Abstract Fragility curves which express the failure probability of a structure as function of a loading intensity measure are nowadays widely used to facilitate the design and decision making of structures/infrastructures against seismic hazard (and possibly other natural hazards), with analysis procedures specified by Seismic Probabilistic Risk Assessment, Performance-Based Earthquake Engineering, and other frameworks. To avoid the use of parametric models (such as the lognormal model) to estimate fragility curves from a reduced number of numerical calculations, a methodology based on Support Vector Machines (SVMs) coupled with an active learning algorithm is proposed in this paper. In practice, input excitation is reduced to some relevant parameters and then SVMs are used for a binary classification of the structural responses relative to a limit threshold of exceedance. Since the output is not binary but a real-valued score, a probabilistic interpretation of the output is exploited to estimate very efficiently fragility curves as score functions or as functions of classical seismic intensity measures.

中文翻译：

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通过支持向量机上的主动学习进行地震易损性曲线估计的有效方法

摘要 将结构的失效概率表示为载荷强度测量的函数的脆性曲线如今被广泛用于促进结构/基础设施的设计和决策制定，以抵御地震灾害（以及可能的其他自然灾害），其分析程序由 Seismic 指定。概率风险评估、基于性能的地震工程和其他框架。为了避免使用参数模型（例如对数正态模型）通过减少的数值计算来估计脆性曲线，本文提出了一种基于支持向量机（SVM）和主动学习算法的方法。在实践中，输入激励被减少到一些相关参数，然后 SVM 被用于结构响应相对于超出极限阈值的二元分类。由于输出不是二进制而是实值分数，因此利用输出的概率解释来非常有效地估计脆性曲线作为分数函数或经典地震烈度测量的函数。