Optimizing risk treatment of structures in post-event decision-making is extremely difficult due to the lack of information on building damage/status after an event, particularly for nonlinear structures. This work develops an automated, no human intervention, modeling approach using structural health monitoring results to create accurate digital building clones of nonlinear structures for collapse prediction assessment and optimized decision-making. Model-free hysteresis loop analysis structural health monitoring method provides accurate structural health monitoring results from which model parameters of a nonlinear computational foundation model are identified. A new identifiable nonlinear smooth hysteretic model capturing essential structural dynamics and deterioration is developed to ensure robust parameter identification using support vector machines. Method performance is validated against both numerical and experimental data of a scaled 12-story reinforced concrete nonlinear structure. Results of numerical validation show an average error of 1.5% across 18 structural parameters from hysteresis loop analysis and an average error of 2.0% over 30 identified model parameters from support vector machines in the presence of 10% added root-mean-square noise. Validation using experimental data of the scale test reinforced concrete structure also shows a good match of identified hysteresis loop analysis and predicted nonlinear stiffness changes using the digital clones created with an average difference of 1.4%. More importantly, the predicted response using the digital clones for the highly nonlinear pinched hysteretic behavior matches the measured response well, with the average correlation coefficient Rcoeff = 0.92 and average root-mean-square error of 4.6% across all cases. The overall approach takes structural health monitoring from a tool providing retrospective damage data into automated prospective prediction analysis by “cloning” the structure using computational modeling, which in turn allows optimized decision-making using existing risk analyses and tools.

中文翻译：

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一种新的收缩非线性滞后结构模型，用于在结构健康监测中自动创建数字克隆

由于缺乏事件后建筑物损坏/状态的信息，特别是非线性结构，因此在事后决策中优化结构的风险处理极其困难。这项工作开发了一种自动化、无人干预的建模方法，使用结构健康监测结果创建非线性结构的准确数字建筑克隆，用于倒塌预测评估和优化决策。无模型磁滞回线分析结构健康监测方法提供准确的结构健康监测结果，从中识别非线性计算基础模型的模型参数。开发了一种新的可识别非线性平滑滞后模型，捕获基本结构动力学和退化，以确保使用支持向量机进行稳健的参数识别。方法性能根据按比例缩放的 12 层钢筋混凝土非线性结构的数值和实验数据进行验证。数值验证结果表明，在存在 10% 的均方根噪声的情况下，滞后回线分析的 18 个结构参数的平均误差为 1.5%，支持向量机的 30 个已识别模型参数的平均误差为 2.0%。使用比例测试钢筋混凝土结构的实验数据进行的验证还表明，确定的滞后回线分析与使用平均差异为 1.4% 的数字克隆创建的预测非线性刚度变化之间具有良好的匹配。更重要的是，对于高度非线性收缩滞后行为，使用数字克隆的预测响应与测量响应非常匹配，平均相关系数 Rcoeff = 0.92，所有情况下的平均均方根误差为 4.6%。整体方法通过使用计算模型“克隆”结构，将结构健康监测从提供回顾性损坏数据的工具转变为自动化的前瞻性预测分析，从而允许使用现有风险分析和工具优化决策。对于高度非线性收缩滞后行为，使用数字克隆的预测响应与测量响应非常匹配，平均相关系数 Rcoeff = 0.92，所有情况下的平均均方根误差为 4.6%。整体方法通过使用计算模型“克隆”结构，将结构健康监测从提供回顾性损坏数据的工具转变为自动化的前瞻性预测分析，从而允许使用现有风险分析和工具优化决策。对于高度非线性收缩滞后行为，使用数字克隆的预测响应与测量响应非常匹配，平均相关系数 Rcoeff = 0.92，所有情况下的平均均方根误差为 4.6%。整体方法通过使用计算模型“克隆”结构，将结构健康监测从提供回顾性损坏数据的工具转变为自动化的前瞻性预测分析，从而允许使用现有风险分析和工具优化决策。