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<p>The seismic performance of long-span bridge systems supported by deep pile foundations is affected by multiple sources of uncertainty, particularly those associated with soil conditions and structural properties. This study presents seismic response and fragility analyses of precast segmental pier bridges (PSPBs), comprehensively accounting for uncertainties throughout the entire seismic analysis process, including those related to ground motion propagation through soil layers, soil-structure interaction (SSI), and structural parameters. First, spatially depth-varying seismic motions are synthesized using a simulation method based on site-specific transfer functions to capture soil-related uncertainties. Then, a parameterized finite element model of a representative PSPB incorporating uncertainties in SSI and structural parameters is developed in OpenSees. The effects of soil- and structure-related uncertainties on seismic responses of the studied bridge are systematically evaluated. Sensitivity analyses are performed to identify the relative influence of each uncertainty parameter on peak and residual pier drift ratios. Based on extensive seismic fragility analyses, an artificial neural network (ANN)-based model is developed to efficiently predict fragility curves under the combined influence of these uncertainties. In addition, SHapley Additive exPlanations is employed to identify the key input features affecting the ANN model outputs. Further uncertainty analyses are conducted to assess the individual and combined effects of soil- and structure-related uncertainties on the seismic fragility of the investigated bridge. The numerical results highlight the importance of incorporating both soil and structural uncertainties in seismic performance assessments of the studied bridge within the considered parameter ranges. Neglecting these uncertainties may lead to a significant underestimation of structural responses and fragility. The proposed ANN-based approach provides an efficient and reliable tool for seismic fragility assessment and demonstrates the methodological potential for addressing complex geotechnical-structural uncertainty problems.</p>

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Development of a new seismic performance assessment method for bridges considering uncertainties in the entire process of seismic wave propagation and structural dynamic response: a case study of precast segmental pier bridges

  • 期刊论文

  • 发表日期：

    2026年2月10日

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  • Yu-Cheng Diao,
  • Chao Li,
  • Hong-Nan Li

摘要

The seismic performance of long-span bridge systems supported by deep pile foundations is affected by multiple sources of uncertainty, particularly those associated with soil conditions and structural properties. This study presents seismic response and fragility analyses of precast segmental pier bridges (PSPBs), comprehensively accounting for uncertainties throughout the entire seismic analysis process, including those related to ground motion propagation through soil layers, soil-structure interaction (SSI), and structural parameters. First, spatially depth-varying seismic motions are synthesized using a simulation method based on site-specific transfer functions to capture soil-related uncertainties. Then, a parameterized finite element model of a representative PSPB incorporating uncertainties in SSI and structural parameters is developed in OpenSees. The effects of soil- and structure-related uncertainties on seismic responses of the studied bridge are systematically evaluated. Sensitivity analyses are performed to identify the relative influence of each uncertainty parameter on peak and residual pier drift ratios. Based on extensive seismic fragility analyses, an artificial neural network (ANN)-based model is developed to efficiently predict fragility curves under the combined influence of these uncertainties. In addition, SHapley Additive exPlanations is employed to identify the key input features affecting the ANN model outputs. Further uncertainty analyses are conducted to assess the individual and combined effects of soil- and structure-related uncertainties on the seismic fragility of the investigated bridge. The numerical results highlight the importance of incorporating both soil and structural uncertainties in seismic performance assessments of the studied bridge within the considered parameter ranges. Neglecting these uncertainties may lead to a significant underestimation of structural responses and fragility. The proposed ANN-based approach provides an efficient and reliable tool for seismic fragility assessment and demonstrates the methodological potential for addressing complex geotechnical-structural uncertainty problems.