In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.

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Utilising optimal general regression neural network (GRNN) inverse model for modelling and control of magnetorheological elastomer base isolation system

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Utilizing the unique feature of MRE materials for vibration isolators has been intensively studied over the last several years. Real-time control of the MRE isolators holds the key to unlock MRE material's unique characteristics, i.e. instantly changeable shear modulus in continuous and reverse fashion. However, one of the critical issues for the applications of real-time control is the response time delay of MRE vibration isolators, which has not yet been fully addressed and studied. This paper identified the inherent response time of the MRE isolator and explored two feasible approaches to minimize the response time delay.Experiments were designed and conducted to evaluate the effectiveness of the proposed approaches on minimizing time delay on: i) the transient response of current of a large coil that generates magnetic field and ii) the transient response of shear force from the MRE isolator. The results show that the proposed approaches are effective and promising. For example, the proposed approach is able to reduce the force response time from 421ms to 52ms at rising and from 402 ms to 48ms falling edges, respectively. Such level of short response time of the MRE isolators demonstrates the feasibility of application of real-time control and hence is the essential step on the realization of real-time control of vibration suppression system based on MRE isolator.