纤维增强树脂基复合材料(Fiber reinforced plastics,FRPs)因其高模高强、可设计强、耐腐蚀等优异性能,已广泛应用于航空航天等高性能领域。结构健康监测(Structural health mornitoring,SHM)对于保障复合材料结构的安全运行至关重要。...纤维增强树脂基复合材料(Fiber reinforced plastics,FRPs)因其高模高强、可设计强、耐腐蚀等优异性能,已广泛应用于航空航天等高性能领域。结构健康监测(Structural health mornitoring,SHM)对于保障复合材料结构的安全运行至关重要。随着人工智能技术的进步,机器学习方法在复合材料结构健康监测领域也得到快速发展,以数据驱动方法代替传统模型对结构状态进行判断,使得基于各类传感器的结构健康监测技术具有更高的准确性、鲁棒性及高效性。基于此,本文首先阐述了在复合材料结构健康监测领域中常用的机器学习算法,其次总结了机器学习方法在复合材料损伤模式识别、损伤位置识别和损伤程度识别几个方面的研究进展,最后讨论了基于机器学习的复合材料结构健康监测的未来发展趋势。展开更多
Based on the finite element （FE） program ANSYS, a three-dimensional model for the Runyang Suspension Bridge （RSB） is established. The structural natural frequency, vibration mode, stress and displacement response ...Based on the finite element （FE） program ANSYS, a three-dimensional model for the Runyang Suspension Bridge （RSB） is established. The structural natural frequency, vibration mode, stress and displacement response under various load cases are given. A new method of FE model updating is presented based on the physical meaning of sensitivity and the penalty function concept. In this method, the structural model is updated by modifying the parameters of design, and validated by structural natural vibration characteristics, stress response as well as displacement response. The design parameters used for updating are bounded according to measured static response and engineering judgment. The FE model of RSB is updated and validated by the measurements coming from the structural health monitoring system （SHMS）, and the FE baseline model reflecting the current state of RSB is achieved. Both the dynamic and static results show that the method is effective in updating the FE model of long span suspension bridges. The results obtained provide an important research basis for damage alarming and health monitoring of the RSB.展开更多
Carbon fiber-reinforced polymer(CFRP)is widely used in aerospace applications.This kind of material may face the threat of high-velocity impact in the process of dedicated service,and the relevant research mainly cons...Carbon fiber-reinforced polymer(CFRP)is widely used in aerospace applications.This kind of material may face the threat of high-velocity impact in the process of dedicated service,and the relevant research mainly considers the impact resistance of the material,and lacks the high-velocity impact damage monitoring research of CFRP.To solve this problem,a real high-velocity impact damage experiment and structural health monitoring(SHM)method of CFRP plate based on piezoelectric guided wave is proposed.The results show that CFRP has obvious perforation damage and fiber breakage when high-velocity impact occurs.It is also proved that guided wave SHM technology can be effectively used in the monitoring of such damage,and the damage can be reflected by quantifying the signal changes and damage index(DI).It provides a reference for further research on guided wave structure monitoring of high/hyper-velocity impact damage of CFRP.展开更多
针对金属表面裂纹的多特征监测问题,提出了一种基于射频识别(Radio Frequency Identification, RFID)技术的裂纹多特征监测方法。首先,设计了一种RFID标签阵列,通过裂纹影响标签反射信号的相位特征来感知裂纹特征。然后,设计了一种基于...针对金属表面裂纹的多特征监测问题,提出了一种基于射频识别(Radio Frequency Identification, RFID)技术的裂纹多特征监测方法。首先,设计了一种RFID标签阵列,通过裂纹影响标签反射信号的相位特征来感知裂纹特征。然后,设计了一种基于几何法的裂纹粗定位算法,利用粗定位结果结合指纹法进行细粒度定位,计算得到裂纹的方向、位置和宽度。最后,通过在不同标签阵列密度以及指纹密度的情况下进行仿真,并进行真实实验来验证该算法的有效性和准确性。仿真及实验结果表明,与传统RFID指纹法相比,所提方法能有效降低裂纹多特征监测误差。展开更多
This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemb...This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.展开更多
文摘纤维增强树脂基复合材料(Fiber reinforced plastics,FRPs)因其高模高强、可设计强、耐腐蚀等优异性能,已广泛应用于航空航天等高性能领域。结构健康监测(Structural health mornitoring,SHM)对于保障复合材料结构的安全运行至关重要。随着人工智能技术的进步,机器学习方法在复合材料结构健康监测领域也得到快速发展,以数据驱动方法代替传统模型对结构状态进行判断,使得基于各类传感器的结构健康监测技术具有更高的准确性、鲁棒性及高效性。基于此,本文首先阐述了在复合材料结构健康监测领域中常用的机器学习算法,其次总结了机器学习方法在复合材料损伤模式识别、损伤位置识别和损伤程度识别几个方面的研究进展,最后讨论了基于机器学习的复合材料结构健康监测的未来发展趋势。
文摘Based on the finite element （FE） program ANSYS, a three-dimensional model for the Runyang Suspension Bridge （RSB） is established. The structural natural frequency, vibration mode, stress and displacement response under various load cases are given. A new method of FE model updating is presented based on the physical meaning of sensitivity and the penalty function concept. In this method, the structural model is updated by modifying the parameters of design, and validated by structural natural vibration characteristics, stress response as well as displacement response. The design parameters used for updating are bounded according to measured static response and engineering judgment. The FE model of RSB is updated and validated by the measurements coming from the structural health monitoring system （SHMS）, and the FE baseline model reflecting the current state of RSB is achieved. Both the dynamic and static results show that the method is effective in updating the FE model of long span suspension bridges. The results obtained provide an important research basis for damage alarming and health monitoring of the RSB.
基金supported by the National Natural Science Foundation of China(Nos.51921003,52275153)the Fundamental Research Funds for the Central Universities(No.NI2023001)+2 种基金the Research Fund of State Key Laboratory of Mechanics and Control for Aero-space Structures(No.MCAS-I-0423G01)the Fund of Pro-spective Layout of Scientific Research for Nanjing University of Aeronautics and Astronauticsthe Priority Academic Program Development of Jiangsu Higher Education Institu-tions of China.
文摘Carbon fiber-reinforced polymer(CFRP)is widely used in aerospace applications.This kind of material may face the threat of high-velocity impact in the process of dedicated service,and the relevant research mainly considers the impact resistance of the material,and lacks the high-velocity impact damage monitoring research of CFRP.To solve this problem,a real high-velocity impact damage experiment and structural health monitoring(SHM)method of CFRP plate based on piezoelectric guided wave is proposed.The results show that CFRP has obvious perforation damage and fiber breakage when high-velocity impact occurs.It is also proved that guided wave SHM technology can be effectively used in the monitoring of such damage,and the damage can be reflected by quantifying the signal changes and damage index(DI).It provides a reference for further research on guided wave structure monitoring of high/hyper-velocity impact damage of CFRP.
文摘针对金属表面裂纹的多特征监测问题,提出了一种基于射频识别(Radio Frequency Identification, RFID)技术的裂纹多特征监测方法。首先,设计了一种RFID标签阵列,通过裂纹影响标签反射信号的相位特征来感知裂纹特征。然后,设计了一种基于几何法的裂纹粗定位算法,利用粗定位结果结合指纹法进行细粒度定位,计算得到裂纹的方向、位置和宽度。最后,通过在不同标签阵列密度以及指纹密度的情况下进行仿真,并进行真实实验来验证该算法的有效性和准确性。仿真及实验结果表明,与传统RFID指纹法相比,所提方法能有效降低裂纹多特征监测误差。
文摘This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.
