基于灵敏度分析的结构损伤识别中的传感器优化配置

本文提出了结构损伤识别中的传感器测点优化配置的方法。该方法是通过仅考虑结构刚度变化的结构特征灵敏度分析，以结构各自由度的损伤信息为条件，计算出结构的Fisher信息阵，并且考虑到Fisher信息阵的逆阵可能不存在，而将Fisher信息阵对应于每个自由度进行分解，通过计算每个分解的Fisher信息阵的迹而确定每个自由度含有的损伤信息的多少，从而从结构的全部自由度中去掉那些含损伤信息少的自由度。建立直接利用结构不完整的实测模态来定位结构的损伤，避免结构模态扩阶带来不必要的误差。最后，通过数值算例表明，该方法能有效地识别出结构的损伤。     

基于连续小波变换的时变结构瞬时模态参数识别

为体现时变结构动力特性,定义随机冲击荷载作为时变结构输入激励,提出了基于连续小波变换的时变结构瞬时模态参数识别方法。在短时时变假定条件下,建立基于模局部极大值的连续小波变换时变参数识别原理,利用结构的输出响应进行瞬时模态参数识别,采用三自由度的时变结构体系进行数值模拟,该方法能够准确识别时变结构的瞬时模态参数值。通过设计具有质量参数可变的两层钢框架模型进行测试,验证了方法的有效性与可行性。     

基于Hilbert-Huang变换的建筑物强震记录分析方法研究

建筑物在强震中可能受到损伤,通过对结构瞬时频率的分析可以诊断出结构的损伤发展过程。本文探讨了基于H ilbert-Huang变换的结构物损伤诊断方法,研究了如何从结构地震响应信号中提取模态响应、1阶模态振型和损伤发展规律。本文采用HHT法分析了Northridge地震中某超高层建筑物的强震记录,分析结果表明:带有间歇检验准则的经验模态分解法能够提取结构的模态振动响应;通过分析不同楼层的相对H ilbert边际谱能够识别出结构的1阶模态振型;分析结构振动中瞬时频率的时变特点,可以直观地掌握振动中结构的损伤发展规律。     

基于LMD的非线性结构系统识别

提出了一个基于局部均值分解(local mean decomposition,LMD)识别的非线性多自由度结构系统的新方法。首先运用LMD将非线性结构的自由响应信号自适应地分解为有限个乘积函数之和,然后由每个乘积函数分量的包络函数和纯调频函数求取相应的瞬时幅值和瞬时频率,根据自由响应信号瞬时特征间的关系,进而得到非线性结构的骨架曲线。算例仿真结果验证了该方法的有效性和可行性。     

基于Hilbert-Huang变换和随机子空间识别的模态参数识别

基于Hilbert-Huang变换和随机子空间识别技术提出了两种土木工程结构的模态参数识别方法。方法一是基于Hilbert-Huang变换和自然激励技术,通过经验模态分解和Hilbert变换提取信号的瞬时特性,进而利用自然激励技术和模态分析的基本理论识别结构的模态参数;方法二是基于经验模态分解和随机子空间识别技术,通过经验模态分解对信号进行预处理,进而运用随机子空间识别方法处理得到的结构单阶模态响应以识别结构的模态参数。利用这两种方法,通过对一12层钢筋混凝土框架模型振动台试验测点加速度记录的处理,识别了该模型结构的模态参数。识别结果与传统的基于傅里叶变换的识别结果及有限元分析结果的对比验证了这两种方法的可行性和实用性。     

基于Hilbert-Huang变换和随机减量技术的模态参数识别

傅里叶分析的信号处理方法对非线性、非平稳信号的处理能力差,传统的模态参数识别方法也存在阻尼比识别精度不高的问题。基于Hilbert-Huang变换和随机减量技术提出了一种新的、实用的模态参数识别方法,首先对结构振动信号进行滤波处理和经验模态分解,得到若干阶本征模态响应,然后利用随机减量技术提取自由衰减响应,进而由Hilbert-Huang变换得到信号的瞬时特性,最后结合模态识别的基本理论识别结构的模态频率和模态阻尼比。为了验证这一方法的有效性,对12层钢筋混凝土框架模型振动台试验一测点的加速度记录进行了处理,识别了模态参数,识别结果与其它识别方法及有限元分析结果的对比表明该方法识别模态频率是可靠的,而模态阻尼比识别的精准性仍然难以确认。     

Hilbert-Huang变换在提取阵列声波信号动力特性中的应用

阵列声波信号是典型的非线性、非平稳信号,其动力特性的量化提取对于进行地层结构构造分析提供了必要的基础资料.而Hilbert-Huang变换(HHT)是一种处理非线性、非平稳信号的新方法.它通过经验模态分解(EMD)将信号分解为有限个固有模态函数(IMF),并对每个固有模态函数进行Hilbert变换得到Hilbert谱.本文将这种方法应用于阵列声波信号动力特性的提取,有效地获得了信号能量的时频分布,瞬时能量、Hilbert能量、最大振幅对应的时频分布等动力特性,显示了HHT的优势以及对于进一步实现地层结构构造分析的重要意义.     

ARX结构模态参数识别方法对比(Ⅱ)——基于地震观测记录的对比

结构参数识别是结构抗震安全性能鉴定和健康诊断的基础,利用地震观测记录来识别结构模态参数,是地震工程领域备受关注的研究课题之一。本文利用实际结构的地震观测记录,对一维、多维和整体ARX模型三种模态参数识别方法进行了对比分析。结果表明：整体ARX模型对多自由度结构的模态参数识别较为稳定且精度较高;实际应用中多维ARX模型有时会导致丢失模态和虚假模态现象。     

复杂追踪算法结合EEMD识别结构损伤

结构损伤会引起结构振动信号的突变,而该突变信号会淹没于环境噪声信号中。为此,文章将复杂追踪理论(CP)引入结构损伤识别领域,将损伤识别问题转化为突变特征提取问题。提出一种复杂追踪结合集合经验模态分解(EEMD)识别结构损伤的新方法,首先采用EEMD预处理结构振动信号,接着将分解得到的本征模函数(IMF)作为混合信号输入CP模型中,提取出包含损伤特征的本征模函数,进而识别出结构损伤发生的时刻及位置。最后,通过对环境激励下六自由度质量-弹簧系统和地震激励下三层框架模型的数值分析。结果表明,该方法能够准确有效地识别结构损伤异常时刻与位置。     

基于复振型分解的多自由度非线性体系动力可靠性研究

提出了基于复模态理论的多自由度非线性体系动力可靠性分析方法。该方法首先采用等效线性化的方法处理体系的非线性问题,然后采用复模态分析处理非经典的等效线性阻尼矩阵,将具有非经典阻尼的等效多自由度线性体系按复振型分解,将多自由度体系的随机反应分解为一系列一阶体系的复模态反应,从而求得体系的随机反应,最后进行体系的动力可靠度计算。通过算例验证,表明该方法概念明确、思路清晰,为一般多自由度非线性体系提供了一个普遍适用的动力可靠性分析方法。     

基于Hilbert-Huang变换的地震瞬时属性提取方法及应用(英文)

希尔伯特—黄变换(Hilbert-Huang transform,HHT)是一种新的适合非平稳和非线性信号的分析方法,由于地震信号一般呈现出非平稳与非线性特性,因此HHT非常适合地震信号的分析。本文首先介绍了HHT中关于经验模态分解(Empirical Mode Decomposition,EMD)的实现过程,在此基础上分析了几种基于EMD获得本征模态函数(Intrinsic Mode Functions,IMF)来计算瞬时频率的算法,其中利用了两个采样间隔瞬时频率的平均来计算瞬时频率,较好地反映了地震信号频率成分随时间变化的特征。将该方法应用于四川东北部某地区海相碳酸盐岩地层三维地震叠后偏移数据处理,提取"三瞬"地震属性,与传统的希尔伯特变换提取的"三瞬"地震属性进行对比,结果表明基于HHT的"三瞬"地震属性结果具有更高的分辨率,IMF2的瞬时相位能够较好地刻画台地边缘生物礁相,IMF2的瞬时频率亦具有较好的分带性。将IMF2的"三瞬"地震属性与钻井等资料结合分析,能够更好地识别沉积相的分布。     

基于瞬时谱匹配的地震波选波方法及应用

地震波输入对结构非线性响应具有重要影响,选取合适的地震波输入计算是保证结构时程分析响应结果准确的首要条件。文章通过对比地震波瞬时谱的模型化方法,优化并提出可以考虑地震波时频特性的选波方法,并与《建筑抗震设计规范》方法进行对比分析。结果表明：匹配瞬时谱的选波方法可以有效地控制所选地震波的时、频域差异性,降低结构时程分析响应离散度,可为工程选波提供依据。     

Comparing the performance of empirical black‐box models for river flow forecasting in the Heihe River Basin,Northwestern China

For many practical reasons, the empirical black‐box models have become an increasingly popular modelling tool for river flow forecasting, especially in mountainous areas where very few meteorological observatories exist. In this article, precipitation data are used as the only input to estimate river flow. Using five empirical black‐box models—the simple linear model, the linear perturbation model, the linearly varying gain factor model, the constrained nonlinear system model and the nonlinear perturbation model–antecedent precipitation index—modelling results are compared with actual results in three catchments within the Heihe River Basin. The linearly varying gain factor model and the nonlinear perturbation model yielded excellent predictions. For better simulation accuracy, a commonly used multilayer feed‐forward neural network model (NNM) was applied to incorporate the outputs of the individual models. Comparing the performance of these models, it was found that the best results were obtained from the NNM model. The results also suggest that more reliable and precise predictions of river flow can be obtained by using the NNM model while also incorporating the combined outputs of different empirical black‐box models. Copyright © 2012 John Wiley & Sons, Ltd.     

Forecasting river flow using nonlinear dynamics

A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts.     

A family of explicit algorithms for general pseudodynamic testing

A new family of explicit pseudodynamic algorithms is proposed for general pseudodynamic testing. One particular subfamily seems very promising for use in general pseudodynamic testing since the stability problem for a structure does not need to be considered. This is because this subfamily is unconditionally stable for any instantaneous stiffness softening system, linear elastic system and instantaneous stiffness hardening system that might occur in the pseudodynamic testing of a real structure. In addition, it also offers good accuracy when compared to a general second-order accurate method for both linear elastic and nonlinear systems.     

Generalized force vectors for multi‐mode pushover analysis

A generalized pushover analysis (GPA) procedure is developed for estimating the inelastic seismic response of structures under earthquake ground excitations. The procedure comprises applying different generalized force vectors separately to the structure in an incremental form with increasing amplitude until a prescribed seismic demand is attained for each generalized force vector. A generalized force vector is expressed as a combination of modal forces, and simulates the instantaneous force distribution acting on the system when a given response parameter reaches its maximum value during dynamic response to a seismic excitation. While any response parameter can be selected arbitrarily, generalized force vectors in the presented study are derived for maximum interstory drift parameters. The maximum value of any other response parameter is then obtained from the envelope of GPAs results. Each nonlinear static analysis under a generalized force vector activates the entire multi‐degree of freedom effects simultaneously. Accordingly, inelastic actions develop in members with the contribution of all ‘instantaneous modes’ in the nonlinear response range. Target seismic demands for interstory drifts at the selected stories are calculated from the associated drift expressions. The implementation of the proposed GPA is simpler compared with nonlinear response history analysis, whereas it is less demanding in computational effort when compared with several multi‐mode adaptive nonlinear static procedures. Moreover, it does not suffer from the statistical combination of inelastic modal responses obtained separately. The results obtained from building frames have demonstrated that GPA is successful in estimating maximum member deformations and member forces with reference to the response history analysis. When the response is linear elastic, GPA and response spectrum analysis produce identical results. Copyright © 2010 John Wiley & Sons, Ltd.     

Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform

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.     

Extensions of nonlinear error propagation analysis for explicit pseudodynamic testing

Two important extensions of a technique to perform a nonlinear error propagation analysis for an explicit pseudodynamic algorithm (Chang, 2003) are presented. One extends the stability study from a given time step to a complete step-by-step integration procedure. It is analytically proven that ensuring stability conditions in each time step leads to a stable computation of the entire step-by-step integration procedure. The other extension shows that the nonlinear error propagation results, which are derived for a nonlinear single degree of freedom (SDOF) system, can be applied to a nonlinear multiple degree of freedom (MDOF) system. This application is dependent upon the determination of the natural frequencies of the system in each time step, since all the numerical properties and error propagation properties in the time step are closely related to these frequencies. The results are derived from the step degree of nonlinearity. An instantaneous degree of nonlinearity is introduced to replace the step degree of nonlinearity and is shown to be easier to use in practice. The extensions can be also applied to the results derived from a SDOF system based on the instantaneous degree of nonlinearity, and hence a time step might be appropriately chosen to perform a pseudodynamic test prior to testing.     

A new extension of seismic instantaneous frequency using a fractional time derivative

The seismic instantaneous frequency attribute has been applied to interpret a depositional layer. Instead of the standard instantaneous frequency, the Caputo fractional differential operator has been demonstrated as a more suitable mathematical tool of nonlinear analysis for the depositional layer. Based on the Caputo operator, we propose a fractional extension of instantaneous frequency attribute to detect thin layers of sandstone formations. From numerical analysis of a 25-Hz Ricker wavelet, we found that the fractional instantaneous frequency of 0.99 order keeps an approximation of standard instantaneous frequency and enhances the negative frequency (anomalous frequency spike), which help in more precise geological interpretation. A three-layer wedge model and a case history from the Bohai Bay Basin showed that the 0.99 order instantaneous frequency provides more depositional sandstone information than the standard instantaneous frequency. The anomalous frequency spike, at the merging point of the top and the base reflections or at the tip of the stratum, of 0.99 order instantaneous frequency is more prominent than that of the standard one. The results help to interpret the thickness changes of thin sandstone formations. In practical application, the 0.99 order instantaneous frequency has revealed several deposition phases and lateral heterogeneity of a fluvial reservoir system. Therefore, the 0.99 order instantaneous frequency is a valuable seismic attribute for reservoir characterization.     

希尔伯特-黄变换地震信号时频分析与属性提取

地震信号属于非线性和非平稳信号,传统的分析方法主要包括短时傅立叶变换、小波变换和Cohen类时频分布等等;希尔伯特-黄变换是分析非平稳信号的新方法,该方法的关键部分是信号的经验模态分解,通过经验模态分解,复杂的信号可以分解为有限的数量很少的几个固有模态函数,从而可以得到信号的希尔伯特时频谱;将该方法应用于单个的地震道数据,可以对地震道进行经验模态分解并得到希尔伯特谱,应用于地震剖面,可以得到意义更加明确的瞬时频率和瞬时振幅等地震属性,模型试算和实际应用表明了该方法的有效性.