Browsing by Author "Xie, Yazhou"

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    Enabling efficient regional seismic fragility assessment of multi-component bridge portfolios through Gaussian process regression and active learning
    (Wiley, 2024) Ning, Chunxiao; Xie, Yazhou; Burton, Henry; Padgett, Jamie E.
    Regional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge-specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high-fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis-based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge-model-ground-motion sample pairs to conduct NRHA with much-improved efficiency. Both independent and correlated multi-output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL-GPR framework and the associated component-level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL-selected samples would enable the GPR to derive bridge-specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL-GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi-output independent GPR, a stable and reliable fragility model can be achieved using 50 AL-selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi-component bridge portfolios.
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    Seismic fragilities of single‐column highway bridges with rocking column‐footing
    (Wiley, 2019) Xie, Yazhou; Zhang, Jian; DesRoches, Reginald; Padgett, Jamie E.
    Rocking isolation has been increasingly studied as a promising design concept to limit the earthquake damage of civil structures. Despite the difficulties and uncertainties of predicting the rocking response under individual earthquake excitations (due to negative rotational stiffness and complex impact energy loss), in a statistical sense, the seismic performance of rocking structures has been shown to be generally consistent with the experimental outcomes. To this end, this study assesses, in a probabilistic manner, the effectiveness of using rocking isolation as a retrofit strategy for single‐column concrete box‐girder highway bridges in California. Under earthquake excitation, the rocking bridge could experience multi‐class responses (eg, full contacted or uplifting foundation) and multi‐mode damage (eg, overturning, uplift impact, and column nonlinearity). A multi‐step machine learning framework is developed to estimate the damage probability associated with each damage scenario. The framework consists of the dimensionally consistent generalized linear model for regression of seismic demand, the logistic regression for classification of distinct response classes, and the stepwise regression for feature selection of significant ground motion and structural parameters. Fragility curves are derived to predict the response class probabilities of rocking uplift and overturning, and the conditional damage probabilities such as column vibrational damage and rocking uplift impact damage. The fragility estimates of rocking bridges are compared with those for as‐built bridges, indicating that rocking isolation is capable of reducing column damage potential. Additionally, there exists an optimal slenderness angle range that enables the studied bridges to experience much lower overturning tendencies and significantly reduced column damage probabilities at the same time.
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    Sensitivity of seismic demands and fragility estimates of a typical California highway bridge to uncertainties in its soil-structure interaction modeling
    (Elsevier, 2019) Xie, Yazhou; DesRoches, Reginald
    This study investigates the sensitivity of seismic demands and fragility estimates of a typical highway bridge in California to variation in its soil-structure interaction (SSI) modeling parameters. A rigorous p-y spring based modeling approach is developed and validated for an instrumented highway overcrossing that provides a dependable screening of each modeling parameter. Modifications are made to benchmark the overcrossing against typical bridge designs in California, including the consideration of diaphragm and seat abutments. Plausible variation in SSI modeling parameters is established using 18 random variables that cover different soil zones. Influential SSI parameters are identified for the seismic demands of bridge components through two regression techniques such as stepwise and LASSO regressions. Concurring results from both regressions indicate that bridge demand models tend to be sensitive to the modeling parameters associated with near-ground soils. Furthermore, the relative importance of the uncertainty in SSI modeling parameters is assessed with respect to the fragility estimates in both component and system levels. The study reveals that the bridge performance and fragility curves of bridge columns and decks are dominated by the uncertainty in the ground motion. However, the propagation of the potentially variable SSI parameters plays a significant role in the fragility estimates of bridge foundations and abutment components such as span unseating, bearings and shear keys. The results offer insights to guide future uncertainty treatment in SSI modeling and investment in refined soil parameter estimates through field testing or other measures.