Abstract Structural health monitoring (SHM) is backwards analysis of past to current state of damage, but cannot create a structure-specific nonlinear model for forward analysis of future response and damage. This paper aims to develop an automated modelling approach to translate proven hysteresis loop analysis (HLA) SHM results into nonlinear foundation models for response forecasting in subsequent events, particularly for steel structures with post-yielding behaviors. Support vector machine (SVM) is employed to identify the proposed nonlinear baseline model. Stiffness features are extracted from HLA to train the SVM model incorporating the constraints of SHM identification. A proof-of-concept case study validates the ability of the proposed method to accurately identify 12 model parameters with average error of 2.8% for a nonlinear numerical structure in the presence of 10% RMS measurement noise. Experimental validation from a full-scale 3-storey real building shows the predicted nonlinear responses match the measured response well with cross correlation coefficients Rcoeff = 0.94, 0.92 and 0.89 for the first, second and third floor, respectively. In addition, the predicted stiffness changes also match the SHM results very well with errors less than 2.1%. Finally, and most importantly, the identified model is able to predict the response of 2 further events with average of correlation coefficient Rcoeff = 0.91 and average error of 1.9% for stiffness changes across all cases. The overall results validate the ability of the created predictive model to accurately capture the essential dynamics and structural degradation, as well as predicting future possible response and risk.

中文翻译：

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使用健康监测结果对非线性结构进行自动建模的支持向量机

摘要 结构健康监测 (SHM) 是对过去到当前损坏状态的反向分析，但不能创建特定于结构的非线性模型来对未来的响应和损坏进行正向分析。本文旨在开发一种自动化建模方法，将经过验证的磁滞回线分析 (HLA) SHM 结果转化为非线性基础模型，用于后续事件的响应预测，特别是对于具有屈服后行为的钢结构。采用支持向量机 (SVM) 来识别所提出的非线性基线模型。从 HLA 中提取刚度特征以训练包含 SHM 识别约束的 SVM 模型。概念验证案例研究验证了所提出的方法准确识别平均误差为 2 的 12 个模型参数的能力。在存在 10% RMS 测量噪声的情况下，非线性数值结构为 8%。来自全尺寸三层真实建筑的实验验证表明，预测的非线性响应与测量的响应很好地匹配，一楼、二楼和三楼的互相关系数 Rcoeff = 0.94、0.92 和 0.89。此外，预测的刚度变化也与 SHM 结果非常匹配，误差小于 2.1%。最后，也是最重要的是，识别的模型能够预测 2 个其他事件的响应，平均相关系数 Rcoeff = 0.91，所有情况下刚度变化的平均误差为 1.9%。总体结果验证了创建的预测模型准确捕捉基本动力学和结构退化的能力，