共查询到20条相似文献，搜索用时 359 毫秒
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基于瞬时最优算法的磁流变阻尼隔震结构半主动控制 总被引：1，自引：0，他引：1
采用瞬时最优控制算法，对附加了磁流变阻尼器的多自由度隔震结构进行了半主动控制的数值模拟。首先，将被动隔震装置——叠层钢板橡胶垫与磁流变阻尼器相结合，形成磁流变智能隔震系统。其次，根据瞬时最优控制算法的基本原理，针对磁流变阻尼器的特点，建立与之相适应的半主动控制算法。最后，以六层隔震结构为例，进行数值分析。比较了被动与半主动控制的结构反应，并得到较好的控制效果。 相似文献
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线性结构的滑动模态半主动控制 总被引：1，自引：0，他引：1
本文研究应用可变阻尼器对线性结构进行半主动控制的算法和原理。采用滑动模态控制算法,并基于Ｌｙａｐｕｎｏｖ直接法提出了半主动控制器设计。利用滑动模态控制方法和所建立的控制器,我们给出了一个风荷载激励下的线性结构的半主动控制算例。计算机模拟分析表明,半主动控制具有效果明显,鲁棒性好等优点,是一种非常有发展前途的控制方法。 相似文献
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高架桥地震反应半主动控制分析 总被引：5，自引：3，他引：5
本文探讨了高架桥结构地震反应LQR(Linear Quadratic Regulator)半主动控制算法以及考虑刚度退化的桥墩非线性计算模型，并利用Matlab语言编制的程序对其进行了数值仿真计算。结果表明，将隔震技术与利用MR阻尼器的半主动控制技术相结合，能够有效地减小高架桥的地震反应；MR阻尼器的设置位置以及结构的参数对控制效果有较大影响。考虑桥墩非线性影响将能得到更为接近实际的计算结果。 相似文献
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基于AFSMC算法的结构非线性振动MR控制与仿真分析 总被引：2，自引：0，他引：2
作为最近发展起来的高性能半主动控制装置,磁流变阻尼器通过改变磁场强度来调节控制力,可靠度高,体积小,出力大,并且具有Fail-Safe的特点,是一种具有广泛应用前景的新型结构控制装置。本文主要研究结构非线性振动的磁流变阻尼半主动控制。首先采用我们提出的自适应模糊滑模控制(AFSMC)算法得到了结构非线性振动的主动控制力,然后参照主动控制力,提出和仿真实现了结构非线性振动的磁流变阻尼半主动控制。最后,针对3层和20层benchm ark非线性模型,每层均设置一个磁流变阻尼器,对在给定的地震动下的结构响应进行了计算,分析了半主动控制跟踪主动控制的效果,并且对于半主动控制下的结构位移响应、加速度响应等各项指标也进行了对比分析。仿真结果表明,由于自适应模糊滑模控制算法与半主动控制算法相结合可以很好地实现结构非线性振动的半主动控制,所以能够得到令人满意的控制结果。 相似文献
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将二硫化钼作为摩擦材料设计出一种带限位器的滑移隔震支座。根据多层框架结构变形的特点,给出适用于滑移框架隔震结构的计算模型,推导出层间滑移隔震结构的运动方程。运用SAP2000有限元软件建立一层间滑移框架隔震结构的有限元模型,对比分析El Centro地震波下摩擦系数和隔震层位置不同时隔震结构的地震反应。结果表明,上部结构的动力反应随摩擦系数的增加而不断增大,滑移隔震结构的减震效果逐渐减弱,但隔震层的滑移量却在不断减小;摩擦系数的选取应综合考虑减震效果和隔震层滑移量两个因素。随着隔震层的增高,结构的加速度反应和层间位移反应整体上呈增大趋势,隔震效果不断减弱,且隔震层的加速度值下部层比上部层要大得多,一层隔震和三层隔震时的变形主要集中于隔震层,而五层隔震时结构层间位移并未出现突变,说明隔震层设置在较高位置处对结构体系的影响较小。 相似文献
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结构可变阻尼半主动控制 总被引：12，自引：4，他引：12
本文阐述了结构半主动控制的概念，并介绍了国内外有关结构半主动控制的研究状态，阐述了几种有关结构半主动控制的算法，包括基于经典最优控制的控制律及算法，基于变结构系统理论的滑动模太控制算法和非线性奇次系统的ｂａｎｇ－ｂａｎｇ控制算法。重点阐述了变结构系统理论和滑移面的确定及控制律的设计。 相似文献
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总结了6种半主动控制算法,采用黏滞阻尼器,对一座三跨简支梁桥进行了不同地震动输入下的半主动控制地震反应计算分析,比较分析了不同地震动输入和半主动控制算法对简支梁桥地震反应控制效果的影响。结果表明,半主动控制能有效地减小桥梁结构的大部分地震反应,同时可能会放大另外部分地震反应,这与地震动输入密切相关,不同地震动输入下的控制效果各不相同。所提六种半主动控制算法中,算法2、5、6对该简支梁桥地震反应的减震效果相对最好,这与各种算法的阻尼器耗能大小有关。 相似文献
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《地震工程与工程振动(英文版)》2021,20(3)
An innovative damage identification method using the nearest neighbor search method to assess 3 D structures is presented. The frequency response function was employed as the input parameters to detect the severity and place of damage in 3 D spaces since it includes the most dynamic characteristics of the structures. Two-dimensional principal component analysis was utilized to reduce the size of the frequency response function data. The nearest neighbor search method was employed to detect the severity and location of damage in different damage scenarios. The accuracy of the approach was verified using measured data from an experimental test; moreover, two asymmetric 3 D numerical examples were considered as the numerical study. The superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural network. Different levels of white Gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted data. The results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise, but the nearest neighbor search method is more robust against noisy data than the artificial neural network. 相似文献
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《水文科学杂志》2013,58(5):896-916
Abstract The performances of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are: the simple neural network (SNN), the radial basis function neural network (RBFNN) and the multi-layer perceptron neural network (MLPNN). Daily data of eight catchments, located in different parts of the world, and having different hydrological and climatic conditions, are used to enable comparisons of the performances of these three methods to be made. In the case of each catchment, each neural network combination method synchronously uses the simulated river flows of four rainfall—runoff models operating in design non-updating mode to produce the combined river flows. Two of these four models are black-box, the other two being conceptual models. The results of the study show that the performances of all three combination methods are, on average, better than that of the best individual rainfall—runoff model utilized in the combination, i.e. that the combination concept works. In terms of the Nash-Sutcliffe model efficiency index, the MLPNN combination method generally performs better than the other two combination methods tested. For most of the catchments, the differences in the efficiency index values of the SNN and the RBFNN combination methods are not significant but, on average, the SNN form performs marginally better than the more complex RBFNN alternative. Based on the results obtained for the three NN combination methods, the use of the multi-layer perceptron neural network (MLPNN) is recommended as the appropriate NN form for use in the context of combining simulated river flows. 相似文献
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A method of approximate magnetotelluric sounding (MTS) data inversion is developed on the basis of the representation of the inverse operator by an artificial neural network in classes of geoelectric structures. A methodology of the neural network inversion of magnetotelluric data is proposed for a family of classes of geoelectric structures and the uncertainty of the inferred results is estimated. A neural network algorithm of MTS data inversion is tested using synthetic 2-D data. 相似文献
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灰关联与人工神经网络在建筑物震害预测中的应用 总被引：2，自引：1，他引：1
基于灰关联识别方法,解析了各震害影响因子对多层砖房抗震性能的影响程度;并利用BP人工神经网络非线性模型对震害实例样本进行了训练。结果表明:利用灰关联分析,可得出各因子对多层砖房抗震性能影响程度的大小排序,有利于实际的工程抗震设计;基于BP人工神经网络模型的多层砖房的震害预测结果与震害实例的实际情况比较吻合,其思路和方法可推广于其他不同类型的建筑结构的震害预测。 相似文献
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The use of artificial neural networks in the general framework of a performance-based seismic vulnerability evaluation for earth retaining structures is presented. A blockwork wharf-foundation-backfill complex is modeled with advanced nonlinear 2D finite difference software, wherein liquefaction occurrence is explicitly accounted for. A simulation algorithm is adopted to sample geotechnical input parameters according to their statistical distribution, and extensive time histories analyses are then performed for several earthquake intensity levels. In the process, the seismic input is also considered as a random variable. A large dataset of virtual realizations of the behavior of different configurations under recorded ground motions is thus obtained, and an artificial neural network is implemented in order to find the unknown nonlinear relationships between seismic and geotechnical input data versus the expected performance of the facility. After this process, fragility curves are systematically derived by applying Monte Carlo simulation on the obtained correlations. The novel fragility functions herein proposed for blockwork wharves take into account different geometries, liquefaction occurrence and type of failure mechanism. Results confirm that the detrimental effects of liquefaction increase the probability of failure at all damage states. Moreover, it is also demonstrated that increasing the base width/height ratio results in higher failure probabilities for the horizontal sliding than for the tilting towards the sea. 相似文献
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This paper investigates the application of the sliding mode control (SMC) strategies for reducing the dynamic responses of the building structures with base‐isolation hybrid protective system. It focuses on the use of reaching law method, a most attractive controller design approach of the SMC theory, for the development of control algorithms. By using the constant plus proportional rate reaching law and the power rate reaching law, two kinds of hybrid control methods are presented. The compound equation of motion of the base‐isolation hybrid building structures, which is suitable for numerical analysis, has been constructed. The simulation results are obtained for an eight‐storey shear building equipped with base‐isolation hybrid protective system under seismic excitations. It is observed that both the constant plus proportional rate reaching law and the power rate reaching law hybrid control method presented in this paper are quite effective. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
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基于灰关联与人工神经网络综合评价模型的多层砖房震害预测 总被引：1，自引：0，他引：1
结合几次大地震中多层砖房的实际震害资料,基于灰关联识别方法,解析了各影响因子对多层砖房抗震性能的影响程度。以反映结构抗震性能的各类物理参数作为输入数据,以给定地震动峰值加速度下建筑物破坏状态的概率作为输出数据,采用8-6-5层结构,建立了基于BP人工神经网络的非线性模型,并对震害样本进行了训练。结果表明:利用灰关联分析,可得出各因子对多层砖房抗震性能影响程度的大小排序,有利于实际的工程抗震设计;基于BP人工神经网络模型的多层砖房的震害预测结果与震害实例的实际情况比较吻合,其思路和方法可推广于其他不同类型的建筑结构的震害预测。 相似文献
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Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks 总被引：1，自引：0，他引：1
Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion. 相似文献
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人工神经网络及其在地震预报中的应用 总被引：7，自引：3，他引：7
文概述了人工神经网络的原理和算法，利用１９８５－１９９２年全国年度趋势会商报告的资料来训练的检验神经网络。结果表明，网络经训练后具有较高的识别能力，在地震预报中有深入研究和进一步应用的价值。最后讨论了神经网络中几个重要参数的取值问题。 相似文献