高耸塔架结构节点损伤基于神经网络的两步诊断法   总被引：15，自引：1，他引：15  

瞿伟廉  黄东梅 《地震工程与工程振动》2003,23(2):143-149

本文针对高耸钢塔架结构的损伤特点，建立了具有节点损伤的有限元模型，提出了一种分层神经网络两步诊断法：第一步，由基于区域残余力理论的第一层神经网络进行结构损伤区域的初诊；第二步，由基于应变模态理论的第二层神经网络进行损伤区域内的具体损伤节点位置和程度的诊断。对一平面塔架结构的数值仿真分析表明：本文提出的损伤诊断方法的结果是令人满意的。  相似文献   

郑州国际会展中心展厅钢屋盖的动力特性分析   总被引：7，自引：1，他引：7  

任俊超  童丽萍  赵轶西 《世界地震工程》2004,20(1):70-74

郑州国际会展中心展览厅钢屋盖采用了空间斜拉式索拱结构体系，为探讨该体系的动力特性，采用ANSYS有限元程序，分别建立钢屋盖与下部混凝土结构共同作用、钢屋盖单独作用以及无斜拉系统的钢屋盖单独作用时的3个空间计算模型，采用子空间迭代法计算其自振周期和振型，并加以比较。从而得出钢屋盖的动力特性，为进一步研究此类结构的受力特性奠定了基础。  相似文献   

城区地表形变差分TomoSAR监测方法     

王爱春  向茂生  汪丙南 《测绘学报》2016,45(12):1413-1422

压缩感知技术(CS)的差分TomoSAR技术解决了中高分辨率SAR数据在城区出现的叠掩问题,实现了城区地表形变信息的重构,但是该方法仅利用了目标的稀疏特性并没有考虑目标的结构特性,对具有这两种特性的目标进行重构时其性能较差。针对这一问题,本文采用联合Khatri-Rao子空间和块压缩感知(KRS-BCS),提出了一种差分SAR层析成像方法。该方法依据目标的结构特性和重构观测矩阵具有的Khatri-Rao积性质,将稀疏结构目标的差分TomoSAR问题转化为Khatri-Rao子空间下的BCS问题,然后对目标进行块稀疏的l1/l2范数最优化求解,最后通过理论分析和仿真试验对分辨能力和重构估计性能进行了定性和定量评价,仿真结果表明本文所采用的KRS-BCS方法不仅保持了高分辨率的优点,而且有效地降低了虚假目标出现的概率,大幅度提高了散射点准确重构概率,切实可行地解决了CS方法的不足。应用实例研究中,利用34景Envisat卫星ASAR时间序列影像对日本千叶县茂原市城区进行地表形变监测,并以一等水准点和实时测量的GPS站点观测数据作为参考形变结果进行验证,试验结果表明采用KRS-BCS方法反演的结果与参考形变结果保持了良好的一致性且形变速率整体偏差也较小,实现了较高精度的城区地表形变估计。  相似文献   

Generalized Subspace Methods For Large-Scale Inverse Problems   总被引：4，自引：0，他引：4  

D. W. Oldenburg  P. R. Mcgillivray  R. G. Ellis 《Geophysical Journal International》1993,114(1):12-20

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Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method     

Viet-Ha Nhu  Khabat Khosravi  James R. Cooper  Mahshid Karimi  Ozgur Kisi  Binh Thai Pham 《水文科学杂志》2020,65(12):2116-2127

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.  相似文献   

Prediction error method‐based second‐order structural identification algorithm in stochastic state space formulation     

Jian Li  Yunfeng Zhang 《地震工程与结构动力学》2006,35(6):761-779

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Performance of Jacobi preconditioning in Krylov subspace solution of finite element equations     

F.‐H. Lee  K.K. Phoon  K.C. Lim  S.H. Chan 《国际地质力学数值与分析法杂志》2002,26(4):341-372

This paper examines the performance of the Jacobi preconditioner when used with two Krylov subspace iterative methods. The number of iterations needed for convergence was shown to be different for drained, undrained and consolidation problems, even for similar condition number. The differences were due to differences in the eigenvalue distribution, which cannot be completely described by the condition number alone. For drained problems involving large stiffness ratios between different material zones, ill‐conditioning is caused by these large stiffness ratios. Since Jacobi preconditioning operates on degrees‐of‐freedom, it effectively homogenizes the different spatial sub‐domains. The undrained problem, modelled as a nearly incompressible problem, is much more resistant to Jacobi preconditioning, because its ill‐conditioning arises from the large stiffness ratios between volumetric and distortional deformational modes, many of which involve the similar spatial domains or sub‐domains. The consolidation problem has two sets of degrees‐of‐freedom, namely displacement and pore pressure. Some of the eigenvalues are displacement dominated whereas others are excess pore pressure dominated. Jacobi preconditioning compresses the displacement‐dominated eigenvalues in a similar manner as the drained problem, but pore‐pressure‐dominated eigenvalues are often over‐scaled. Convergence can be accelerated if this over‐scaling is recognized and corrected for. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

Discussion of user-defined parameters for recursive subspace identification: Application to seismic response of building structures     

Shieh-Kung Huang  Jun-Da Chen  Kenneth J. Loh  Chin-Hsiung Loh 《地震工程与结构动力学》2020,49(15):1738-1757

Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, an appropriate data analysis and system identification technique is required to interpret the measured data and to identify the state of the structure. Generally, the recursive system identification algorithm is used. In this study, the recursive subspace identification (RSI) algorithm based on the matrix inversion lemma algorithm with oblique projection technique (RSI-Inversion-Oblique) is applied to investigate the time-varying dynamic characteristics. The user-defined parameters used in the RSI-Inversion-Oblique technique are carefully discussed, which include the size of the data Hankel matrix (i), model order to extract the physical modes, and forgetting factor (FF) to detect the time-varying system modal frequencies. Response data from the Northridge earthquake from the Sherman Oaks building (CSMIP) is used as an example to examine a systematic method to determine the suitable user-defined parameters in RSI. It is concluded that the number of rows in the data Hankel matrix significantly influences the identification of the time-varying fundamental modal frequency of the structure. An algorithmic model order selection method using the eigenvalue distribution of RSI-Inversion can detect the system modal frequencies at each appending data window without causing any abnormality.  相似文献   

Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform   总被引：1，自引：1，他引：0  

Han Jianping  Zheng Peijuan  Wang Hongtao 《地震工程与工程振动(英文版)》2014,13(1):101-111

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.  相似文献   

改进曲率模态识别桥梁损伤位置方法研究     

常军  任永辉  陈忠汉 《地震工程与工程振动》2010,30(4)

结构健康监测和结构状态评估的主要前提之一是结构损伤识别。基于曲率模态对结构局部损伤比较敏感和频率指标测试简单方便、精度高的特点,本文提出了一种以结构的曲率模态为基础,综合考虑频率的变化的改进的结构损伤识别方法。随机子空间方法是一种行之有效的基于环境激励的结构状态识别方法。该方法的主要优点是无需人工激励,不中断桥梁的运营。为此,论文提出了一种不中断桥梁运营的基于改进曲率模态的桥梁结构损伤识别方法。最后用一三跨连续梁的有限元模型对该改进方法进行了验证。结果表明,采用随机子空间结合改进的曲率模态方法可以在不中断桥梁运营的前提下有效地识别出桥梁的损伤状况。  相似文献