Structural Identification Methodology Research Articles

Indirect identification of bridge frequencies using a four-wheel vehicle: Theory and three-dimensional simulation

Indirect identification of bridge frequencies, which refers to identifying bridge frequencies from the dynamic response of a vehicle moving on the bridge, has the potential to fast inspect bridges in large quantities. Accurate analysis on the dynamic response of the vehicle used for sensing is crucial to the indirect frequency identification. Most published research on this topic, however, is based on a simplified two-dimensional (2D) analysis of vehicle–bridge interaction (VBI), which cannot fully reproduce the mechanism of a real three-dimensional (3D) vehicle. As a complement, this study carries out the 3D simulation of VBI and accordingly proposes a novel frequency-domain method to identify bridge natural frequencies from the vertical acceleration of a full-car model’s wheels. The wheels’ equations of motion are first transferred into the frequency domain, then the frequency responses of the front and rear wheels are subtracted with a time lag to eliminate the adverse effect of road roughness. Also proposed by this study is a new method to identify the speed of the sensing vehicle with the correlation function of wheel acceleration so that the time lag for subtraction can be calculated. In order to investigate the performance of the proposed methods, a series of numerical simulations are conducted, including sensitivity analysis on the vehicle speed, the class of road roughness, the noise level, and the vehicle frequency. Finally, this paper remarks on the accuracy and robustness of the proposed method and challenges faced by the engineering practice of the indirect bridge structural identification methodology.

Development of a structural identification methodology with uncertainty quantification through the SSI and bootstrap techniques

In the last few years, increasing attention has been given to obtaining reliable results in system identification as a key step in performing model updating, damage detection and structural health monitoring. Many methods have been developed to measure the uncertainty in modal estimates. Some techniques are based on signal resampling to obtain a larger sample space and consequently better process statistics. Thus, this study proposes a new method to quantify uncertainties using the stochastic subspace identification (SSI) technique combined with bootstrap resampling. To validate and compare the performance of the proposed algorithm, two algorithms previously mentioned in the literature were implemented. The first is based on the “moving block method”, and the second is based on blocks of the projection matrix. The proposed method initially generates random decrement functions from signal segments and later uses these functions as bootstrap resampling blocks in SSI. To validate and compare the performance of the proposed algorithm, numerical simulations and two experimental analyses are performed. The first analysis involves a simply supported steel beam subjected to vibration tests through random impacts, and the second involves a three-dimensional four-story frame subjected to vibrations from random signals generated by an electrodynamic exciter. The results of the proposed technique were more robust and accurate than those of the other methods.

Field measurements for calibration of simplified models of the stiffening effect of infill masonry walls in high-rise RC framed and shear-wall buildings

As a type of nonstructural component, infill walls play a significant role in the seismic behavior of high-rise buildings. However, the stiffness of the infill wall is generally either ignored or considered by simplified empirical criteria that lead to a period shortening. The difference can be greatly decreased by using a structural identification methodology. In this study, an ambient vibration test was performed on four on-site reinforced concrete high-rise buildings, and the design results were compared with the PKPM models using corresponding finite element (FE) models. A diagonal strut model was used to simulate the behavior of the infill wall, and the identified modal parameters measured from the on-site test were employed to calibrate the parameters of the diagonal strut in the FE models. The SAP2000 models with calibrated elastic modulus were used to evaluate the seismic response in the elastic state. Based on the load-displacement relationship of the infill wall, nonlinear dynamic analysis models were built in PERFORM-3D and calibrated using the measured modal periods. The analysis results revealed that the structural performance under small/large earthquake records were both strengthened by infill walls, and the contribution of infill walls should be considered for better accuracy in the design process.

Measurement-based support for post-earthquake assessment of buildings

After a damaging earthquake, assessment of the residual seismic capacity is required for large parts of the building stock. Increased vulnerability of structures together with the threat of immediate aftershocks call for rapid and objective decision making. Structural identification has the potential to reduce parameter-value uncertainties of physics-based models through interpreting measurement data. Significant amounts of uncertainty are associated with the non-linear behaviour of structures during extreme events such as earthquakes. Therefore, a structural identification methodology that accommodates multiple sources of systematic modelling uncertainties is used. Error-domain model falsification (EDMF) enables structural identification through combining damage grades observed by visual inspection with fundamental frequencies that are derived from ambient vibrations. Parametric uncertainties of a hysteretic model are reduced with the two information sources in order to extrapolate the vulnerability of the building regarding future earthquakes. The applicability of the methodology is shown using measurements made on a mixed reinforced-concrete unreinforced-masonry building tested on a shaking table. Based on nonlinear time-history analyses involving single-degree-of-freedom models, EDMF leads to more precise, yet robust, vulnerability predictions of earthquake-damaged buildings when compared with prediction ranges that are obtained without data interpretation.

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Accurate measurement-data interpretation leads to increased understanding of structural behaviour and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an $L_\infty$-norm-based Gaussian likelihood function) and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are compared. In the modified Bayesian model updating methodology, a correction is used in the likelihood function to account for the effect of a finite number of measurements on posterior probability-density functions. The application of these data-interpretation methodologies for condition assessment and fatigue-life prediction is illustrated on a highway steel-concrete composite bridge having four spans with a total length of 219m. A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time-stress response at a fatigue critical location along the bridge span. The time stress-response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology. It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue-life compared with residual minimization and traditional Bayesian model updating in the presence of correlated non-Gaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practising engineers, thus enabling incremental asset-management decision making over long service lives. Finally, parallel implementations of EDMF using grid sampling have lower computations times than implementations using adaptive sampling.

Data-Interpretation Methodologies for Non-Linear Earthquake Response Predictions of Damaged Structures

Seismic exposure of buildings presents difficult engineering challenges. The principles of seismic design involve structures that sustain damage and still protect inhabitants. Precise and accurate knowledge of the residual capacity of damaged structures is essential for informed decision-making regarding clearance for occupancy after major seismic events. Unless structures are permanently monitored, modal properties derived from ambient vibrations are most likely the only source of measurement data that is available. However, such measurement data is linearly elastic and limited to a low number of vibration modes. Structural identification using hysteretic behavior models that exclusively relies on linear measurement data is a complex inverse engineering task that is further complicated by modeling uncertainty. Three structural identification methodologies that involve probabilistic approaches to data interpretation are compared: error-domain model falsification, Bayesian model updating with traditional assumptions as well as modified Bayesian model updating. While noting the assumptions regarding uncertainty definitions, the accuracy and robustness of identification and subsequent predictions are compared. A case study demonstrates limits on non-linear parameter identification performance and identification of potentially wrong prediction ranges for inappropriate model uncertainty distributions.