A framework for the generation of bridge-specific fragility utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and expensive re-simulation. The methodology does not place any assumptions on the demand model of various components and helps to identify the relative importance of each uncertain variable in their seismic demand model. The methodology is demonstrated through the case studies of multi-span concrete bridges in California. Geometric, material and structural uncertainties are accounted for in the generation of bridge models and fragility curves. It is also noted that the traditional lognormality assumption on the demand model leads to unrealistic fragility estimates. Fragility results obtained the proposed methodology curves can be deployed in risk assessment platform such as HAZUS for regional loss estimation.

中文翻译：

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使用机器学习技术对多跨混凝土桥梁类进行基于条纹的脆性分析

本文提出了一种利用机器学习和基于条带的方法的能力生成桥梁特定脆弱性的框架。所提出的使用随机森林的方法有助于以较少的计算工作和昂贵的重新模拟为一组新的输入参数生成或更新脆弱性曲线。该方法不对各种组件的需求模型进行任何假设，并有助于确定每个不确定变量在其地震需求模型中的相对重要性。该方法通过加利福尼亚多跨混凝土桥梁的案例研究得到证明。在桥梁模型和易损性曲线的生成中考虑了几何、材料和结构的不确定性。还需要注意的是，需求模型上的传统对数正态性假设导致了不切实际的脆弱性估计。所提出的方法曲线获得的脆弱性结果可以部署在风险评估平台如 HAZUS 中进行区域损失估计。