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.     

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.     

Identification of modal parameters of a 600-m-high skyscraper from field vibration tests

Accurate prediction of the dynamic responses of a high-rise building subjected to dynamic loads such as earthquake and wind excitations requires the information of its structural dynamic properties such as modal parameters including natural frequencies and damping ratios. This paper presents the identification results of the modal parameters based on field vibration tests on a 600-m high skyscraper. A set of tests, including ambient vibration test (AVT) and free vibration test (FVT), were conducted on the skyscraper to identify its modal parameters. Firstly, this paper presents and discusses the modal parameters of the skyscraper assessed by several identification methods applied to the AVT measurements. These methods include the wavelet transform (WT) method, the stochastic subspace identification (SSI) method, and the random decrement technique (RDT). Secondly, an active mass damper (AMD) system with total mass 1000 tons equipped into the skyscraper was used to excite the building for estimation of the modal parameters by FVT. Thirdly, this paper presents observations on the structural dynamic behavior of the skyscraper with the operation of the AMD system during a typhoon event. The field measurement results show that the AMD system functioned efficiently for suppression of the wind-induced vibrations of the skyscraper during the typhoon. This paper aims to further understand the structural dynamic properties of super-tall buildings and provide useful information for structural design and vibration control of future skyscrapers.     

Evolution of modal characteristics of a mid-rise cold-formed steel building during construction and earthquake testing

Vibration-based structural identification is an essential technique for assessing structural conditions by inferring information from the dynamic characteristics of structures. However, the robustness of such techniques in monitoring the progressive damage of real structures has been validated with only a handful of research efforts, largely due to the paucity of monitoring data recorded from damaged structures. In a recent experimental program, a mid-rise cold-formed steel building was constructed at full scale atop a large shake table and subsequently subjected to a unique multi-hazard scenario including earthquake, post-earthquake fire, and finally post-fire earthquake loading. Complementing the simulated hazard events, low-amplitude vibration tests, including ambient vibrations and white noise base excitation tests, were conducted throughout the construction and the test phases. Using the vibration data collected during the multi-hazard test program, this paper focuses on understanding the modal characteristics of the cold-formed steel building in correlation with the construction and the structural damage progressively induced by the simulated hazard events. The modal parameters of the building (i.e., natural frequencies, damping ratios, and mode shapes) are estimated using two input–output and two output-only time-domain system identification techniques. Agreement between the evolution of modal parameters and the observations of the progression of physical damage demonstrates the effectiveness of the vibration-based system identification techniques for structural condition monitoring and damage assessment.     

Localized identification of MDOF structures in the frequency domain

For the identification of multi-degree-of-freedom structures, it is not practical to identify all of the parameters included in the structures because enormous computation time is required and because identifiability may not be possible. In this paper, a localized identification approach through substructuring is formulated in the frequency domain. A technique of spectral smoothing is incorporated in the approach to deal with noise-corrupted data. The proposed approach can be used to identify the structural parameters in any part of interest in a structure. The numerical investigations for a lumped mass-spring-dashpot system indicate that faster convergence and higher accuracy are achieved and the noise influences on the identified results are reduced greatly by spectral smoothing. The approach also applies to whole-structure identification if the required records available and the numerical example shows that higher accuracy results are obtained with less cpu time and more poorly guessed initial values as compared with the general complete-structure identification.     

System identification and modeling of a dynamically tested and gradually damaged 10‐story reinforced concrete building

This paper discusses the dynamic tests, system identification, and modeling of a 10‐story reinforced concrete building. Six infill walls were demolished in 3 stages during the tests to introduce damage. In each damage stage, dynamic tests were conducted by using an eccentric‐mass shaker. Accelerometers were installed to record the torsional and translational responses of the building to the induced excitation, as well as its ambient vibration. The modal properties in all damage states are identified using 2 operational modal analysis methods that can capture the effect of the wall demolition. The modal identification is facilitated by a finite element model of the building. In turn, the model is validated through the comparison of the numerically and experimentally obtained modal parameters. The validated model is used in a parametric study to estimate the influence of structural and nonstructural elements on the dynamic properties of the building and to assess the validity of commonly used empirical formulas found in building codes. Issues related to the applicability and feasibility of system identification on complex structures, as well as considerations for the development of accurate, yet efficient, finite element models are also discussed.     

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...     

Tracking modal parameters from building seismic response data using recursive subspace identification algorithm

Tracking modal parameters and estimating the current structural state of a building from seismic response measurements, particularly during strong earthquake excitations, can provide useful information for building safety assessment and the adaptive control of a structure. Therefore, online or recursive identification techniques need to be developed and implemented for building seismic response monitoring. This paper develops and examines different methods to track modal parameters from building seismic response data. The methods include recursive data‐driven subspace identification (RSI‐DATA) using Givens rotation algorithm, and RSI‐DATA using Bona fide algorithm. The question on how well the results of RSI‐DATA reflect the real condition is investigated and verified with a bilinear SDOF simulation study. Time‐varying modal parameters of a four‐story reinforced concrete school building are identified based on a series of earthquake excitations, including several seismic events, large and small. Discussions on the different methods' ability to track the time‐varying modal parameters are presented. The variation of the identified building modal frequencies and damping ratios from a series of event‐by‐event seismic responses, particularly before and after retrofitting of the building is also discussed. Copyright © 2017 John Wiley & Sons, Ltd.     

APPLICATION OF OFF-LINE AND ON-LINE IDENTIFICATION TECHNIQUES TO BUILDING SEISMIC RESPONSE DATA

The objectives of this paper are to present a comparison of the dynamic characteristics of a seven-storey reinforced concrete building (Van Nuys–Holiday Inn) identified from four recorded strong-motion response data (Whittier earthquake, Landers earthquake, Big Bear earthquake and Northridge earthquake). In the analysis, time-domain methods for estimating the system parameters and the modal properties of the building are studied. Both off-line and on-line identification algorithms are applied to these seismic response data. Under the assumption of a linear time-invariant system the ARX model and ARMAX model are used. Comparison of the identification results using different models are made. In addition, recursive procedures are adapted as on-line identification and the time-varying modal parameters are estimated. For structural systems under strong earthquake excitation, a recursive identification method, adaptive forgetting through multiple models (AFMM), is introduced to identify systems with rapidly changing parameters. Through the analysis of the seismic response data of the building subjected to four earthquakes the identification algorithm and the identification results are discussed.     

Influence of lateral-load-resisting system on the earthquake response of structures—a system identification study

Analysis and comparison of the dynamic responses of three well instrumented (with accelerographs) high-rise buildings shaken during the 1984 Morgan Hill earthquake are presented. The buildings examined in the present work are (i) the Town Park Towers Apartment building, a 10-storey, concrete shear wall building; (ii) the Great Western Savings and Loan building, a 10-storey building with concrete frames and shear walls; and (iii) the Santa Clara County Office building, a 13-storey, moment-resistant steel frame building. The structures are located within 2 km of each other and, as may be confirmed by visual inspection of the recorded seismograms, experienced similar ground motions. One-dimensional and three-dimensional linear structural models are fitted to the observations using the modal minimization method' for structural identification, in order to determine optimal estimates of the parameters of the dominant modes of the buildings. The time-varying character of these parameters over the duration of the response is also investigated. Comparison of the recorded earthquake response of the structures reveals that the type of lateral-load-resisting system has an important effect on the dynamic behaviour of the structures because it controls the spacing of the characteristic modes on the frequency axis. The Santa Clara County Office building has closely spaced natural frequencies and exhibits strong torsional response and modal coupling. Its dynamic behaviour is contrasted with that of the Great Western Savings and Loan building which has well separated natural frequencies and exhibits small torsional response and no modal coupling. Strong modal coupling causes a beating-type phenomenon and makes earthquake response of structures different from that envisioned by codes.     

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。     

混凝土框架模型结构参数的识别

框架结构是常见的建筑结构形式,也是结构损伤诊断的主要研究对象之一。本文以钢筋混凝土框架结构模型的振动测试数据为基础,采用灵敏度分析方法,对结构的物理参数进行识别。有限元分析中,考虑框架结构的节点转动,采用静凝聚方法得到结构刚度矩阵。参数识别结果表明,对于不同的固有频率和振型的测试信息组合所识别的物理参数有所不同。根据已知的概率分布,利用MonteCarlo方法,将模态参数的不确定性传递给物理参数,得到了物理参数的不确定性。     

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.

     

Amplitude dependence of equivalent modal parameters in monitored buildings during earthquake swarms

This paper investigates the dynamic response of three sample buildings belonging to the Seismic Observatory for Structures, the Italian network for the permanent seismic monitoring of strategic structures, managed by the Italian Department of Civil Protection. The case studies cover different building types that could loosely represent the Italian building stock, with a special emphasis on cultural heritage and masonry structures. Observed under a low‐intensity seismic swarm comprising about 30 aftershocks after a main event, the three buildings are analysed through an input–output, model‐driven linear dynamic identification procedure, depicting the relation between the shaking level at the site and the variation of the equivalent structural modal parameters, while keeping into account the effects of soil–structure interaction. Finite element models will be used to investigate one of the case studies and to compare the law of variation of the structural modal parameters with respect to simplified models proposed by technical standards. Copyright © 2017 John Wiley & Sons, Ltd.     

A system identification approach to the detection of changes in both linear and non-linear structural parameters

This paper explores the potential of a new time domain identification procedure to detect changes in structural dynamic characteristics on the basis of measurements. This procedure is verified using mathematical models simulated on the computer. The experiments involve two eight-storey steel structures with and without energy devices, and a 47-storey building at San Francisco during the Loma Prieta earthquake. The recursive instrumental variable method and extended Kalman filter algorithm are used as identification algorithms. An exploratory investigation is made of the usefulness of various indices, such as mode shape and storey drift, that can be extracted accurately from identification to quantify changes in the characteristics of the physical system. It is concluded that the change of storey drift is the key information to the detection of changes in structural parameters, from which the proposed system identification algorithm can be applied with an appropriate inelastic model to simulate the dynamic behaviour of real structures undergoing strong ground motion excitations.     

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.     

Predicting torsion‐induced lateral displacements for pushover analysis: Influence of torsional system characteristics

The increasing popularity of simplified nonlinear methods in seismic design has recently led to many proposals for procedures aimed at extending pushover analysis to plan asymmetric structures. In terms of practical applications, one particularly promising approach is based on combining pushover analysis of a 3D structural model with the results of linear (modal) dynamic analysis. The effectiveness of such procedure, however, is contingent on one fundamental requirement: the elastic prediction of the envelope of lateral displacements must be conservative with respect to the actual inelastic one. This paper aims at verifying the above assumption through an extensive parametric analysis conducted with simplified single‐storey models. The main structural parameters influencing torsional response in the elastic and inelastic range of behaviour are varied, while devoting special attention to the system stiffness eccentricity and radius. The analysis clarifies the main features of inelastic torsional response of different types of building structures; in this manner, it is found that the above‐mentioned method is generally suitable for structures characterized by moderate to large torsional stiffness, whereas it cannot be recommended for extremely torsionally stiff structures, as their inelastic torsional response almost always exceeds the elastic one. Copyright © 2010 John Wiley & Sons, Ltd.     

基于BP神经网络的网格结构损伤识别与试验研究

针对网格结构中杆件数量众多,但节点数总是远远小于杆件数的特点,损伤识别中采用了基于BP神经网络技术和面向节点的损伤初步定位方法的网格结构损伤识别的三步法。对双层柱面网壳结构模型在不同杆件去掉时的四种损伤情况下的振动特性进行了实测,并以实测低阶模态的频率变化率和少数测点的振型分量作为神经网络输入参数,对模型的各种损伤情况进行了识别。结果表明,所用的方法可以精简神经网络的结构,并提高其模式识别的能力。该方法可用于对大型复杂结构的损伤识别。     

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。