Output‐only structural identification in time domain: Numerical and experimental studies

By identifying changes in stiffness parameters, structural damage can be detected and monitored. Although considerable progress has been made in this research area, many challenges remain in achieving robust structural identification based on incomplete and noisy measurement signals. The identification task is made even more difficult if measurement of input force is to be eliminated. To this end, an output‐only structural identification strategy is proposed to identify unknown stiffness and damping parameters. A non‐classical approach based on genetic algorithms (GAs) is adopted. The proposed strategy makes use of the recently developed GA‐based method of search space reduction, which has shown to be able to accurately and reliably identify structural parameters from measured input and output signals. By modifying the numerical integration scheme, input can be computed as the parameter identification task is in progress, thereby eliminating the need to measure forces. Numerical and experimental results demonstrate the power of the strategy in accurate and efficient identification of structural parameters and damage using only incomplete acceleration measurements. Copyright © 2007 John Wiley & Sons, Ltd.     

Condition assessment of structures under unknown support excitation

A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses. The support excitation acting on a structure is modeled by orthogonal polynomial approximations, and the sensitivities of structural dynamic response with respect to its physical parameters and orthogonal coeffi cients are derived. The identifi cation equation is based on Taylor’s fi rst orde...     

On‐line identification of non‐linear hysteretic structural systems using a variable trace approach

In this paper, an adaptive on‐line parametric identification algorithm based on the variable trace approach is presented for the identification of non‐linear hysteretic structures. At each time step, this recursive least‐square‐based algorithm upgrades the diagonal elements of the adaptation gain matrix by comparing the values of estimated parameters between two consecutive time steps. Such an approach will enforce a smooth convergence of the parameter values, a fast tracking of the parameter changes and will remain adaptive as time progresses. The effectiveness and efficiency of the proposed algorithm is shown by considering the effects of excitation amplitude, of the measurement units, of larger sampling time interval and of measurement noise. The cases of exact‐, under‐, over‐parameterization of the structural model have been analysed. The proposed algorithm is also quite effective in identifying time‐varying structural parameters to simulate cumulative damage in structural systems. Copyright © 2001 John Wiley & Sons, Ltd.     

基于自然激励技术和FT时移特性的模态阻尼比识别方法

基于环境激励下结构动力响应信号分析与处理识别结构的模态参数,是结构健康监测和损伤诊断的一个重要环节,目前为止,要得到较为可靠的识别结果仍有一定困难,尤其是模态阻尼比。基于自然激励技术和傅里叶变换的时移特性,提出了一种新的结构模态阻尼比估算方法,通过理论推导和仿真算例验证了该方法的可行性,进而利用一刚构-连续组合梁桥在环境激励下的动力测试数据,通过该方法对其阻尼比进行了识别,并将识别结果与数据驱动随机子空间法的识别结果进行了对比。结果表明:提出的方法可以减轻噪声影响,得到可接受的识别结果,可为大型工程结构阻尼比的识别提供一个方便和有效的途径。     

Model updating for real time dynamic substructures based on UKF algorithm

Combining the advantages of numerical simulation with experimental testing, real-time dynamic substructure (RTDS) testing provides a new experimental method for the investigation of engineered structures. However, not all unmodeled parts can be physically tested, as testing is often limited by the capacity of the test facility. Model updating is a good option to improve the modeling accuracy for numerical substructures in RTDS. In this study, a model updating method is introduced, which has great performance in describing this nonlinearity. In order to determine the optimal parameters in this model, an Unscented Kalman Filter (UKF)-based algorithm was applied to extract the knowledge contained in the sensors data. All the parameters that need to be identified are listed as the extended state variables, and the identification was achieved via the step-by-step state prediction and state update process. Effectiveness of the proposed method was verified through a group of experimental data, and results showed good agreement. Furthermore, the proposed method was compared with the Extended Kalman Filter (EKF)-based method, and better accuracy was easily found. The proposed parameter identification method has great applicability for structural objects with nonlinear behaviors and could be extended to research in other engineering fields.     

Multi‐objective framework for structural model identification

Structural identification based on measured dynamic data is formulated in a multi‐objective context that allows the simultaneous minimization of the various objectives related to the fit between measured and model predicted data. Thus, the need for using arbitrary weighting factors for weighting the relative importance of each objective is eliminated. For conflicting objectives there is no longer one solution but rather a whole set of acceptable compromise solutions, known as Pareto solutions, which are optimal in the sense that they cannot be improved in any objective without causing degradation in at least one other objective. The strength Pareto evolutionary algorithm is used to estimate the set of Pareto optimal structural models and the corresponding Pareto front. The multi‐objective structural identification framework is presented for linear models and measured data consisting of modal frequencies and modeshapes. The applicability of the framework to non‐linear model identification is also addressed. The framework is illustrated by identifying the Pareto optimal models for a scaled laboratory building structure using experimentally obtained modal data. A large variability in the Pareto optimal structural models is observed. It is demonstrated that the structural reliability predictions computed from the identified Pareto optimal models may vary considerably. The proposed methodology can be used to explore the variability in such predictions and provide updated structural safety assessments, taking into consideration all Pareto structural models that are consistent with the measured data. Copyright © 2005 John Wiley & Sons, Ltd.     

基于摄动有限元方法对梁结构损伤的识别

结构损伤的定量识别是工程技术中急待解决的问题。利用矩阵摄动和结构有限元动力学理论推出梁结构损伤程度定量识别的公式和方法,该方法仅需要在役结构的固有频率测量值就可识别结构的损伤位置和损伤程度,而且可以识别结构的老化程度,避免了由模态振型识别损伤,因测量自由度不足带来的误差,通过对一钢悬臂梁损伤识别的数值仿真,证明了该方法的有效性。该方法具有较大的工程应用价值。     

随机子空间方法在桥塔模态参数识别中的应用

基于环境振动的结构模态参数识别方法正逐渐成为国内外研究的一大热点。环境振动方法就是仅仅利用结构测试的输出信号进行结构的模态参数识别,随机子空间方法就是其中的一种。随机子空间法是近年来发展起来的一种线性系统辩识方法,可以有效地从环境激励的结构响应中获取模态参数。它属于时域的方法,该方法不需要进行FFT变换,它不仅可以识别结构的频率,而且可以识别结构的阻尼和振型。文章首先介绍了随机子空间的理论,然后用该方法对正在施工中的南京长江三桥的南塔进行模态参数识别,通过与其他方法的识别结果进行比较,证明随机子空间方法不失为一种有效的模态参数识别方法。     

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.     

Damage identification of a 3D full scale steel–concrete composite structure with partial‐strength joints at different pseudo‐dynamic load levels

Partial‐strength composite steel–concrete moment‐resisting (MR) frame structures represent an open research field in seismic design from both a theoretical and an experimental standpoint. Among experimental techniques, vibration testing is a well‐known and powerful technique for damage detection, localization and quantification, where actual modal parameters of a structure at different states can be determined from test data by using system identification methods. However, the identification of semi‐rigid connections in framed structures is limited, and hence this paper focuses on a series of vibration experiments that were carried out on a realistic MR frame structure, following the application of pseudo‐dynamic and quasi‐static cyclic loadings at the European laboratory for structural assessment of the Joint Research Centre at Ispra, Italy, with the scope of understanding the structural behaviour and identifying changes in the dynamic response. From the forced vibration response, natural frequencies, damping ratios, modal displacements and rotations were extracted using the circle fitting technique. These modal parameters were used for local and global damage identification by updating a 3D finite element model of the intact structure. The identified results were then correlated with observations performed on the structure to understand further the underlying damage mechanisms. Finally, the latin hypercube sampling technique, a variant of the Monte Carlo method, was employed in order to study the sensitivity of the updated parameters of the 3D model to noise on the modal inputs. Copyright © 2009 John Wiley & Sons, Ltd.     

基于模糊神经网络和证据理论的结构损伤识别新方法

为了有效利用结构健康监测系统中的多源不确定数据,提高损伤识别的正确率,通过构造模糊神经网络(FNN)分类器,提出了一种新的概率赋值函数构造方法和数据融合损伤识别新方法.该损伤识别方法先对数据预处理,提取有效的特征参数,接着将它作为FNN的输入,构造FNN分类器,最后运用数据融合中的D-S证据理论计算出融合决策结果.为了验证所提方法的有效性,通过一个七层剪切型框架结构的数值模型,分别用单一FNN分类器和数据融合损伤识别方法进行了损伤识别和比较.研究结果表明,本文所提方法比单一决策结果更准确,具有更高的可靠度。     

Fault diagnosis using noise modeling and a new artificial immune system based algorithm

A new fault classification/diagnosis method based on artificial immune system(AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms(GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.     

基于正交分解的非线性结构外荷载识别方法研究

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验验证所提基于正交分解的荷载识别方法可行性。研究结果表明,基于切比雪夫正交基分解的外荷载识别方法及正交基项数确定方法,适用于非线性结构的荷载识别。从识别效果上看,即使在噪声及模型误差因素的影响下外荷载仍然能够得到较好的识别。     

Rapid visual screening of soft-story buildings from street view images using deep learning classification

Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.

     

Algorithms for time synchronization of wireless structural monitoring sensors

Dense networks of wireless structural health monitoring systems can effectively remove the disadvantages associated with current wire‐based sparse sensing systems. However, recorded data sets may have relative time‐delays due to interference in radio transmission or inherent internal sensor clock errors. For structural system identification and damage detection purposes, sensor data require that they are time synchronized. The need for time synchronization of sensor data is illustrated through a series of tests on asynchronous data sets. Results from the identification of structural modal parameters show that frequencies and damping ratios are not influenced by the asynchronous data; however, the error in identifying structural mode shapes can be significant. The results from these tests are summarized in Appendix A. The objective of this paper is to present algorithms for measurement data synchronization. Two algorithms are proposed for this purpose. The first algorithm is applicable when the input signal to a structure can be measured. The time‐delay between an output measurement and the input is identified based on an ARX (auto‐regressive model with exogenous input) model for the input–output pair recordings. The second algorithm can be used for a structure subject to ambient excitation, where the excitation cannot be measured. An ARMAV (auto‐regressive moving average vector) model is constructed from two output signals and the time‐delay between them is evaluated. The proposed algorithms are verified with simulation data and recorded seismic response data from multi‐story buildings. The influence of noise on the time‐delay estimates is also assessed. Copyright © 2004 John Wiley & Sons, Ltd.     

Parameter identification of hysteretic model of rubber-bearing based on sequential nonlinear least-square estimation

In order to evaluate the nonlinear performance and the possible damage to rubber-bearings (RBs) during their normal operation or under strong earthquakes, a simplified Bouc-Wen model is used to describe the nonlinear hysteretic behavior of RBs in this paper, which has the advantages of being smooth-varying and physically motivated. Further, based on the results from experimental tests performed by using a particular type of RB (GZN110) under different excitation scenarios, including white noise and several earthquakes, a new system identification method, referred to as the sequential nonlinear least-square estimation (SNLSE), is introduced to identify the model parameters. It is shown that the proposed simplified Bouc-Wen model is capable of describing the nonlinear hysteretic behavior of RBs, and that the SNLSE approach is very effective in identifying the model parameters of RBs.     

Application of subspace identification technique to long‐term seismic response monitoring of structures

This paper addresses the issue of structural system identification using earthquake‐induced structural response. The proposed methodology is based on the subspace identification algorithm to perform identification of structural dynamic characteristics using input–output seismic response data. Incorporated with subspace identification algorithm, a scheme to remove spurious modes is also used to identify real system poles. The efficiency of the proposed method is shown by the analysis of all measurement data from all measurement directly. The recorded seismic response data of three structures (one 7‐story RC building, one midisolation building, and one isolated bridge), under Taiwan Strong Motion Instrumentation Program, are analyzed during the past 15 years. The results present the variation of the identified fundamental modal frequencies and damping ratios from all the recorded seismic events that these three structures had encountered during their service life. Seismic assessment of the structures from the identified system dynamic characteristics during the period of their service is discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

Probabilistic approach for active control of structures: experimental verification

In our previous study (Earthquake Engineering and Structural Dynamics 2003; 32 :2301), we have developed a probabilistic algorithm for active control of structures. In the probabilistic control algorithm, the control force is determined by the probability that the structural energy exceeds a specified target critical energy, and the direction of a control force is determined by the Lyapunov controller design method. In this paper, an experimental verification of the proposed probabilistic control algorithm is presented. A three‐story test structure equipped with an active mass driver (AMD) has been used. The effectiveness of the control algorithm has been examined by exciting the test structure using a sinusoidal signal, a scaled El Centro earthquake and a broadband Gaussian white noise; and, especially, experiments on control have been performed under different conditions to that of system identification in order to prove the stability and robustness of the proposed control algorithm. The experimental results indicate that the probabilistic control algorithm can achieve a significant response reduction under various types of ground excitations even when the modeling error exists. Copyright © 2004 John Wiley & Sons, Ltd.     

地震破裂模式的模糊模式识别

把几种可能的物理机制作为识别对象,定义了计算各识别对象与物理现象相符合的证据量．从这种思想出发,提出了判定强震破裂模式的模糊模式识别准则,只要把各识别对象的“证据量”带人这个识别准则,就可识别出某指定强震的地震破裂机制该方法被具体地应用于炉霍地震（1973,Ms＝7.9）破裂机制的识别,结果表明炉霍地震符合barrier破裂机制这个结论与有关炉霍地震破裂机制的地震学研究结果是完全一致的该方法比已有的其他方法更为简便,因此有推广价值.