基于改进模态参数灵敏度法的结构损伤识别研究

推导了模态参数对于损伤构件的一阶和二阶灵敏度矩阵,并对在推导一阶和二阶振型灵敏度的过程中产生的模态截尾误差进行了改进。根据泰勒级数展开的原理分别建立了一阶和二阶的灵敏度方程。考虑到一阶灵敏度方程求解速度快和二阶灵敏度方程求解精度高的特点,本文提出了一种用于结构损伤识别的混合迭代算法,该算法用二阶非线性的解析解作为算法的第一次迭代值,用一阶灵敏度方程的求解值对该算法的第一次迭代值进行关于泰勒级数截尾误差的修正。研究表明,本文提出的混合迭代算法由于采用了精确度较高的二阶非线性解析解作为迭代修正的初值,因此,迭代修正精度更高,收敛性更好。     

基于改进遗传算法的公路桥梁损伤程度标定的两阶段法

提出了基于改进遗传算法的公路桥梁损伤程度标定的两阶段法。第一阶段:应用静应变残差进行损伤定位;第二阶段:基于已经识别出的损伤位置,利用改进的遗传算法进行损伤程度的标定。两阶段方法有效地克服了同时进行常规的损伤位置识别和损伤程度的标定的收敛速度慢、存储空间大及可能误标定等问题。某三跨连续桥梁应用分析发现,在已知很少实测数据的情况下,对损伤程度的识别取得较理想的效果,证实了基于改进遗传算法的两阶段法用于损伤程度的识别具有更高的效率,更好的灵敏度、稳定性和可靠性。     

基于振动台试验的RC框架模型修正及模拟损伤识别

利用有限元模型修正技术综合利用理论建模和实验建模的优点,可以得到更加符合结构实际的基准模型,为结构动力分析、损伤诊断及健康监测提供更可靠的依据。基于一12层钢筋混凝土框架模型振动台试验测点加速度记录,采用特征系统实现算法对该模型结构进行模态参数识别,识别结果与有限元分析结果之间存在明显的差异。采用基于灵敏度分析的参数型有限元模型修正技术,选择识别精度较高的实测模态频率为修正基准,以构件的弹性模量和密度为修正参数,对该框架的初始有限元模型进行了修正,得到基准有限元模型。进一步以基准有限元模型为标准,以构件弹性模量的降低模拟结构的损伤,对两种假设工况下的损伤结构进行修正,得到构件弹性模量的变化值并与假设的降低值对比,验证了有限元模型修正技术在结构损伤识别中应用的可行性。     

基于支持向量机与分层遗传算法的斜拉桥全结构损伤分步识别

为了实现斜拉桥全结构的损伤识别,提出一种支持向量机与分层遗传算法相结合的分步识别方法。该方法首先按结构的材料特性将斜拉桥分为主梁、索塔、拉索三类子结构,利用支持向量机的分类特性判定损伤的来源,确定损伤属于某一类子结构;然后,应用分层遗传算法对子结构中的单元进行损伤位置与损伤程度的识别。以实验室独塔斜拉桥模型作为研究对象进行数值仿真,结果表明:采用支持向量机方法能较准确的对主梁、索塔、拉索三类子结构的损伤进行分类,确定损伤的来源;分层遗传算法能快速有效的完成斜拉桥某一子结构中损伤单元的定位与识别;两种算法结合的分步识别方法,实现了斜拉桥全结构的损伤识别,同时分步识别策略减少了支持向量机训练样本与遗传算法中初始种群的规模,提升了寻优效率。     

基于一维卷积神经网络的结构损伤识别

传统结构损伤识别需对采集数据进行分析,提取相应特征进行损伤诊断。特征提取过程需消耗大量的计算成本,无法满足结构健康监测在线损伤识别的需求。为提高损伤识别的计算效率和自动化程度,提出基于一维卷积神经网络的结构损伤识别方法,其特点是可以直接从原始振动信号中自主学习损伤特征,并准确快速地识别结构的损伤位置和损伤程度。采用简支梁数值模型和IABMAS BHM Benchmark数值模型验证所提方法的有效性。数值结果表明:所建立的一维卷积神经网络模型能够准确识别结构的损伤位置和损伤程度,具备一定的抗噪性能,整体模型收敛快,对单条样本测试延迟低。设计了钢框架结构损伤识别试验,采用所提方法对框架结构的损伤情况进行了识别。分析结果表明:所提方法可准确识别结构损伤程度及损伤类别,测试集准确率为100%,验证了方法在实际结构损伤识别的应用可行性。     

地震波双模态时变修正Kanai-Tajimi非平稳随机模型的改进及参数识别

在Vlachos等提出的双模态时变修正Kanai-Tajimi功率谱模型及其参数识别方法的基础上,利用杜修力等提出的Kanai-Tajimi功率谱滤波方法并引进遗传算法及二次优化识别技术进行改进,建立地震动时变功率谱的参数模型化方法。通过集集地震波的时变功率谱模型参数识别及模拟地震动算例,验证改进后的双模态时变修正Kanai-Tajimi功率谱模型的可行性和有效性,其方法可运用到重大工程结构抗震分析的设计地震动输入中。     

基于灵敏度分析的模型修正方法在隔震桥梁的运用

运用灵敏度分析技术对某隔震桥梁进行了模型修正。以结构自振频率差值作为收敛准则,通过摄动待修正参数获得结构的灵敏度矩阵,继而优化迭代求得结构参数修正值,从而达到模型修正目的。修正研究结果表明:基于灵敏度分析的模型修正方法能较好地运用在隔震桥梁上。     

基于峰值位移均值识别非线性结构的损伤

目前对结构进行损伤识别的方法大多基于结构振动信号的变化而进行，不同振动信号在不同环境下的损伤识别能力各不相同。为验证振动信号在随机环境下的的识别效果，以随机振动理论为基础，针对非线性结构的损伤识别问题，提出了利用结构峰值位移均值进行损伤识别的新方法。该方法不需要结构损伤前的模态参数。通过对两种不同类型非线性结构的数值模拟分析，对该方法进行了验证。结果表明，该方法能够有效识别结构的单处损伤、多处损伤以及损伤程度，充分显示了该指标检测损伤的准确性和敏感性。     

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。     

应用人工神经网络技术的大型斜拉桥子结构损伤识别研究

本文应用人工神经网络技术对大型斜拉桥结构进行了子结构损伤识别研究。文中首先介绍了子结构损伤识别的基本方法，然后应用自组织竞争神经网络建立了对于大型桥梁结构识别子结构损伤情况的子结构损伤识别方法，并且应用BP网络进一步建立了大型桥梁结构各子结构内部的损伤位置和损伤程度的识别方法，数值模拟了一大跨度斜拉桥子结构损伤以及子结构内部损伤的识别过程，最后得出结论：(1)基于自组织竞争网络的子结构损伤识别方法能迅速准确地识别大型结构的损伤情况；(2)基于BP网络所建立的结构损伤识别方法，能对子结构中结构损伤的位置和程度进行进一步的识别；(3)基于人工神经网络技术的结构损伤识别方法是大型土木工程结构损伤识别的有效方法，可在工程结构损伤识别中广泛应用。     

New Results at Mayon,Philippines, from a Joint Inversion of Gravity and Deformation Measurements

In this paper, we detail the combination of the genetic algorithm (GA) inversion technique with the elastic-gravitational model originally developed by Rundle and subsequently refined by Fernández and others. A sensitivity analysis is performed for the joint inversion of deformation and gravity to each of the model parameters, illustrating the importance of proper identification of both the strengths and limitations of any source model inversion, and this technique in particular. There is a practical comparison of the theoretical results with the inversion of geodetic data observed at the Mayon volcano in the Philippines, where there are gravity changes without significant deformation, after the 1993 eruption.     

A combinatorial optimization scheme for parameter structure identification in ground water modeling

This research develops a methodology for parameter structure identification in ground water modeling. For a given set of observations, parameter structure identification seeks to identify the parameter dimension, its corresponding parameter pattern and values. Voronoi tessellation is used to parameterize the unknown distributed parameter into a number of zones. Accordingly, the parameter structure identification problem is equivalent to finding the number and locations as well as the values of the basis points associated with the Voronoi tessellation. A genetic algorithm (GA) is allied with a grid search method and a quasi-Newton algorithm to solve the inverse problem. GA is first used to search for the near-optimal parameter pattern and values. Next, a grid search method and a quasi-Newton algorithm iteratively improve the GA's estimates. Sensitivities of state variables to parameters are calculated by the sensitivity-equation method. MODFLOW and MT3DMS are employed to solve the coupled flow and transport model as well as the derived sensitivity equations. The optimal parameter dimension is determined using criteria based on parameter uncertainty and parameter structure discrimination. Numerical experiments are conducted to demonstrate the proposed methodology, in which the true transmissivity field is characterized by either a continuous distribution or a distribution that can be characterized by zones. We conclude that the optimized transmissivity zones capture the trend and distribution of the true transmissivity field.     

Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm

This study proposes an inverse solution algorithm through which both the aquifer parameters and the zone structure of these parameters can be determined based on a given set of observations on piezometric heads. In the zone structure identification problem fuzzy c-means (FCM) clustering method is used. The association of the zone structure with the transmissivity distribution is accomplished through an optimization model. The meta-heuristic harmony search (HS) algorithm, which is conceptualized using the musical process of searching for a perfect state of harmony, is used as an optimization technique. The optimum parameter zone structure is identified based on three criteria which are the residual error, parameter uncertainty, and structure discrimination. A numerical example given in the literature is solved to demonstrate the performance of the proposed algorithm. Also, a sensitivity analysis is performed to test the performance of the HS algorithm for different sets of solution parameters. Results indicate that the proposed solution algorithm is an effective way in the simultaneous identification of aquifer parameters and their corresponding zone structures.     

基于遗传算法优化神经网络权值的大坝结构损伤识别

针对传统 BP 神经网络存在着容易陷入局部极小点、训练时间太长等缺点,本文采用基于浮点编码的遗传算法,对 BP 神经网络的初值空间进行了遗传优化。用基于浮点编码的遗传算法来优化 BP 神经网络的权值,可得到最佳初始权值矩阵,并按误差前向反馈算法,沿负梯度搜索进行网络学习。文中以混凝土重力坝结构作为算例,用结构的模态频率变化作为网络的输入向量,结构的损伤位置作为输出向量,对网络进行了训练。仿真结果表明：遗传 BP 神经网络的收敛和诊断能力优于传统 BP 神经网络,可有效地运用到大坝结构的健康诊断与损伤识别中。     

基于经验遗传-单纯形算法和结构模态参数识别结构物理参数的方法

对于复杂的大自由度系统的反演分析,遗传算法每步计算中包含大量的正演分析,成为限制遗传算法应用的运行速度的瓶颈。减少反演分析中的正演计算次数,是扩大遗传算法适用范围的有效途径。经验遗传-单纯形算法正是解决这一问题的一种有效方法。本文将这一方法应用于不完全模态参数已知条件下的结构物理参数识别研究。结果表明:本文建议的方法有精度和搜索效率高、对初值选取依赖性不强、可以反映"残缺"的高阶模态信息等优点。     

Non-parametric identification of a class of nonlinear multidegree dynamic systems

A non-parametric identification technique is presented for chain-like multidegree-of-freedom non-linear dynamic systems. The method uses information about the state variables of non-linear systems to express the system characteristics in terms of two-dimensional orthogonal functions. The technique is applied to a model of a steel frame that has been extensively investigated both analytically and experimentally. The method can be used with deterministic or random excitation to identify dynamic systems with arbitrary non-linearities, including those with hysteretic characteristics. It is also shown that the method is easy to implement and needs much less computer time and storage requirements compared to the Wiener-kernel approach. The method is shown to have low sensitivity to the effects of additive noise in the experimental data.     

Optimal design of soil dynamic compaction using genetic algorithm and fuzzy system

Computational intelligent techniques, such as fuzzy and genetic algorithm, have proven to be useful in modeling of complex nonlinear phenomena such as dynamic compaction. Dynamic compaction method is used to improve the mechanical behavior of underlying soil layers especially loose granular materials. The method involves the repeated impart of high-energy impacts to the soil surface using steel or concrete tampers with weights ranging 10–40 ton and with drop heights ranging 10–40 m. A relatively exact estimation of dynamic compaction level is of major concern to geotechnical engineers. This paper develops a fuzzy set base method for the analysis of dynamic compaction phenomenon. In this model, the input variables are tamper weight, height of tamper drop, print spacing, tamper radius, number of impact and soil layer geotechnical properties. The main shortcoming of this technique is uncertainty to locate the best sketch of dynamic compaction to gain maximum effect of this method of soil improvement. Therefore, this paper describes the incorporation of genetic algorithm methodology using fuzzy system for determining the optimum design of dynamic compaction. Subsequently, it will be shown that the genetic algorithm has some abilities in the optimization of dynamic compaction design. Also different manners of this algorithm are compared and then the optimized structure of genetic algorithm will be suggested for dynamic compaction.     

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

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.