Comparison of adaptive structural damage identification techniques in nonlinear hysteretic vibration isolation systems

Early structural damage identification to obtain an accurate condition assessment can assist in the reprioritization of structural retrofitting schedules in order to guarantee structural safety. Nowadays, seismic isolation technology has been applied in a wide variety of infrastructure, such as buildings, bridges, etc., and the health conditions of these nonlinear hysteretic vibration isolation systems have received considerable attention. To effectively detect structural damage in vibration isolation systems based on vibration data, three time-domain analysis techniques, referred to as the adaptive extended Kalman filter （AEKF）, adaptive sequential nonlinear least-square estimation （ASNLSE） and adaptive quadratic sum-sqnares error （AQSSE）, have been investigated. In this research, these analysis techniques are compared in terms of accuracy, convergence and efficiency, for structural damage detection using experimental data obtained through a series of laboratory tests based on a base-isolated structural model subjected to E1 Centro and Kobe earthquake excitations. The capability of the AEKF, ASNLSE and AQSSE approaches in tracking structural damage is demonstrated and compared.     

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。     

结构损伤识别方法研究综述

对工程结构进行损伤识别与检测,可以发现结构损伤位置,评估损伤程度,为结构加固与修复提供依据,从而保证工程结构正常运行,进而保护人们生命财产安全,因此结构损伤识别方法研究一直是土木工程领域重要研究课题。结构损伤识别方法总体上分为确定性方法和不确定性方法,相比于确定性方法,不确定性方法考虑了识别过程中不确定因素的影响,成为目前损伤识别领域的研究热点。本文回顾了确定性方法和不确定性方法发展历程,阐述了几种常见的损伤识别方法及其优缺点,并根据国内外研究现状对结构损伤识别方法发展进行了展望,可供损伤识别方法研究与应用参考。     

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.     

A procedure on ground motion selection and scaling for nonlinear response of simple structural systems

This study presents a ground-motion selection and scaling methodology that preserves the basic seismological features of the scaled records with reduced scatter in the nonlinear structural response. The methodology modifies each strong-motion recording with known fundamental seismological parameters using the estimations of ground-motion prediction equations for a given target hazard level. It provides robust estimations on target building response through scaled ground motions and calculates the dispersion about this target. This alternative procedure is not only useful for record scaling and selection but, upon its further refinement, can also be advantageous for the probabilistic methods that assess the engineering demand parameters for a given target hazard level. Case studies that compare the performance of the proposed procedure with some other record selection and scaling methods suggest its usefulness for building performance assessment and loss models. Copyright © 2012 John Wiley & Sons, Ltd.     

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 identifi cation and structural damage detection.Identif ication 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 ...     

Bayesian model selection for ARX models and its application to structural health monitoring

A Bayesian framework for model order selection of auto‐regressive exogenous (ARX) models is developed and applied to actual earthquake response data obtained by the structural health monitoring system of a high‐rise building. The model orders of ARX models are selected appropriately by the Bayesian framework, and differ significantly from the optimal order estimated by AIC; in fact, in many cases AIC does not even give an optimal order. A method is also proposed for consistently selecting the same ‘genuine’ modes of interest from the whole set of modes corresponding to each of the identified models from a sequence of earthquake records. In the identification analysis based on building response records from 43 earthquakes over 9 years, the modal parameters of the first four modes in each horizontal direction are estimated appropriately in all cases, showing that the developed methods are effective and robust. As the estimates of natural frequency depend significantly on the response amplitude, they are compensated by an empirical correction so that the influence of the response amplitude is removed. The compensated natural frequencies are much more stable over the nine‐year period studied, indicating that the building had no significant change in its global dynamic characteristics during this period. Copyright © 2010 John Wiley & Sons, Ltd.     

Substructural first‐ and second‐order model identification for structural damage assessment

This paper presents two methods to perform system identification at the substructural level, taking advantage of reduction in the number of unknowns and degrees of freedom (DOFs) involved, for damage assessment of fairly large structures. The first method is based on first‐order state space formulation of the substructure where the eigensystem realization algorithm (ERA) and the observer/Kalman filter identification (OKID) are used. Identification at the global level is then performed to obtain the second‐order model parameters. In the second method, identification is performed at the substructural level in both the first‐ and second‐order model identification. Both methods are illustrated using numerical simulation studies where results indicate their significantly better performance than identification using the global structure, in terms of efficiency and accuracy. A 12‐DOF system and a fairly large structural system with 50 DOFs are used where the effects of noisy data are considered. In addition to numerical simulation studies, laboratory experiments involving an eight‐storey frame model are carried out to illustrate the performance of the proposed method. The identification results presented in terms of the stiffness integrity index show that the proposed methodology is able to locate and quantify damage fairly accurately. Copyright © 2005 John Wiley & Sons, Ltd.     

基于地震信息的岩体物理力学参数分析软件与应用

采用了C 语言和Visual C 6.0开发环境开发了一套基于地震信息,并结合地质和测井资料的综合分析软件,获得的了国家软件版权,该软件由5个模块组成,即基本参数模块、试验分析模块、声波测井标定和修正模块和岩石(体)物理力学参数分析模块和自定义模块.将基本参数模块、试验分析模块得到的结果应用到岩石(体)物理力学参数分析模块中,也可以根据用户实际需要和研究区特征自定义函数进行计算.各部分之间数据相互依赖,运行时一般遵循其逻辑顺序,但它们又能保持相对独立.通过淮南煤田潘东勘探试验区应用取得了好的应用效果.     

Modeling of environmental influence in structural health assessment for reinforced concrete buildings

One branch of structural health monitoring (SHM) utilizes dynamic response measurements to assess the structural integrity of civil infrastructures. In particular,modal frequency is a widely adopted indicator for structural damage since its square is proportional to structural stiffness. However,it has been demonstrated in various SHM projects that this indicator is substantially affected by fluctuating environmental conditions. In order to provide reliable and consistent information on the health status of the monitored structures,it is necessary to develop a method to filter this interference. This study attempts to model and quantify the environmental influence on the modal frequencies of reinforced concrete buildings. Daily structural response measurements of a twenty-two story reinforced concrete building were collected and analyzed over a one-year period. The Bayesian spectral density approach was utilized to identify the modal frequencies of this building and it was clearly seen that the temperature and humidity fluctuation induced notable variations. A mathematical model was developed to quantify the environmental effects and model complexity was taken into consideration. Based on a Timoshenko beam model,the full model class was constructed and other reduced-order model class candidates were obtained. Then,the Bayesian modal class selection approach was employed to select the one with the most suitable complexity. The proposed model successfully characterizes the environmental influence on the modal frequencies. Furthermore,the estimated uncertainty of the model parameters allows for assessment of the reliability of the prediction. This study not only improves the understanding about the monitored structure,but also establishes a systematic approach for reliable health assessment of reinforced concrete buildings.     

Subspace system identification of support excited structures—part II: gray‐box interpretations and damage detection

A theoretical framework is presented for the estimation of the physical parameters of a structure (i.e., mass, stiffness, and damping) from measured experimental data (i.e., input–output or output‐only data). The framework considers two state‐space models: a physics‐based model derived from first principles (i.e., white‐box model) and a data‐driven mathematical model derived by subspace system identification (i.e., black‐box model). Observability canonical form conversion is introduced as a powerful means to convert the data‐driven mathematical model into a physically interpretable model that is termed a gray‐box model. Through an explicit linking of the white‐box and gray‐box model forms, the physical parameters of the structural system can be extracted from the gray‐box model in the form of a finite element discretization. Prior to experimental verification, the framework is numerically verified for a multi‐DOF shear building structure. Without a priori knowledge of the structure, mass, stiffness, and damping properties are accurately estimated. Then, experimental verification of the framework is conducted using a six‐story steel frame structure under support excitation. With a priori knowledge of the lumped mass matrix, the spatial distribution of structural stiffness and damping is estimated. With an accurate estimation of the physical parameters of the structure, the gray‐box model is shown to be capable of providing the basis for damage detection. With the use of the experimental structure, the gray‐box model is used to reliably estimate changes in structural stiffness attributed to intentional damage introduced. Copyright © 2012 John Wiley & Sons, Ltd.     

A neural network approach for structural identification and diagnosis of a building from seismic response data

This work presents a novel procedure for identifying the dynamic characteristics of a building and diagnosing whether the building has been damaged by earthquakes, using a back‐propagation neural network approach. The dynamic characteristics are directly evaluated from the weighting matrices of the neural network trained by observed acceleration responses and input base excitations. Whether the building is damaged under a large earthquake is assessed by comparing the modal parameters and responses for this large earthquake with those for a small earthquake that has not caused this building any damage. The feasibility of the approach is demonstrated through processing the dynamic responses of a five‐storey steel frame, subjected to different strengths of the Kobe earthquake, in shaking table tests. Copyright © 2002 John Wiley & Sons, Ltd.     

A frequency response function change method for damage localization and quantification in a shear building under ground excitation

A frequency response function change (FRFC) method to detect damage location and extent based on the change in the frequency response functions of a shear building under the effects of ground excitation was proposed in this paper. The damage identification equation was derived from the motion equations of the system before and after the occurrence of the damage. Efforts to make the FRFC method less model‐dependent were made. Intact system matrices, which could be estimated using the measured data without the need for an analytical model, and the frequency response functions were required for the FRFC method. The effects of measurement noise and model parameter error in the FRFC method were studied numerically. The proposed FRFC method was validated by experimental studies of a six‐story steel building structure with single and multiple damage cases. Copyright © 2012 John Wiley & Sons, Ltd.