Improved modal truncation error in the directly analytical method for damage identification of frame structures

The damage identification is made by the numerical simulation analysis of a five-storey-and-two-span RC frame structure, using improved and unimproved direct analytical method respectively; and the fundamental equations were solved by the minimal least square method （viz. general inverse method）. It demonstrates that the feasibility and the accuracy of the present approach were impoved significantly, compared with the result of unimproved damage identification.

Output only modal identification and structural damage detection using time frequency & wavelet techniques

The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Mooring Damage Identification of Floating Wind Turbine Using a Non-Probabilistic Approach Under Different Environmental Conditions

This paper discusses the damage identification in the mooring line system of a floating wind turbine(FWT)exposed to various environmental loads.The proposed method incorporates a non-probabilistic method into artificial neural networks(ANNs).The non-probabilistic method is used to overcome the problem of uncertainties.For this purpose,the interval analysis method is used to calculate the lower and upper bounds of ANNs input data.This data contains some of the natural frequencies utilized to train two different ANNs and predict the output data which is the interval bounds of mooring line stiffness.Additionally,in order to reduce computational time and more importantly,identify damage in various conditions,the proposed method is trained using constant loads(CL)case(deterministic loads,including constant wind speed and airy wave model)and is tested using random loads(RL)case(including Kaimal wind model and JONSWAP wave theory).The superiority of this method is assessed by applying the deterministic method for damage identification.The results demonstrate that the proposed non-probabilistic method identifies the location and severity of damage more accurately compared to a deterministic one.This superiority is getting more remarkable as the difference in uncertainty levels between training and testing data is increasing.

Research of Physical Parameter Identification and Damage Localization based on the Gibbs Sampling

A new method for structural physical parameter identification is proposed for linear structure.Firstly,a linear structural identification model was obtained based on a series of transformation of the dynamic characteristic equation.Then the posterior distribution of the model is obtained by the Bayesian updating theory.Using the structural modal parameters and considering their randomness,the structural stiffness parameter is obtained from the conditional posterior distribution of the linear structural identification model.The Gibbs sampling based on the Markov Chain Monte Carlo(MCMC)method is employed during the process.In order to illustrate the proposed method,a 3-DOF linear shear building is used as an example to detect and quantify its damage based on model data measured before and after a severe loading event.The research shows that damage level and locations can be identified with little error by using proposed method.

A new neural-network-based method for structural damage identification in single-layer reticulated shells

Single-layer reticulated shells(SLRSs)find widespread application in the roofs of crucial public structures,such as gymnasiums and exhibition center.In this paper,a new neural-network-based method for structural damage identification in SLRSs is proposed.First,a damage vector index,NDL,that is related only to the damage localization,is proposed for SLRSs,and a damage data set is constructed from NDL data.On the basis of visualization of the NDL damage data set,the structural damaged region locations are identified using convolutional neural networks(CNNs).By cross-dividing the damaged region locations and using parallel CNNs for each regional location,the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated.Second,a damage vector index,DS,that is related to the damage location and damage degree,is proposed for SLRSs.Based on the damaged region identified previously,a fully connected neural network(FCNN)is constructed to identify the location and damage degree of members.The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS.The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.

A SINGULAR VALUE DECOMPOSITION BASED TRUNCATION ALGORITHM IN SOLVING THE STRUCTURAL DAMAGE EQUATIONS

The structural damage identification through modal data often leads to solving a set of linear equations. Special numerical treatment is sometimes required for an accurate and stable solution owing to the ill conditioning of the equations. Based on the singular value decomposition (SVD) of the coefficient matrix, an error based truncation algorithm is proposed in this paper. By rejection of selected small singular values, the influence of noise can be reduced. A simply-supported beam is used as a simulation example to compare the results to other methods. Illustrative numerical examples demonstrate the good efficiency and stability of the algorithm in the nondestructive identification of structural damage through modal data.

退耕还林地生态环境损害评估案例分析及应用

近年来,随着我国经济的高速发展,林地生态环境损害事件日益增加。不同类型的生态系统或同一生态系统在不同区域所提供的生态服务功能侧重均不同,鉴定评估林地生态系统的环境损害价值时应选用不同的测算方法。通过阐述理论公式法和实际监测法的原理和适用范围及其在生态环境损害评估中的应用,并对量化过程中参数的选取原则与方法进行说明。结合案例分析非法占用退耕还林地导致的生态环境损害范围和程度,计算出2012年~2019年的生态环境损害价值量为205万元。研究退耕还林地生态环境损害典型案例,并将其推广应用于小规模普通林地,对林地生态环境损害案件的鉴定评估环节具有重要引领作用。

基于CEEMD和统计参数的斜拉桥损伤识别方法研究

为解决仅使用互补集成经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)方法的斜拉桥信号分解存在含噪固有模态函数(intrinsic mode function,IMF)分量且不能进行损伤定量的问题,提出了一种基于CEEMD与统计参数方法相结合的斜拉桥损伤识别方法。该方法基于CEEMD方法对斜拉桥动力响应信号进行自适应性分解,确定适用的白噪声幅值标准差并推导CEEMD方法的集成次数,得到各阶IMF分量;采用欧氏距离对分解的IMF分量进行谱系聚类分析以避免模态混叠现象;采用峰度统计参数的有效权重峰度指标方法滤除含噪IMF分量,提取有效IMF分量并重构为有效IMF分量和;利用变异系数统计参数、二阶中心差分法和泰勒展开式推导损伤定位指标,根据四阶统计矩峰度统计参数推导损伤定量指标。用所提方法对某斜拉桥进行损伤识别研究,结果表明:仿真分析的损伤定位识别精度为100%,损伤定量最大误差为1.80%;在高斯白噪声干扰下,损伤定位不受影响,损伤定量最大误差为1.88%;进行实桥的损伤识别,结果表明实桥主梁无损伤。

弧形绝缘子金属连接环局部机械损坏识别仿真

弧形绝缘子金属连接环通常安装在电力设备上,由于其所处的环境复杂,且存在各种环境干扰因素的影响,使得其局部出现机械损坏时,识别结果不准确。为了精准识别连接环局部机械损坏,提出一种弧形绝缘子金属连接环局部机械损坏识别方法。通过形态学开—闭重构运算对弧形绝缘子金属连接环图像展开建模,使用差分运算获取差分图像,并结合最大熵值法对其分割。根据弧形绝缘子金属连接环的特点,排除非损伤部分对图像识别的干扰。采用基于分形理论的差分盒维法,将图像划分为多个子块并计算其分形维数值,并判断图像是否在数值范围内,从而实现对连接环局部机械损坏的识别。实验结果表明,所提方法可以精准识别弧形绝缘子金属连接环局部机械损坏,具有比较强的适用性。

基于改进灰狼优化算法的结构损伤识别

结合模态柔度矩阵、广义模态柔度矩阵和振型三个识别精度较好的指标,构造新的目标函数求解损伤识别问题。通过Nelson方法求解得到的频率与振型的导数,得到对结构刚度发生变化时更具敏感性的位置,然后在这些位置布置传感器以提取结构信息。针对原有的灰狼算法虽然全局搜索能力强,但是存在局部搜索精度差的问题,本文从初始种群和收敛因子等方面着手,改善灰狼算法的局部搜索能力及收敛速度。最后利用提出的方法,通过识别梁模型及桁架模型中的损伤单元说明本文方法的有效性。

水平地震作用下双级加筋土挡墙损伤识别研究

因对台阶式加筋土挡墙损伤识别的研究不足,开展了双级加筋土挡墙的大型振动台试验。采用时域识别方法分析了水平地震作用下模型的动力响应特征,阐述了上、下级挡墙自振频率、阻尼比的分布规律,探究了结构损伤程度与自振频率、阻尼比间的对应关系。研究结果表明:加载前上、下级挡墙的自振频率基本一致,阻尼比随墙高的增加而减小;随着加载工况的累积,自振频率逐渐减小,阻尼比逐渐增大。采用数学方法对自振频率和阻尼比分布曲线进行多项式拟合,对比分析可得:当自振频率减小0~15.41%、阻尼比增大0~299.35%时,结构处于基本完好阶段;当自振频率减小15.41%~18.92%、阻尼比增大299.35%~360.07%时,结构处于轻微破坏阶段;当自振频率减小18.92%~21.29%、阻尼比增大360.07%~398.21%时,结构处于中等破坏阶段;当自振频率减小21.29%~29.60%、阻尼比增大398.21%~532.99%时,结构处于毁坏状态。

基于改进粒子群算法的层合板损伤定量检测方法

针对层合板在加工和服役过程中出现的结构损伤问题,提出基于差分法和改进粒子群算法的两阶段损伤定量检测方法。首先,采用单元弹性模量降低的方法定义了结构损伤单元,通过结构振动特性分析获得了不同损伤工况层合板数值模型的模态参数,运用差分法识别了损伤大致区域。其次,利用改进的粒子群算法定量计算损伤的精确位置和程度。所提算法通过待测结构模型和迭代更新后有限元模型模态参数的柔度矩阵构建目标函数,引入变异因子以使算法有效的跳出局部收敛,MS算子减少了健康单元对真实损伤单元损伤程度的影响。结果表明:基于改进的粒子群算法可精确识别复合材料层合板结构损伤单元的位置和损伤程度。

基于子结构模型降阶和数据驱动的杆塔结构损伤识别方法

针对受静载作用的输电杆塔大型复杂结构,提出了一种基于子结构模型降阶和数据驱动的损伤回归识别方法.根据杆塔框架结构特征及其在自重和覆冰静载作用下的变形特征划分子结构,确定结构可能出现的损伤状态,定义损伤指标.采用子结构模型降阶方法对含损伤结构的有限元模型进行降阶,形成降阶模型库.进一步,根据杆塔受载特征确定标定载荷,根据变形及破坏模式设计应变传感器布置方案,采用有限元方法计算降阶模型库中所有模型在标定载荷作用下的变形,构建数据集.以传感器测点的应变数据作为输入,损伤指标作为输出,利用BP神经网络算法建立损伤回归识别模型,实现杆塔损伤位置识别和损伤指标预测,为杆塔结构健康状态实时监测技术开发奠定了基础.

一种改进的残余力向量法在结构损伤识别中的应用

残余力向量法是结构损伤识别中常用的方法,复杂结构中单元数量较多而损伤位置较少,容易造成无关变量增多,进而导致计算量过大的问题。鉴于此,本文提出一种改进的残余力向量法用于结构的损伤识别。该方法利用刚度联系向量与残余力向量之间线性相关的特性,以向量投影值作为损伤定位的影响系数,初步筛选出结构可能出现损伤的单元范围。在此基础上,构建出残余力向量对应的线性方程组,根据顺序主子式不为零的条件,对线性方程组进行行初等变换,再根据单元的数量保留前n维线性方程,通过求解该方程组的代数解可得到该结构单元的刚度损伤参数。以简支梁为例的数值模拟表明,本文方法可减少无关单元变量的计算,降低残余力向量的维度并且具有较好的抗噪能力。

砂岩破裂状态声发射梅尔倒谱系数判识方法

岩体结构破裂是严重制约矿山、地铁、隧道等地下空间工程建设及其安全运行的重要因素。实现对岩体结构破裂状态的识别是当下研究的热点与重点之一。为此,开展了不同条件的砂岩加载破坏实验,提取了加载全程的声发射梅尔倒谱系数及其波动差,研究了系数及其波动差在砂岩受载破坏全程的变化规律,分析了1号系数(一组声发射梅尔倒谱系数包括12个,1号系数指第1个声发射梅尔倒谱系数)及其波动差与砂岩破裂状态的相关性特征,基于此提出了砂岩破裂状态声发射梅尔倒谱系数判识方法,构建了判识准则并进行判识效果检验。结果表明:随载荷增加,1号系数整体上增大,系数及其离散性在破坏阶段显著增大并表现出显著的规律波动性特征;1号系数波动差具有阶段性变化特征,波动差的大小及其起伏变化可表征砂岩的破裂,波动差整体增大及突增的变化可反映砂岩非稳定变形和峰后破坏阶段的宏观破裂,波动差的突增幅度可反映砂岩破裂程度;声发射梅尔倒谱系数及其波动差对砂岩破裂表现出良好的响应特征,该特征受不同加载条件的影响较小,说明声发射梅尔倒谱系数在反映砂岩破裂上具有适用性;1号系数及其波动差与砂岩破裂状态具有较好相关性,该相关性可分为3个阶段,即1号系数及其波动差在砂岩微破裂阶段分布集中,在临近失稳破坏阶段分布范围急剧增大、整体值升高且出现高异常值,在峰后破坏阶段分布范围进一步增大、整体值更高、高异常值更多;利用1号系数的75%位点值和异常值、1号系数波动差的75%位点值和异常值构建了砂岩破裂状态判识准则,采用三分类模型混淆矩阵对判识准则的效果进行了检验,判识准确度和精准度分别为90.43%、94.45%。该成果可为其他种类煤岩的破裂状态识别提供借鉴,为煤岩失稳监测预警提供参考。

基于ICEEMDAN的近断层地震动识别方法改进

近断层地震动严重威胁重要结构的安全,然而地震动波形复杂导致近断层地震动识别困难,经典Baker识别方法因采用小波技术,而存在母小波与潜在脉冲差异过大,致使识别产生误差。针对该问题,利用具有自适应噪声的完全集成经验模式分解(ICEEMDAN)对Baker方法进行改进,改进方法利用白噪声对信号分解过程进行辅助,从而增强了信号分析方法对地震动这类复杂信号的应对能力。为提高改进方法的识别效率和质量,对太平洋工程抗震中心数据库中的3 655条地震动进行识别。结果表明,由于ICEEMDAN技术的加入改进后的地震动识别方法共识别出192条近断层地震动,比经典Baker方法则识别出的163条近断层地震动多识别出17.79%的近断层地震动。为进一步探讨识别出的近断层地震动的近断层特性,从地震反应谱和地震损伤入手进行分析,两种方式获得近断层地震动反应谱长周期反应剧烈、峰值周期高,有明显的近断层特征;在地震损伤方面,以一典型钢管混凝土拱桥为案例,对两种方式识别出的近断层地震动及远场地震动进行地震响应计算。结果表明,两种方法获得的近断层地震动有着极为相似的地震损伤规律,且均较远场地震动的损伤有明显的增加。综上,两种方法识别出的近断层地震动均有明显的近断层特性,而基于ICEEMDAN技术的改进方法增加了17.79%的近断层地震动数量,将ICEEMDAN技术引入可以利用该技术较强的鲁棒性,从而对波形较为复杂的速度脉冲进行识别,进而在不降低近断层地震动识别质量的前提下,提升Baker识别方法的识别效率和准确性。

基于改进小波包方法的衬砌损伤识别研究

研究目的:针对隧道衬砌发生损伤的识别方法,提出了一种基于小波包能量的计算方法,在基于前人的研究成果上进行了改进,得到了一种算法简单、损伤识别效果显著的方法,并建立了一个新的损伤指标ELG(小波包结点能量丰富点控制指标);建立了三维隧道有限元模型,通过多种损伤工况计算,研究了该方法在不同工况下的损伤识别效果;进行了模拟实验,通过小车-管道模型模拟隧道衬砌在列车荷载作用下的响应,并使用该方法对响应数据进行分析。研究结论:(1)新的损伤指标ELG具有高效率、高准确性的优点,可以作为一种衬砌的损伤识别方法;(2)损伤识别结果的精确程度取决于测点的布置密度,测点间距约为监测长度段的1/10时,为能否准确识别损伤的临界值,若大于此值,则不具备识别效果。

基于曲率模态的结构损伤识别

随着国内桥梁数量已趋于饱和,更多的桥梁结构进入运维阶段,对老旧桥梁的损伤识别、对新建桥梁的健康监测已经成为亟待解决的社会问题。基于桥梁结构的动力特征响应信息,利用人工神经网络的数据分析方法,提出桥梁结构损伤识别的三步识别法:首先利用固有频率判别桥梁结构是否发生了损伤;若发生损伤,则利用曲率模态分析损伤位置;在损伤位置确定后,利用人工神经网络进一步对数据的隐含信息进行提取,识别损伤程度。研究证明人工神经网络在桥梁结构损伤识别方面有较强的适用性,可以为今后的发展提供一定参考。

基于HIFU回波信号和多尺度模糊熵的生物组织变性识别研究

为了解决高强度聚焦超声(HIFU)治疗中的监测问题,在无需引入其他监测源的情况下,通过研究HIFU回波基波与二次谐波的多尺度模糊熵(MFE),提出了一种生物组织变性辨析新方法。HIFU回波信号通过谱减法去噪后,利用信息散度优化的变分模态分解(KLD-VMD)提取其基波与二次谐波分量,然后结合基波与二次谐波的MFE对组织进行变性识别,并使用等错误概率(EER)评价了该方法的有效性。最后,研究还比较了KLD-VMD与VMD、经验模态分解(EMD)和固有时间尺度分解(ITD)等其他分解方法,结合MFE分析了其辨析变性组织的能力。试验结果表明:基于KLD-VMD和MFE的组织变性识别其EER达到5.1%,相较于其他方法表现出了更好的识别效果;结合基波和二次谐波的识别结果比使用单一特征参数更好。该研究为HIFU治疗提供了一种新的监测方法,具有潜在的实际应用价值。