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1.
A method based on empirical-mode decomposition (EMD) and vector autoregressive moving average (VARMA) model is proposed for structural damage detection. The basic idea of the method is that the structural damages can be identified as the abrupt changes in energy distribution of structural responses at high frequencies. Using the time-varying VARMA model to represent the intrinsic mode functions (IMFs) obtained from the EMD of vibration signal, we define a damage index according to the VARMA coefficients. In the two examples given, the Imperial County Services Building and the Van Nuys hotel are used as the benchmark structures to verify the effectiveness and sensitivity of the damage index in real environments with the presence of actual noise. The analysis results show that the damage index can indicate the occurrence and relative severity of structural damages at multiple locations in an efficient manner. The damage index can also be potentially used for structural health monitoring, since it is based on the time-varying VARMA coefficients. Finally, some recommendations for future research are provided. 相似文献
2.
Qian Lu Shijie Wang Xiaoyong Bai Fang Liu Shiqi Tian Mingming Wang Jinfeng Wang 《Acta Geochimica》2020,(1):116-126
In near-infrared spectroscopy,the traditional feature band extraction method has certain limitations.Therefore,a band extraction method named the three-step extraction method was proposed.This method combines characteristic absorption bands and correlation coefficients to select characteristic bands corresponding to various spectral forms and then uses stepwise regression to eliminate meaningless variables.Partial least squares regression(PLSR)and extreme learning machine(ELM)models were used to verify the effect of the band extraction method.Results show that the differential transformation of the spectrum can effectively improve the correlation between the spectrum and nickel(Ni)content.Most correlation coefficients were above 0.7 and approximately 20%higher than those of other transformation methods.The model effect established by the feature variable selection method based on comprehensive spectral transformation is only slightly affected by the spectral transformation form.Infive types of spectral transformation,the RPD values of the proposed method were all within the same level.The RPD values of the PLSR model were concentrated between 1.6 and 1.8,and those of the ELM model were between 2.5 and2.9,indicating that this method is beneficial for extracting more complete spectral features.The combination of the three-step extraction method and ELM algorithm can effectively retain important bands associated with the Ni content of the soil.The model based on the spectral band selected by the three-step extraction method has better prediction ability than the other models.The ELM model of the first-order differential transformation has the best prediction accuracy(RP^2=0.923,RPD=3.634).The research results provide some technical support for monitoring heavy metal content spectrum in local soils. 相似文献
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针对多自由度时变系统参数识别问题,基于Daubechies小波多分辨率展开的时变参数辨识方法分析影响参数识别鲁棒性的各个因素。通过数值分析针对突变、线性慢变以及谐波快变的时变参数进行识别,研究结果表明:当基函数dbN一定时,在预先确立的分解尺度范围内,识别精度随分解尺度的增加而增加;待识别参数的频率特性对分解尺度的选择有很大影响,快时变参数比慢时变参数对分解尺度更为敏感;基函数dbN并不是影响识别精度的主要因素;在分解尺度相同的情况下,可以通过提高采样频率增加快时变参数识别精度。 相似文献
4.
Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network 总被引：1，自引：1，他引：0
Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models. 相似文献
5.
Carlos Moutinho 《地震工程与结构动力学》2012,41(14):2059-2073
It is well established that small tuned mass dampers (TMDs) attached to structures are very effective in reducing excessive harmonic vibrations induced by external loads but are not as interesting within the context of earthquake engineering problems. For this reason, large mass ratio TMDs have been proposed with the objective of adding a significant amount of damping to structures, thus constituting a good means of reducing structural response in these cases. This solution has other important and attractive dynamic features such as robustness to system uncertainties and reduction of the motion of the inertial mass. In this context, this paper aims to describe an alternative methodology to existing procedures used to tune these devices to earthquake loads and to present some additional considerations regarding its performance in controlling seismic vibrations. The main feature of the proposed method consists of establishing a direct proportion between the damping ratios of the structure's first two vibration modes and the adopted mass ratio. By equalizing the damping ratios of the system's main vibration modes, this proposal also facilitates the use of simplified methods, such as modal analysis based on response spectra. To demonstrate the usefulness of this alternative methodology, an application example is presented, which was also used to perform a parametric study involving other tuning methods and to estimate mass ratio values from which there is no significant advantage in increasing the TMD mass. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins. 相似文献
7.
本文首先分析了地震波在黏弹介质的传播规律,基于黏弹介质地震波动方程总结了时变子波振幅谱和相位谱的关系,从而得出结论,准确估计子波相位谱初值和不同时刻的子波振幅谱是实现时变子波准确提取的必要条件.在此基础上,针对传统方法限制子波振幅谱形态且受限于分段平稳假设的问题,提出了一种利用EMD(Empirical Mode Decomposition)和子波振幅谱与相位谱关系的时变子波提取方法,根据子波对数振幅谱光滑连续而反射系数对数振幅谱振荡剧烈的特点,采用EMD方法将不同时刻地震记录的对数振幅谱分解为一组具有不同振荡尺度的模态分量,通过滤除振荡剧烈分量、重构光滑连续分量提取时变子波振幅谱;再应用子波振幅谱和相位谱的关系提取时变子波相位谱,将分别提取的振幅谱和相位谱逐点进行合成,最终实现时变子波的准确提取.本文方法不需要求取Q值,适用于变Q值的情况,具有良好的抗噪性能.数值仿真和叠后实际资料处理结果表明,相比传统的分段提取方法,利用本文方法提取的时变子波准确度更高,研究成果对提高地震资料分辨率具有重要意义. 相似文献
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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
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Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform 总被引：1，自引：1，他引：0
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
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提出一种基于切比雪夫正交分解的非线性结构外荷载识别方法及分解阶数确定办法.在识别过程中建立非线性结构体系状态空间方程,并将切比雪夫正交多项式展开系数扩展于状态量,对状态量进行递推估计.通过结构反应频域分析筛选频率范围并确定正交多项式项数.文中将通过6层隔震结构、波形钢腹板PC 组合梁桥的数值仿真和3层隔震框架的振动台试... 相似文献
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重力反演是恢复地下密度空间分布的有效工具,而选择合理的密度模型约束方法是提升重力反演分辨率和可靠性的关键.常规约束方法大多是从剖分网格空间中的密度模型出发,通过调整光滑或稀疏约束权重来匹配反演目标,但当地质体类型多样、异常分离不准确及网格剖分方案不合理时,模型约束的合理性与灵活性难以得到有效保证.为此,本文提出了一种基于密度模型稀疏表征的重力反演方法.首先假设待反演的密度模型表征为模型特征矩阵和稀疏分解系数的线性组合,之后重新推导了重力反演目标函数,并给出了分解系数的稀疏求解过程.相比现有重力反演方法,用于构建模型特征矩阵的特征模型可包含不同类型地质体的先验几何信息,分解系数的稀疏性保证了待反演目标来自于最典型的地质模式组合.最后,通过模型试验及实际资料验证了基于密度模型稀疏表征的重力反演方法的有效性. 相似文献
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与常规雷达相比,超宽带雷达具有距离分辨力高、近距离盲区小、穿透性强、目标识别率高等特点,已被广泛应用于灾后搜寻、救援工作中,以对受困生命体征目标进行生命探测。为实现使用超宽带雷达对受困生命体征目标的识别定位,本研究提出基于信号多特征提取技术及支持向量机模型的人体呼吸信号识别方法。首先,使用经验模态分解、变分模态分解及希尔伯特变换提取雷达探测信号的微多普勒特征,使用傅里叶变换提取宏观频谱特征,使用相关分析获取相关性特征;然后,以提取的信号特征为输入,使用支持向量机模型对信号进行分类,进而对人体呼吸信号进行识别,对人体位置进行定位。不同障碍物场景下的试验结果表明,本方法可有效识别砖墙、建筑楼板等遮挡物下的受困生命体征目标,并提供其位置信息。 相似文献
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Structural health monitoring of long-span suspension bridges using wavelet packet analysis 总被引：1，自引：0，他引：1
During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage. This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform (WPT). The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum (WPES) has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations. 相似文献
14.
Hae Young Noh Dimitrios G. Lignos K. Krishnan Nair Anne S. Kiremidjian 《地震工程与结构动力学》2012,41(4):681-696
Fragility functions are commonly used in performance‐based earthquake engineering for predicting the damage state of a structure subjected to an earthquake. This process often involves estimating the structural damage as a function of structural response, such as the story drift ratio and the peak floor absolute acceleration. In this paper, a new framework is proposed to develop fragility functions to be used as a damage classification/prediction method for steel structures based on a wavelet‐based damage sensitive feature (DSF). DSFs are often used in structural health monitoring as an indicator of the damage state of the structure, and they are easily estimated from recorded structural responses. The proposed framework for damage classification of steel structures subjected to earthquakes is demonstrated and validated with a set of numerically simulated data for a four‐story steel moment‐resisting frame designed based on current seismic provisions. It is shown that the damage state of the frame is predicted with less variance using the fragility functions derived from the wavelet‐based DSF than it is with fragility functions derived from an alternate acceleration‐based measure, the spectral acceleration at the first mode period of the structure. Therefore, the fragility functions derived from the wavelet‐based DSF can be used as a probabilistic damage classification model in the field of structural health monitoring and an alternative damage prediction model in the field of performance‐based earthquake engineering. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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基于传统面向对象方法, 提出了一种基于最优特征空间的损毁建筑物信息提取方法。 采用ESP(Estimate of Scale Parameter)工具对图像进行最优尺度分割, 之后通过选取样本, 计算各类地物距离矩阵和最小分离距离寻求最优特征空间, 最后运用最优特征空间对震后损毁建筑物影像进行提取实验, 在QuickBird影像中提取总体精度达到了83.1％, Kappa系数达到了0.813, 在无人机影像中提取总体精度为92.9％, Kappa系数达到了0.940。 本文建立的提取方法与传统分类决策树方法相比, 其提取精度和效率都有较大提高, 在损毁建筑物信息提取方面具有较好的推广价值。 相似文献
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基于HHT的非线性结构系统识别研究 总被引：13，自引：2，他引：11
本文研究基于HHT的多自由度非线性结构系统识别方法。首先通过EMD分解得到结构的非线性模态(NNM),然后对非线性模态进行H ilbert分析,识别出结构的瞬时特征参数(瞬时振幅、瞬时固有频率等),进而由各参数间关系识别出非线性结构的类型。最后通过一个具有非线性刚度的两自由度剪切型建筑结构的数值模拟验证了该方法的有效性。 相似文献
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Approximate seismic risk assessment of building structures with explicit consideration of uncertainties 
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下载免费PDF全文 An approximate seismic risk assessment procedure for building structures, which involves pushover analysis that is performed utilizing a deterministic structural model and uncertainty analysis at the level of the equivalent SDOF model, is introduced. Such an approach is computationally significantly less demanding in comparison with procedures based on uncertainty analysis at the level of the entire structure, but still allows for explicit consideration of the effect of record‐to‐record variability and modelling uncertainties. A new feature of the proposed pushover‐based method is the so‐called probabilistic SDOF model. Herein, the proposed methodology is illustrated only for reinforced concrete (RC) frames, although it could be implemented in the case of any building structure, provided that an appropriate probabilistic SDOF model is available. An extensive parametric analysis has been performed within the scope of this study in order to develop a probabilistic SDOF model, which could be used for the seismic risk assessment of both code‐conforming and old, that is, non code‐conforming RC frames. Based on the results of risk analysis for the four selected examples, it is shown that the proposed procedure can provide conservative estimates of seismic risk with reasonable accuracy, in spite of the employed simplifications and the relatively small number of Monte Carlo simulations with Latin hypercube sampling, which are performed at the level of the SDOF model. An indication of the possible default values of dispersion measures for limit‐state intensities in the case of low to medium‐height RC frames is also presented. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
20.
谱分解技术在地震解释领域已得到广泛应用,但常用的谱分解方法存在两方面的不足.一是时间分辨率低,难以对薄层进行刻画;二是在烃类检测中多解性强,难以区分流体类型.为了改善该问题,本文提出一种基于地震复谱分解技术的烃类检测方法.复谱分解是指用一个包含多个不同频率Ricker子波的复子波库对地震道进行分解,从而得到时变子波频率和相位信息的过程.借助稀疏反演技术复谱分解可以获得高分辨率的时频能量谱和时频相位谱.本文首先通过拟合算例验证了复谱分解方法刻画薄层的能力以及求取子波频率和相位的准确性.然后利用基于Kelvin-Voigt模型的黏弹波动方程数值模拟对衰减引起子波相位改变的原因进行了分析.最后通过实际资料应用展示了本文方法在储层预测中的高时间分辨率优势,验证了利用子波相位信息识别气藏的有效性. 相似文献