Browsing by Author "Birrell, Matías"

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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) 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) 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 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.