通过高斯过程回归和主动学习对多成分桥梁组合进行高效的区域地震脆性评估

IF 4.3 2区 工程技术 Q1 ENGINEERING, CIVIL Earthquake Engineering & Structural Dynamics Pub Date : 2024-05-11 DOI:10.1002/eqe.4144
Chunxiao Ning, Yazhou Xie, Henry Burton, Jamie E. Padgett
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引用次数: 0

摘要

桥梁组合的区域地震脆性评估必须解决地震灾害和桥梁属性(如几何形状、材料、设计细节)带来的不确定性和变化。为实现针对特定桥梁的脆性评估,最近开发了多变量概率地震需求模型 (PSDM),将地震动烈度测量值和桥梁参数作为输入。然而,明确使用桥梁参数作为输入需要进行大量的非线性响应历史分析(NRHA)。在这种情况下,对于具有复杂部件连接性和复杂材料构成规律的高保真桥梁模型,相关计算成本会呈指数级增长。此外,目前仍不清楚需要进行多少次分析才能使响应数据和由此产生的需求模型覆盖整个求解空间而不会过度拟合。为了解决这些问题,本研究将高斯过程回归(GPR)和主动学习(AL)整合到一个多步骤工作流程中,以实现对桥梁组合进行高效的区域地震脆性评估。高斯过程回归放宽了典型的基于云分析的 PSDM 中的概率分布假设,从而实现了异方差非参数地震需求建模。AL 利用不同的标准偏差来选择最少但最具代表性的桥梁-模型-地动样本对,从而大大提高了 NRHA 的效率。提出了独立和相关的多输出 GPR,以处理多个组件(支柱、支座、剪力键、桥台、非密封和接缝密封)之间存在地震需求相关性的桥梁组合。以加利福尼亚州的一座基准公路桥为例,研究了 AL-GPR 框架和相关构件级脆性结果的效率、准确性和稳健性。脆性结果表明,70 个 AL 选样可使 GPR 得出特定桥梁的脆性模型,与使用多条纹分析方法(每座桥梁考虑 1950 次地面运动)得出的模型相当。AL-GPR 模型还成功地捕捉到了桥梁跨度长度、桥面面积、柱子细长度和钢筋比例将如何改变不同桥梁部件损坏状态超限概率的物理现象。AL 的高效性源于以下事实:与至少 270 个随机选择的样本相比,利用多输出独立 GPR,只需 50 个 AL 选择的样本就能获得稳定可靠的脆性模型。所提出的方法推动了对多组件桥梁组合进行更高效、更可靠的区域地震脆性评估的技术发展。
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Enabling efficient regional seismic fragility assessment of multi-component bridge portfolios through Gaussian process regression and active learning

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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来源期刊
Earthquake Engineering & Structural Dynamics
Earthquake Engineering & Structural Dynamics 工程技术-工程:地质
CiteScore
7.20
自引率
13.30%
发文量
180
审稿时长
4.8 months
期刊介绍: Earthquake Engineering and Structural Dynamics provides a forum for the publication of papers on several aspects of engineering related to earthquakes. The problems in this field, and their solutions, are international in character and require knowledge of several traditional disciplines; the Journal will reflect this. Papers that may be relevant but do not emphasize earthquake engineering and related structural dynamics are not suitable for the Journal. Relevant topics include the following: ground motions for analysis and design geotechnical earthquake engineering probabilistic and deterministic methods of dynamic analysis experimental behaviour of structures seismic protective systems system identification risk assessment seismic code requirements methods for earthquake-resistant design and retrofit of structures.
期刊最新文献
Issue information Issue information SSI-induced seismic earth pressures on an integral abutment bridge model: Experimental measurements versus numerical simulations and code provisions Estimation of inelastic displacement ratio spectrum for existing RC structures via displacement response spectrum Linear equivalence for motion amplification devices in earthquake engineering
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