随机地震模型及反应谱分析

本文以实际地震加速度的频谱特性和相位谱特性为依据,仿概率统计理论中的高斯概率密度函数,建立了随机地震加速度频谱数学模型。通过选用不同的模型参数,用自编的程序进行大量的计算,得到了具有不同特性的随机地震加速度反应谱曲线。根据地震场地条件与地面运动主周期的对立关系,本文对具有不同主频率特性的地震加速度反应谱曲线进行了分区整理,得到了基本适合各类地震场地特性的外包、中值、内切反应谱,并将三种反应谱与Housner平均加速度反应谱、ELCentro地震加速度反应谱包线及我国工民建抗震设计规范中的标准设计反应谱进行比较分析。

一种随机地震作用荷载识别方法研究

为研究不同模型下地震作用荷载识别的方法,本文基于Kalman滤波理论,并选取了一种随机地震模型,对结构在随机地震动作用下的识别方法进行了研究。本方法首先模拟地震动作用于结构,通过得到结构加速度响应后将系统扩展形成新的系统状态,并用Kalman滤波对这个新的系统状态进行估计,即用加速度响应值反演所受到的地震作用,同时研究结构分别在地震模型模拟出的平稳激励过程和非平稳激励过程下识别结果的准确性。结果表明:在满足一定要求下,随机地震作用荷载能够被Kalman滤波准确地识别反演,最大误差仅在7%左右。由此可知,无论是平稳激励过程还是非平稳激励过程,本方法均可以高效地预估反演所受到的地震作用,为今后的相关研究提供依据与参考。

地震加速度反应谱拟合的直接法研究

本文研究地震动加速度时程的反应谱拟合方法，通过给定地震动加速度时程的三角级数表达式，从理论上推导了响应的直接计算公式，与传统的反应谱拟合方法相比，本文所给的方法无须进行功率谱转换及迭代运算即可直接生成拟合目标反应谱的地震动加速度时程，因此，具有计算量少和计算精确简便的特点，所给算例表明该方法具有较好的拟合精度。

Random Vibration Analysis of Urban Underground Tunnels Under Vertical Earthquake Excitations Based on Mass–Damper–Spring Model

In this paper, the verticalseismic effects on tunnels are studied based on a classic mass–damper–spring model. An analyticaldiscrete modelof urban underground tunnels subjected to verticalearthquake excitations is proposed by considering the first verticalvibration mode. Taking a light railproject in Tianjin as an example, this study uses the proposed discrete modelto analyze the displacements of tunneland soilunder verticalearthquake excitations. The soildisplacement responses at different tunnellocations are analyzed with linear random vibration theory.The computationalcost is greatly reduced using the proposed model. It can be seen that different from the case of horizontalearthquakes, the displacement responses under verticalearthquake excitations keep growing after seismic acceleration reaches its peak for a short duration, and then,they begin to decay. The soils at different positions around the tunnels have large relative displacement under verticalearthquake excitations. Moreover, a finite-element modelis also established for displacement responses using ABAQUS.The comparison with the results of the finite-element modelshows that the results of the proposed discrete modelare available.

oncausal spatial prediction filtering based on an ARMA model

Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

Self-organized Criticality in an Earthquake Model on Random Network

A simplified Olami-Feder-Christensen model on a random network has been studied. We propose a new toppling rule -- when there is an unstable site toppling, the energy of the site is redistributed to its nearest neighbors randomly not averagely. The simulation results indicate that the model displays self-organized criticality when the system is conservative, and the avalanche size probability distribution of the system obeys finite size scaling. When the system is nonconservative, the model does not display scaling behavior. Simulation results of our model with different nearest neighbors q is also compared, which indicates that the spatial topology does not alter the critical behavior of the system.

Use of probabilistic and deterministic measures to identify unfavorable earthquake records

This study introduces measures to identify resonant (concentration of energy in a single or a few frequencies) or unfavorable earthquake ground motions. Probabilistic measures based on the entropy rate and the geometric properties of the power spectral density function (PSDF) of the ground acceleration are developed first. Subsequently, deterministic measures for the frequency content of the ground acceleration are also developed. These measures are then used for identifying resonance and criticality in stochastic earthquake models and 110 acceleration records measured at rock, stiff, medium and soft soil sites. The unfavorable earthquake record for a given structure is defined as the record having a narrow frequency content and dominant frequency close to the structure fundamental natural frequency. Accordingly, the measures developed in this study may provide a basis for selecting records that are capable of producing the highest structural response. Numerical verifications are provided on damage caused to structures by identified resonant records.

Physical random function model of ground motions for engineering purposes

A physical random function model of ground motions for engineering purposes is presented with verification of sample level. Firstly,we derive the Fourier spectral transfer form of the solution to the definition problem,which describes the one-dimensional seismic wave field. Then based on the special models of the source,path and local site,the physical random function model of ground motions is obtained whose physical parameters are random variables. The superposition method of narrow-band harmonic wave groups is improved to synthesize ground motion samples. Finally,an application of this model to simulate ground motion records in 1995 Kobe earthquake is described. The resulting accelerograms have the frequencydomain and non-stationary characteristics that are in full agreement with the realistic ground motion records.