By comparing and analyzing the AE parameter signals from specimens reinforced with different CFRP configurations, the effectiveness of AE technology in revealing damage patterns during the bending of CFRP-strengthened steel pipes was verified. The Markov chain model predicts that, across specimens ST, 0°C2S, 0°C4S, 30°C2S, 60°C2S, and 90°C2S, the steady-state probabilities of the first stage decrease from 0.5996 to 0.2839, those of the second stage decrease from 0.7712 to 0.3887, and those of the third stage decrease from 0.8557 to 0.4129. This indicates that the model effectively captures the non-sequential (i.e., non-monotonic or multi-path) nature of damage evolution in steel pipes. Combining AE signal characteristics with Markov chain state transitions enables effective detection of damage evolution in CFRP-strengthened steel pipes.

     

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