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ANN based methodology for active control of buildings for seismic excitation for different seismic zones of India

机译:基于ANN基于印度不同地震区地震激励建筑物的积极控制方法

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In the last two decades, many studies are reported in literature for determining the control force for the active control systems for damage mitigation of buildings due to earthquake. However, no study has been reported for prediction of control force considering the seismic zones in India. In the present study, spectrum compatible time histories are generated for the seismic zones IV and V as per Indian standard IS 1893(Part 1):2002 design spectrum. Time history analysis are carried out with spectrum compatible time histories for shear type buildings modelled as multi-degree of freedom system (MDOF) with a computer program developed in MATLAB by modal superposition using Newmark-beta method. Control forces are obtained by adopting algorithm proposed in literature and input and output patterns are generated for development of Artificial Neural Network (ANN) models in Stuttgart Neural Network Simulator (SNNS). In the present study the methodology is demonstrated with five storey building by developing 24 ANN models consisting of two ANN architectures viz., NET1 and NET2 for each seismic zone and three soil types. From the validation of results from ANN models it is observed that the maximum difference in percentage response reduction of peak displacement is less than 10% when it is compared with the target value of percentage response reduction.
机译:在过去的二十年中,在文献中报告了许多研究,用于确定由于地震因地震而损坏建筑物损伤的主动控制系统的控制力。然而,据报道,考虑印度地震区的控制力预测,据报道没有研究。在本研究中,根据印度标准,为地震区IV和V产生频谱相容时间历史是1893(第1部分):2002设计谱。时间历史分析是用频谱兼容的时间历史,用于使用Newmark-Beta方法在Matlab中开发的计算机程序建模的剪切型建筑物的剪切型建筑物的时间历史。通过采用文献中提出的算法获得控制力,并在斯图加特神经网络模拟器(SNNS)中产生用于开发人工神经网络(ANN)模型的输入和输出模式。在本研究中,通过开发由两个ANN架构VIZ组成的24个ANN模型,用五层建筑证明了方法。,每个地震区的NET1和NET2和三种土壤类型。根据ANN模型的结果验证,观察到,当与减少百分比的目标值进行比较时,峰位响应率的响应率的最大差异小于10%。

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