DSpace Repository :: Browsing by Author "Astroza, Rodrigo"

Browsing by Author "Astroza, Rodrigo"

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(33, 33) Aldea, Sofía; Bazáez, Ramiro; Heresi, Pablo; Astroza, Rodrigo; 33
Bridges are one of the most critical and costly structures on road networks. Thus, their integrity and operation must be preserved to prevent safety concerns and connectivity losses after seismic events. Recent large-magnitude earthquakes have revealed a series of vulnerabilities in multi-span highway bridges. In particular, skewed bridges have been severely damaged due to their susceptibility to developing excessive in-plane deck rotations and span unseating. Although seismic design codes have been updated to prescribe larger seating lengths and have incorporated unseating prevention devices, such as shear keys and cable restrainers, research on the seismic performance of skewed bridges with passive energy-dissipation devices is still limited. Therefore, this study focuses on assessing the effectiveness of implementing hysteretic dampers on skewed bridges. With that aim, dampers with and without recentering capabilities are designed and incorporated in representative Chilean skewed bridges to assess their contribution to seismic performance. Three-dimensional nonlinear finite element models, multiple-stripe analysis, and fragility curves are utilized to achieve this objective. The results show that incorporating bidirectional dampers can effectively improve the seismic performance of skewed bridges at different hazard levels by limiting in-plane deck rotations independently of their skew angle. Additionally, the influence of external shear keys and damper hysteretic behavior is analyzed, showing that these parameters have a low influence on bridge performance when bidirectional dampers are incorporated.
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(Springer New York LLC, 33) Astroza, Rodrigo; Hernández, Francisco; Díaz, Pablo; Gutierrez, Gonzalo; 33
A full scale five-story reinforced concrete building was built and tested on the Large High Performance Outdoor Shake Table (LHPOST) at the University of California, San Diego in 2012. The main objective of the test program was to study the seismic response of the structure and the nonstructural components (NSCs) and their dynamic interaction at different levels of seismic excitation. The building specimen was first tested base-isolated and then fixed at its base. In the fixed-base configuration, a suite of six earthquake motions of various intensities was applied to the building to progressively increase the seismic demand. In this paper, the modal parameters of the fixed-base building are identified using the input-output dynamic data recorded during the seismic tests. The deterministic-stochastic subspace identification method (DSI) is employed to estimate the variations of the modal properties of the building during the seismic tests by employing a short-time windowing approach. The changes of the modal parameters during the seismic motions are tracked, analyzed, and compared to those previously obtained from ambient vibrations and low-amplitude white noise base excitation tests. The identified natural frequencies and equivalent damping ratios of the building changed with the intensity of the input motions and damage in the structure, while the mode shapes are found to be insensitive to them.
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(33, 33) Birrell, Matías; Li, Yong; Astroza, Rodrigo; 33
A key aspect of performance assessment of structures is the quantification and propagation of uncertainties, from the estimation of hazards to possible losses. In particular, probabilistic structural analysis deals with aleatory and epistemic sources of uncertainty in nonlinear modeling. Materials and components in structural models are represented by uncertain parameters, which can be accounted for via probabilistic constitutive models. The variability at a local level is then propagated to the system level when the structural model is sampled, sometimes inducing great uncertainty in structural demands. However, probabilistic modeling of real structures via finite element (FE) models has been a challenge due to high computational costs. One avenue to reduce this cost and make probabilistic modeling viable in practice is to develop cost-effective surrogate models. In this paper, a Gaussian Process (GP) approach is proposed to study the composition of parameter-induced uncertainty in mechanics-based nonlinear FE structural model responses. The methodology is validated by evaluating common regression error metrics between the original FE models and their GP surrogates. Case studies of two structures are presented, including a five-story reinforced concrete (RC) building and a five-span RC highway bridge. Finally, the low computational cost of the surrogate models is leveraged to perform simulation-based global sensitivity analysis using Sobol indices to quantify parameter-induced uncertainty in structural responses.
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(33, 33) Pinto, F. J.; Ledezma, C.; Astroza, Rodrigo; Abell , José Antonio; 33
The modeling of structural damping due to the excitation of elastic-waves into the surrounding soil domain, and its effect on structural response as apparent modal damping is explored herein. Four high-fidelity, linear finite-element models of building-site systems, with 20–50 storeys and 2–7 basement levels, are simulated in OpenSees to evaluate their frequency response. Radiation-damping is provided by a layer of high-damping elements, whose design is explored in detail. Results show that up to 1% of apparent, low-amplitude damping can be attributed to radiation-damping depending on number of stories and depth of embedment.
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(33, 33) Murcia-Delso, Juan; Birrell, Matías; Astroza, Rodrigo; Carrillo, Julian; 33
A unified analytical model to compute the slip response and maximum tensile stress capacity of both steel bar anchorages and lap splices is presented. The model assumes idealized bond stress distributions along the anchorage and lap-splice lengths, from which tensile stress-slip relations are derived to characterize their deformation response. A simple bond failure criterion is also established to compute the tensile strength of the anchorage or lap splice, considering potential pullout and splitting failures. The ability of the proposed model to determine the force-deformation response of anchorages and lap splices is verified using experimental data from pullout tests and uniaxial tests on lap splices reported in the literature, and its accuracy for predicting tensile strength is evaluated with results from 457 tests of the ACI 408 database on lap splices. The calibration of the model is refined using a Bayesian estimation framework, which compares analytical results obtained using randomly generated samples from estimated parameter distributions to experimental data from the lap-splice database. The resulting mean value of the experimental-to-analytical strength ratios for the lap-splice tests is practically equal to 1 and the coefficient of variation is 0.20. Based on the results of Bayesian estimation, probabilistic distributions are also proposed to quantify the uncertainty of key model parameters.
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(33, 33) Birrell, Matías; Astroza, Rodrigo; Murcia-Delso, Juan; Hernández, Francisco; Bazáez, Ramiro; 33
This paper proposes a simplified tri-linear model for the lateral force-displacement relationship of monolithic exterior shear keys in reinforced concrete bridges, failing in sliding shear. To allow for implementation compatibility with a widely used simplified model for shear keys failing in diagonal tension (DT), this model is presented in a tri-linear formulation that builds on past experimental data and expressions for the capacity of shear keys. Capacity points are adapted from previously published work on sliding shear (SS) and sliding friction (SF) failing shear keys, and displacement points are devised around available experimental data and numerical studies. These consider the effective stiffness of concrete members, dowel action in vertical steel reinforcement, and empirical observations from monolithic shear key tests. Model parameters are probabilistically characterized in a Bayesian parameter estimation framework, to incorporate experimental data. The calibrated expressions are then validated by comparing sampled predictions to experimental observations, and finally a single distribution for model parameters is suggested. The goal of this model is to provide a simplified formulation to be implemented alongside DT failure in time-history response simulations of structure-level finite element models of bridges, thus covering both major failure mechanisms in monolithic reinforced concrete shear keys.
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(33, 33) Bravo, Tomas P.; Gattas, Joseph M.; Bravo, Felipe; Astroza, Rodrigo; Maluk, Cristian; 33
Hybrid timber composites are becoming increasingly widespread to efficiently enhance timber performance. While carbon fibre reinforcement has been explored for strength in timber structures, its impact on vibration performance remains understudied and is crucial as mass timber buildings extend their spans and heights. This work explores the effect of carbon fibre reinforcement on the vibration performance parameters of CLT floor systems. Natural frequency, mode shape, damping and stiffness parameters are evaluated from experimental testing using the Natural Excitation Technique (NExT) combined with the Eigensystem Realization Algorithm (ERA) and robust techniques for selection of physical dynamic properties, verified against numerical finite element models. The findings reveal that, in the case of three-ply Cross-laminated Timber (CLT) panels with depths ranging from 115 mm to 135 mm, introducing reinforcement at ratios below 0.67% led to an enhancement in frequencies, registering an increase of 6% to 11%, although not explicitly accounting for changes in environmental conditions. Moreover, these reinforced panels showed a relevant increase in effective flexural stiffness, ranging from 15% to 22%, when compared to their unreinforced counterparts. This means an improved maximum span of around 4%–6% within current design limits for timber floors influenced by vibration performance. These findings enhance the understanding and design of high-performance composite timber floors, promoting the use of renewable building materials in construction.
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(33, 33) Guajardo, Benjamín; Pinto, Francisco; Astroza, Rodrigo; 33
Soil exhibits inherent spatial variability, creating a significant source of uncertainty in geotechnical assessments. This variability becomes particularly critical when evaluating the seismic performance of infrastructure such as multi-span highway bridges, since traditional methodologies in bridge design often oversimplify soil properties by assuming uniformity. This approach, however, may lead to considerable inaccuracies in determining structural response under seismic activity. The complexity of soil–structure interaction (SSI) in such multi-span structures further exacerbates the influence of soil spatial variability on the overall structural response to seismic events. Although numerous studies have explored the impact of spatial variation in ground motions on seismic performance, a noticeable gap exists in the literature addressing soil spatial variability in the SSI modeling and its impact in the seismic response of multi-span bridges. Accordingly, this research aims to address this gap by proposing a numerical framework that integrates the inherent spatial variability of soil in SSI modeling by means of random fields theory and 3D nonlinear dynamic finite element models into the seismic performance analysis of multi-span bridges. The findings from a case study reveals a significant influence of soil spatial variability on structural response, leading to discrepancies in vulnerability assessment between different bridge components and highlighting the importance of incorporating spatial variability in soil parameters into seismic assessments of bridges. Moreover, soil variability appeared to slightly impact system-level vulnerability. Although the main conclusions are developed from a case study and are applicable to bridges with similar characteristics and seismic demand, the proposed approach can readily be applied to other bridge configurations and seismic environments.
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(Springer, 33) Song, Mingming; Astroza, Rodrigo; Ebrahimian, Hamed; Moaveni, Babak; Papadimitriou, Costas; 33
This paper studies the performance of recursive and batch Bayesian methods for nonlinear model updating. Unscented Kalman filter (UKF) is selected to represent the recursive Bayesian method, and two UKF approaches are investigated and compared, i.e., non-adaptive UKF and adaptive UKF. The proposed new adaptive filter, forgetting factor adaptive UKF, estimates the model parameters and measurement noise covariance in an online manner. The forgetting factor adaptive UKF is based on the principle of matching the covariance of residuals to its theoretical values by updating the measurement noise covariance. The performance of non-adaptive UKF, adaptive UKF and batch Bayesian method are investigated when applied to a numerical nonlinear 3-story 3-bay steel frame structure for parameter estimation of material properties. Different types of modeling errors are considered in the 21 updating models to study the effects of modeling errors on model updating. It is found that adaptive UKF approach provides the most accurate parameter estimations, while batch Bayesian approach gives the smallest errors on response predictions.
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(33, 33) Fayaz, Jawad; Astroza, Rodrigo; Ruiz, Sergio; 33
In the face of the unrelenting challenge posed by earthquakes—a natural hazard of unpredictable nature with a legacy of significant loss of life, destruction of infrastructure, and profound economic and social impacts—the scientific community has pursued advancements in earthquake early warning systems (EEWSs). These systems are vital for pre-emptive actions and decision-making that can save lives and safeguard critical infrastructure. This study proposes and validates a domain-informed deep learning-based EEWS called the hybrid earthquake early warning framework for estimating response spectra (HEWFERS), which represents a significant leap forward in the capabilities to predict ground shaking intensity in real-time, aligning with the United Nations’ disaster risk reduction goals. HEWFERS ingeniously integrates a domain-informed variational autoencoder for physics-based latent variable (LV) extraction, a feed-forward neural network for on-site prediction, and Gaussian process regression for spatial prediction. Adopting explainable artificial intelligence-based Shapley explanations further elucidates the predictive mechanisms, ensuring stakeholder-informed decisions. By conducting an extensive analysis of the proposed framework under a large database of approximately 14 000 recorded ground motions, this study offers insights into the potential of integrating machine learning with seismology to revolutionize earthquake preparedness and response, thus paving the way for a safer and more resilient future.
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(Springer, 33) Hurtado, Oscar D.; Ortíz, Albert R.; Gómez, Daniel; Astroza, Rodrigo; 33
Simplifications and theoretical assumptions are often incorporated into numerical modeling of structures; however, these assumptions may reduce the accuracy of simulation results. Model updating techniques have been developed to minimize the error between experimental response and modeled structures by updating their parameters based on observed data. Structural numerical models are typically constructed using a deterministic approach, obtaining a single best-estimated value for each structural parameter. However, structural models are often complex and involve many uncertain variables, making it impossible to find a unique solution that captures all the variability. Updating techniques using Bayesian inference (BI) have been developed to quantify parametric uncertainty in analytical models. This chapter presents the implementation of BI in the parametric updating of a five-story building model and the quantification of associated uncertainty. The Bayesian framework is implemented to update the model parameters based on experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is considering the uncertainty in the experimental data, leading to a better representation of the actual building behavior. Additionally, the implications of Bayesian modeling are discussed, highlighting the importance and implications of using a multivariate normal likelihood function in the analysis. The results show that this Bayesian model updating approach effectively allows for a statistically rigorous update of model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions. This is particularly useful in engineering applications where model accuracy is critical.
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(33, 33) Pinto, Francisco J.; Toledo, José; Birrell, Matías; Bazáez, Ramiro; Hernández, Francisco; Astroza, Rodrigo; 33
Bridges are essential structures in the logistic chain of countries, making it critical to design them to be as resilient as possible. One way to achieve this is through performance-based seismic design (PBSD), which involves using nonlinear Finite Element (FE) models to predict the response and potential damage of different structural components under earthquake excitations. Nonlinear FE models need accurate constitutive models of material and components. Among them, seismic bars and laminated elastomeric bearings play an important role in a bridge’s response to earthquakes; therefore, properly validated and calibrated models should be proposed. Only default parameter values from the early development of the constitutive models widely used by researchers and practitioners for these components tend to be used, and low identifiability of its governing parameters and the high cost of generating reliable experimental data have prevented a thorough probabilistic characterization of their model parameters. To address this issue, this study implements a Bayesian probabilistic framework using Sequential Monte Carlo (SMC) for updating the parameters of constitutive models of seismic bars and elastomeric bearings and proposes joint probability density functions (PDF) for the most influential parameters. The framework is based on actual data from comprehensive experimental campaigns. The PDFs are obtained from independent tests conducted on different seismic bars and elastomeric bearings, to then consolidate all the information in a single PDF for each modeling parameter by means of the conflation methodology, where the mean, coefficient of variation, and correlation between calibrated parameters are obtained for each bridge component. Finally, findings show that the incorporation of model parameter uncertainty through a probabilistic framework will allow for a more accurate prediction of the response of bridges under strong earthquakes.
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(Springer New York LLC, 33) Ramancha, Mukesh Kumar; Madarshahian, Ramin; Astroza, Rodrigo; Conte, Joel P.; 33
Bayesian nonlinear finite element (FE) model updating using input and output measurements have emerged as a powerful technique for structural health monitoring (SHM), and damage diagnosis and prognosis of complex civil engineering systems. The Bayesian approach to model updating is attractive because it provides a rigorous framework to account for and quantify modeling and parameter uncertainty. This paper employs the unscented Kalman filter (UKF), an advanced nonlinear Bayesian filtering method, to update, using noisy input and output measurement data, a nonlinear FE model governed by a multiaxial material constitutive law. Compared to uniaxial material constitutive models, multiaxial models are typically characterized by a larger number of material parameters, thus requiring parameter estimation to be performed in a higher dimensional space. In this work, the UKF is applied to a plane strain FE model of Pine Flat dam (a concrete gravity dam on King’s River near Fresno, California) to update the time-invariant material parameters of the cap plasticity model, a three-dimensional non-smooth multi-surface plasticity concrete model, used to represent plain concrete behavior. This study considers seismic input excitation and utilizes numerically simulated measurement response data. Estimates of the multi-axial material model parameters (for the single material model used in this study) are non-unique. All sets of parameter estimates yield very similar and accurate seismic response predictions of both measured and unmeasured response quantities.
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(33, 33) Ortiz, Fabián; Pastén, César; Bustos, José; Ruiz, Sergio; Astroza, Rodrigo; Easton, Gabriel; 33
Three-dimensional physics-based numerical simulations (3D-PBS) of the seismic response of the Santiago Basin, Chile, were performed considering a large-scale velocity model and shallow crustal earthquake scenarios, associated with the west-verging thrust San Ramón Fault. Numerical results show that competent gravelly soils in the center of the basin respond with low seismic amplification and shorter durations of strong ground motions, unlike less competent fine-grained soils in the northern area. A significant increase in the seismic intensities is observed in the vicinity of rock outcrops, attributable to the generation of surface waves. Seismic amplification factors were calculated with respect to a reference site on gravel and their values show high levels of amplification in the vicinity of the seismic source, and on soils with low shear wave velocities (V_s) and long fundamental vibration periods. On the other hand, empirical ground motion models (GMM) were used to estimate amplification factors for peak ground accelerations and spectral accelerations at various periods. Results from GMMs and 3D-PBS were compared, showing similarities in the attenuation pattern on stiff soils, but differences in soils with low V_s. Moreover, 3D-PBS captured site effects associated with the local geomorphology, unlike GMMs.
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(Springer New York LLC, 33) Astroza, Rodrigo; Alessandri, Andres; Conte, Joel P.; 33
A novel approach to deal with modeling uncertainty when updating mechanics-based finite element (FE) models is presented. In this method, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to seismic base excitation is employed to illustrate and validate the proposed methodology. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature.
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(33, 33) Liu, Zhenning; Abtahi, Shaghayegh; Astroza, Rodrigo; Li, Yong; 33
Nonlinear finite-element model updating (FEMU) is a promising approach for post-event damage assessment of civil structures. This paper conducted FEMU for a full-scale reinforced concrete bridge column tested under a sequence of earthquakes and examined the evolution of seismic damage across different earthquake ground motions. It was found that using experimental data, FEMU can be applied to identify unknown key model parameters (e.g. bond-slip and core concrete parameters) and damage of the columns as represented by the change of the key parameters. In addition, the updated models demonstrated their ability to better predict the system response for future earthquakes.
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(33, 33) Chen, Yuxiang; Castiglione, Juan; Astroza, Rodrigo; Li, Yong; 33
Accurate and computationally efficient building energy models are critical to the development of online or pseudo-online control strategies and other building management activities. However, such models need to overcome the large uncertainty involved with continuously changing occupant activities and building status. The present study uses unscented Kalman filtering (UKF) in the model parameter estimation for simple yet accurate resistor-capacitor (RC) models to develop reliable building energy models. The estimation procedure, mathematical operations, and other estimation enhancing techniques are presented in detail. Synthetic and measured data were used to validate and evaluate the methodology. The obtained model shows better performance when compared with a model that was calibrated using genetic algorithms in a previous study. This remarkable model performance shows that UKF can enable timely online model update and improve the model predictability.
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(33, 33) Arellano, Benjamín; Hernández, Francisco; Massone, Leonardo M.; Astroza, Rodrigo; Soto, Pedro; Contreras, Nicolás; Garrido, Bastián; 33
A novel system identification technique called Mod-? (var) is formulated, implemented, and validated to study the seismic response of a 3D R/C shear wall building during the 2010 central Chilean mega-earthquake (Mw = 8.8). The Mod-?(var) approach is an evolution of Least-Square modal system identification techniques, where modal parameters are adjusted through small data windows to fit seismic data in the frequency and time domains. This technique offers several advantages over traditional methods, including the estimation of time-variant dynamic properties during seismic events and the reliable assessment of continuous nonlinear modal responses. As a result, the Mod-?(var) approach allows for determining empirical response spectra related to each seismic input. The technique can also compute the local response of measured and unmeasured floors as the product between the nonlinear modal responses (obtained from the Mod-?(var) approach) and the normalized seismic mode shapes for all building floors, which can be estimated from ambient vibration data. Finally, the seismic floor deformations can be imposed on a FEM to determine relevant engineering quantities such as inter-story drift, inter-story forces, and local demand of structural elements (e.g., drift ratios, curvatures, internal forces, stresses, strains, etc.).
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(Springer Science and Business Media Deutschland GmbH, 33) Mizon, Felipe; Birrell, Matías; Abell, José; Astroza, Rodrigo; 33
This paper presents a method to update linear time-variant finite element (FE) models of civil structures experiencing nonlinear behavior due to earthquake excitation. First, the time-varying modal parameters of the structure are identified using the input–output dynamic data recorded during a damaging seismic event by employing a short-time windowing approach. Then, the identified modal parameters are used to update a linear FE model of the structure using a Bayesian approach. Global sensitivity analysis based on Sobol’ indices is employed to select the most influential parameters for the model updating stage. The evolution of the equivalent stiffness of different elements of the FE model are tracked and their estimation uncertainties are also quantified. The method is verified using numerically simulated data of a two-dimensional nonlinear FE model of a nine-story steel frame.
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(33, 33) Sun, Lin; Conte, Joel P.; Todd, Michael D.; Astroza, Rodrigo; Bock, Yehuda; Offield, Glen; Vernon, Frank; 33
This paper focuses on system identification (SID) of the UC San Diego Geisel Library building using ambient vibration (AV) data, assuming that the building’s behavior can be fully described by linear models in terms of material, geometry, damping, etc. Three state-space-based, output-only time domain SID methods are applied and fully automated to identify the library’s modal properties using AV data from both a 15-day and a 486-day monitoring period. The modes identified from the AV data are higher-order coupled torsional-flexural modes. The identified modal properties are influenced by the atmospheric conditions, and the amplitude of the building’s ambient vibration. The time-varying identified modal properties show a cyclical 1-day pattern due to human activity, earth tremors, and short-term changes in atmospheric conditions such as wind speed and temperature. One of the output-only SID methods was used to estimate modal properties from ambient vibration data recorded continuously over a 486-day period, including three low-intensity earthquakes. No permanent changes in the identified modal properties were observed due to the three low-intensity earthquakes that occurred during that period. Renovations of the Geisel Library involving only non-structural components (e.g., non-load-bearing partition walls, changes in space allocations, and inertial/live loads) caused some discontinuities in the identification of the modes of interest in this study. The influence of the data window length on system identification results, in terms of identification success rate and estimation uncertainty, was investigated. The identified state-space models are also used to assess the relative contribution of the ambient base excitation to the building’s total ambient vibrational response. This research offered a unique opportunity to study linear SID of a large and complex real-world structure under ambient excitations, and the effects of changing environmental conditions on the identified modal properties. It provided insight into some of the causes of the observed temporal variation in the identified modal properties. The SID results presented in this study also provide a baseline for future structural health monitoring studies of the Geisel Library building.