作者: Zhe Feng Yu, Zhi Chun Yang
摘要: A new method for structural damage detection based on the Cross Correlation Function
Amplitude Vector (CorV) of the measured vibration responses is presented. Under a stationary
random excitation with a specific frequency spectrum, the CorV of the structure only depends on the
frequency response function matrix of the structure, so the normalized CorV has a specific shape.
Thus the damage can be detected and located with the correlativity and the relative difference between
CorVs of the intact and damaged structures. With the benchmark problem sponsored by ASCE Task
Group on Structural Health Monitoring, the CorV is proved an effective approach to detecting the
damage in structures subject to random excitations.
2317
作者: Wen Hui Tan, Miao Yu, Peng Fei Zhang
摘要: Blasting is a conventional technique for mining, but structures adjacent to open pit mines are often damaged by blasting vibration, therefore, durability and safety of these structures are affected. Damage characteristics of a three-floor frame structure on the top of an open pit mine under repeatedly blasting vibration is studied in this paper. The damage model of structural members is made based on the theory of damage mechanics of continuous medium, and an overall damage model of the structure is also established by the method of frequency. The results showed that the Young’s modulus, frequency of the structure decreased as increasing of the blasting times, the damage factors of structural members are bigger than that of the structure; the strength of the structure decreased greatly under repeatedly blasting vibration, the strength of the structure is low. The durability of structures is affected, but the structure will not collapse. The research is helpful for blasting control in open pit mines and design on structures adjacent to open pit mines.
3214
作者: Fei Zheng, Jin Yu Xu, Yong Chen, De Hui Zhao
摘要: Underground arch structure is an important structure form of civil air defense engineering, and its damage detection is an important link in safety evaluation of civil air defense engineering. In this study, a neural networks based damage detection method using the modal properties is presented. Based on the analysis of the dynamic properties of underground arch structure before and after damage, it was proved that the ratio of mode shape was insensitive to finite element model errors. The variation rate of mode shape was taken as the signature for damage detection, and it was proved that the variation of mode shape caused by multiple damage state was transformed into superposition of single damage state. It is not needed to constitute multiple location damage patterns when using the neural networks. A typical underground arch structure is analyzed to demonstrate the effectiveness of the proposed method. When 10%’s modeling errors exists, the damage location and extent can be recognized well.
2576
作者: Yan Song Diao, Fei Yu, Dong Mei Meng
摘要: When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.
1211
作者: Long Qiao, Asad Esmaeily
摘要: Deterioration of structures due to aging, cumulative crack growth or excessive response significantly affects the performance and safety of structures during their service life. Recently, signal-based methods have received many attentions for structural health monitoring and damage detection. These methods examine changes in the features derived directly from the measured time histories or their corresponding spectra through proper signal processing methods and algorithms to detect damage. Based on different signal processing techniques for feature extraction, these methods are classified into time-domain methods, frequency-domain methods, and time-frequency (or time-scale)-domain methods. As an enhancement for feature extraction, selection and classification, pattern recognition techniques are deeply integrated into signal-based damage detection. This paper provided an overview of these methods based on two aspects: (1) feature extraction and selection, and (2) pattern recognition. Signal-based methods are particularly more effective for structures with complicated nonlinear behavior and the incomplete, incoherent, and noise-contaminated measurements of structural response.
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