Fractal and chaotic laws on seismic dissipated energy in an energy system of engineering structures

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2000年前辽宁地区地震活动趋势预测

根据数理统计理论和有关文献，利用辽宁地区（３８．７°－４３°Ｎ，１１９°－１２５°Ｅ）１９７０年以来的地震资料，建立了逐年最高震级时间序列的门限自回归模型和修改的震级一频度关系。同时，对辽宁地区地震活动的时空分布特征也作了详细的研究。最后，对辽宁地区未来（到２０００年）地震活动趋势和强度作了预测。     

中国西部及西部边缘Ms≥7．0级地震关系研究

本文研究了中国西部及西部边缘地区Ｍｓ≥７．０级地震在共同应力场源作用下的活动特点和相互关系，指出了两区地震活动具有可类比性。同时，分析了西部区域地震活动，建立了地震活动的动态规律：门限自回归模型，所得结论对评估西部地区的地震形势具有一定价值的指导意义。     

气温和降水时变影响参数的多步预测模型

本文提出了一种气温和降水时变影响参数的多步预测建模新方案。该方案用时间序列均生函数外延矩阵生成的主分量作基函数对序变量建模，同时考虑变量前期数据对后期数据的影响，在模型中引入一个时变影响参数Ｋ１。并用方差分析法求出Ｋ１的显著周期和未来时刻的Ｋ１＋１值，从而建立气温和降水序列的时变影响参数的多步预测模型。该模型用于四川省２０个地、市的气温和降水预报数值试验，其历史拟合率和试报结果与实况值的比较表明，     

近期华北地震大形势的统计分析

文章在３－５年较长时间尺度概率估计的前提下，力争对未来较短时期（１－２年）各区，带地震活动趋势进行定量分析，特别着重于地震总活动水平的比较及年量大震级的预测。     

Parametric time‐domain identification of multiple‐input systems using decoupled output signals

Civil engineering structures are often subjected to multidirectional actions such as earthquake ground motion, which lead to complex structural responses. The contributions from the latter multidirectional actions to the response are highly coupled, leading to a MIMO system identification problem. Compared with single‐input, multiple‐output (SIMO) system identification, MIMO problems are more computationally complex and error prone. In this paper, a new system identification strategy is proposed for civil engineering structures with multiple inputs that induce strong coupling in the response. The proposed solution comprises converting the MIMO problem into separate SIMO problems, decoupling the outputs by extracting the contribution from the respective input signals to the outputs. To this end, a QR factorization‐based decoupling method is employed, and its performance is examined. Three factors, which affect the accuracy of the decoupling result, including memory length, input correlation, and system damping, are investigated. Additionally, a system identification method that combines the autoregressive model with exogenous input (ARX) and the Eigensystem Realization Algorithm (ERA) is proposed. The associated extended modal amplitude coherence and modal phase collinearity are used to delineate the structural and noise modes in the fitted ARX model. The efficacy of the ARX‐ERA method is then demonstrated through identification of the modal properties of a highway overcrossing bridge. Copyright © 2013 John Wiley & Sons, Ltd.     

基于时变窄带滤波技术提取可控震源扫频信号方法研究

精密主动地震监测为我们主动探测地下介质结构, 并监视其动态变化提供了一条可能的技术途径。 由于精密控制震源释放的能量强度小, 随着传播距离的增加, 信号的快速衰减, 在离震源较远处有用的震源信号被掩盖在很强的噪声中, 这对于震相的识别与走时的拾取精度有着很大的影响。 本文设计了一种时变窄带滤波器进行更为精细的滤波, 期望进一步提高观测数据信噪比, 再结合匹配滤波方法实现主动震源信号的检测与波形变换。 另外, 由于不同震相有一定的到时差, 使用时变窄带滤波器提取一个震相波形信息时, 将压制其他震相的波形信号, 从而实现了震相分离的技术。 仿真计算与实际资料处理显示了该方法在提高观测资料质量、 震相识别分辨率及震相分离方面具有一定的有效性与优越性。 通过对广东省新丰江库区精密可控震源试验数据进行时变窄带滤波方法的处理, 在震中距为200 km左右处的台站记录中检测到主动源信号, 体现了对远台弱信号的提取能力。     

Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model

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.     

基于Markov状态切换的水质时序自回归预测模型

水质时序演变特征的研究及预测对制定合理可行的水污染防控措施有重要意义,但水质时序的结构复杂性和非平稳性是采用自回归模型进行预测的瓶颈。针对上述问题,作者将马尔可夫状态切换理论（Markov Switching）应用于水质时序的自回归建模预测。马尔可夫状态切换-自回归模型（MS-AR）是一种研究具有变结构动力特征的时间序列分析方法,对异方差时序有较强的适应性。实例运用中,首先对果河桥断面的氨氮时序进行Box-Cox变换,然后运用MS-AR模型对其进行结构分析及预测。结果表明： MS-AR模型能有效识别出该水质时间序列演变过程中的两种结构模式,通过与经典自回归模型的预测精度相比,该方法的各项指标均优,也说明该方法在水质时间序列动态结构分析和预测方面有良好的应用前景。     

Time series prediction of mooring line top tension by the NARX and Volterra model

To minimize the computation burdens of long-term analysis, the mooring line top tensions under various short-term sea states were predicted using the pre-calculated nonlinear time domain analysis results of some selected sea states. A nonlinear autoregressive with an exogenous input (NARX) technique was introduced with a finite nonlinear memory length to predict the top-tension of a mooring line of the box-type floating production storage and offloading (FPSO). The NARX was designed in such a way that it takes the floater’s motions as its input and produces the mooring line top tension as an output. After training the NARX using the pre-calculated time series of the motions and tensions of some selected sea states, the tension time histories of different sea sates were predicted and compared with the direct time domain analysis results. In addition, to explore the nonlinearity of the system and its contribution to the response, the transfer functions of different orders were extracted after expanding the NARX equation using a Taylor series expansion. The nonlinear contributions coming from both the 3^rd and 5^th order were significant.