This paper provides a new approximate Bayesian computation (ABC) algorithm with reduced hyper-parameter scaling and its application to nonlinear structural model calibration problems. The algorithm initially takes the ABC-SubSim algorithm structure and sequentially estimates the algorithm hyper-parameter by autonomous adaptation following a Markov chain approach, thus avoiding the error associated to modeler's choice for these hyper-parameters. The resulting algorithm, named BC-SubSim, simplifies the application of ABC-SubSim method for new users while ensuring better measure of accuracy in the posterior distribution and improved computational efficiency. A first numerical application example is provided for illustration purposes and to provide a comparative and sensitivity analysis of the algorithm with respect to initial ABC-SubSim algorithm. Moreover, the efficiency of the method is demonstrated in two nonlinear structural calibration case studies where the BC-SubSim is used as a tool to infer structural parameters with quantified uncertainty based on test data. The results confirm the suitability of the method to tackle with a real-life damage parameter inference and its superiority in relation to the original ABC-SubSim.

中文翻译：

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用于结构模型校准的子集模拟自适应近似贝叶斯计算

本文提供了一种新的近似贝叶斯计算（ABC）算法，具有减少的超参数缩放及其在非线性结构模型校准问题中的应用。该算法最初采用ABC-SubSim算法结构，并按照马尔可夫链方法通过自主自适应顺序估计算法超参数，从而避免了与建模者选择这些超参数相关的错误。生成的算法，命名为BC-SubSim，为新用户简化了 ABC-SubSim 方法的应用，同时确保更好地测量后验分布的准确性并提高计算效率。提供第一个数值应用示例用于说明目的，并提供该算法相对于初始 ABC-SubSim 算法的比较和敏感性分析。此外，在两个非线性结构校准案例研究中证明了该方法的效率，其中BC-SubSim 被用作一种工具，根据测试数据推断具有量化不确定性的结构参数。结果证实了该方法适用于处理现实损伤参数推断及其相对于原始 ABC-SubSim 的优越性。