Abstract Bayesian linear regression (BLR) based demand prediction models are proposed for efficient seismic fragility analysis (SFA) of structures utilizing limited numbers of nonlinear time history analyses results. In doing so, two different BLR models i.e. one based on the classical Bayesian least squares regression and another based on the sparse Bayesian learning using Relevance Vector Machine are explored. The proposed models integrate both the record-to-record variation of seismic motions and uncertainties due to structural model parameters. The magnitude of uncertainty involved in the fragility estimate is represented by providing a confidence bound of the fragility curve. The effectiveness of the proposed BLR models are compared with the commonly used cloud method and the maximum likelihood estimates methods of SFA by considering a nonlinear single-degree-of-freedom system and a five-storey reinforced concrete building frame. It is observed that both the BLR models can estimate fragility with improved accuracy compared to those common analytical SFA approaches considering direct Monte Carlo simulation based fragility results as the benchmark.

中文翻译：

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基于贝叶斯线性回归需求模型的结构抗震易损性分析

摘要 提出了基于贝叶斯线性回归 (BLR) 的需求预测模型，利用有限数量的非线性时程分析结果对结构进行有效的地震脆性分析 (SFA)。为此，探索了两种不同的 BLR 模型，即一种基于经典贝叶斯最小二乘回归，另一种基于使用相关向量机的稀疏贝叶斯学习。所提出的模型综合了地震运动的记录间变化和结构模型参数引起的不确定性。脆弱性估计中涉及的不确定性的大小通过提供脆弱性曲线的置信界限来表示。通过考虑非线性单自由度系统和五层钢筋混凝土建筑框架，将所提出的 BLR 模型的有效性与常用的云方法和 SFA 的最大似然估计方法进行比较。观察到，与考虑直接基于蒙特卡罗模拟的脆性结果作为基准的那些常见分析 SFA 方法相比，这两种 BLR 模型都可以以更高的精度估计脆性。