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Radial basis function neural network algorithm for semi-active control of base-isolated structures

机译:径向基函数神经网络的基础隔震结构半主动控制算法

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摘要

Curved surface slider (CSS) is considered as an effective isolation device for structures subjected to earthquake ground motions. Due to constant frequency, CSS may encounter a resonance problem when subjected to near-fault earthquake ground motions. To overcome this problem, we propose CSS combined with a control device in this study. The control device consists of variable orifice fluid damper, and its damping coefficient is controlled by a radial basis function-based neural network algorithm. Numerical simulations are performed to evaluate the effectiveness of the proposed technique for only one-directional horizontal seismic excitations without any evaluation concerning the durability of CSSs. The results of the investigation demonstrate that the proposed technique is effective to reduce both the base shear and the sliding displacement of the isolated structure. In addition, the response predicted by the proposed technique is almost similar to the response of isolated structure with passive damper at optimum damping ratio.
机译:曲面滑块(CSS)被认为是经受地震地震动的结构的有效隔离装置。由于频率恒定,CSS在遭受近断层地震地震动时可能会遇到共振问题。为了克服这个问题,我们在这项研究中提出了将CSS与控制设备结合使用的方法。该控制装置由可变孔流体阻尼器组成,其阻尼系数由基于径向基函数的神经网络算法控制。进行了数值模拟,以评估所提出的技术仅对单向水平地震激励的有效性,而没有关于CSS耐久性的任何评估。研究结果表明,所提出的技术可有效减少基础剪力和隔震结构的滑动位移。此外,所提出的技术所预测的响应几乎与具有最佳阻尼比的无源阻尼器的隔震结构的响应相似。

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