Xiaoyu Gu

Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator.

A hysteresis model for dynamic behaviour of magnetorheological elastomer base isolator

In recent years, an adaptively tuned magnetorheological elastomer (MRE) isolator for a base isolation system has been designed and tested with the benefits of low power cost, fail safe manner and fast responses. To make full use of this striking device for design of smart structures, a highly precise model should be developed to effectively and accurately forecast the shear force of the device in real-time so as to adopt a proper control strategy to improve the responses of the protected structures. In this work, a novel mechanical model is presented to characterize this nonlinear hysteresis for its implementation in structural vibration control. This model employs the displacement and velocity of the device as well as the applied current as the inputs and just has the limited constant parameters to be identified compared with some classical hysteretic models such as Bouc–Wen, improved Dahl and LuGre models. Performance evaluation of this novel hysteresis model has been conducted based on the testing data from an MRE base isolator. The results show that the proposed model has high modelling accuracy and is able to perfectly portray the unique and complicated behaviours of the device with various excitations.

Frequency control of smart base isolation system employing a novel adaptive magneto-rheological elastomer base isolator

In the past decades, base isolation techniques have become increasingly popular for seismic protection of civil structures owing to its capability of decoupling buildings from harmful ground motion. However, it has been recognised recently that the traditional passive base isolation technique could encounter a serious problem during earthquakes due its incapability in adjusting the isolation frequency to cope with the unpredictability and diversity of earthquakes. To address this challenge, a great deal of research efforts have been conducted to improve traditional base isolation systems, most of which focused on hybrid supplementary devices (passive, active and semi-active types) for the isolators to control displacement or to dissipate seismic energy. On the other hand, the most effective approach to address the aforementioned challenge should lay on varying isolator stiffness in real-time to achieve real-time spontaneous decoupling. A recent advance of the development of an adaptive magneto-rheological elastomer base isolator has brought such idea to reality as the new magneto-rheological elastomer base isolator is capable to alter its stiffness significantly in real-time. In this article, an innovative smart base isolation system employing such magneto-rheological elastomer isolator is proposed and a novel frequency control algorithm is developed to shift the fundamental frequency of the structure away from the dominant frequency range of earthquakes. Such design enables the building to avoid resonant state in real-time according to the on-coming spectrum of the earthquakes. Extensive simulation has been conducted using a five-storey benchmark model with the isolation system, and testing results indicate that the proposed control system is able to significantly suppress both the floor accelerations and inter-storey drifts simultaneously under different earthquakes.

Experimental forward and inverse modelling of magnetorheological dampers using an optimal Takagi–Sugeno–Kang fuzzy scheme

An evolving encoding scheme is presented in this article for a fuzzy-based nonlinear system identification scheme, using the subtractive fuzzy C-mean clustering and a modified version of non-dominated sorting genetic algorithm. This method is able to automatically select the best inputs as well as the structure of the fuzzy model such as rules and membership functions. Moreover, three objective functions are considered to satisfy both accuracy and compactness of the model. The developed method is then employed to identify both forward and inverse models of a highly nonlinear structural control device, that is, magnetorheological damper. Experimental results showed that the proposed evolving Takagi–Sugeno–Kang fuzzy model can identify and grasp the nonlinear behaviour of magnetorheological damper very well with minimal number of inputs and fuzzy rules.

Nonlinear Characterization of the MRE Isolator using Binary-Coded Discrete CSO and ELM

Magnetorheological elastomer (MRE) isolator has been proved as a promising semi-active control device for structural vibration control. For its engineering application, developing an accurate and r...