The sensitivity of structural health monitoring systems to environmental and operational conditions poses a significant challenge due to their inherent susceptibility to outliers. This paper proposes an effective model-updating-based optimization algorithm that can alleviate the impact of outliers associated with field and operational fluctuations. The proposed method addresses the influence of uncertainties from sources such as white noise, colored noise, and measurement errors, which can introduce outliers in datasets. The approach comprises a hybrid procedure in which a Gaussian smoothing technique is first employed to smooth out measured data to reduce the impact of irregularities. Next, Johansen cointegration is employed for raw data fusion to further enhance the signature of shared patterns. A novel optimization algorithm based on the Reptile Search Algorithm (RSA), named Improved RSA (IRSA), is proposed to solve the objective function based on the concept of mutual information. This algorithm provides a superior solution with much improved computational speed and accuracy compared to RSA. The new hybrid method was validated by several numerical and experimental damage detection studies. Furthermore, it was compared to other state-of-the-art methods described in the literature. The results clearly demonstrate the superior performance of the newly developed method.

中文翻译：

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使用改进的爬行动物搜索算法和数据分析技术增强噪声输入信号的损坏检测

结构健康监测系统对环境和操作条件的敏感性由于其固有的对异常值的敏感性而构成了重大挑战。本文提出了一种有效的基于模型更新的优化算法，可以减轻与现场和操作波动相关的异常值的影响。所提出的方法解决了白噪声、有色噪声和测量误差等来源的不确定性的影响，这些来源可能会在数据集中引入异常值。该方法包括一个混合过程，其中首先采用高斯平滑技术来平滑测量数据，以减少不规则性的影响。接下来，采用 Johansen 协整进行原始数据融合，以进一步增强共享模式的签名。提出了一种基于爬虫搜索算法（RSA）的新型优化算法，称为改进的RSA（IRSA），用于基于互信息的概念来求解目标函数。该算法提供了一种卓越的解决方案，与 RSA 相比，计算速度和准确性大大提高。新的混合方法通过多项数值和实验损伤检测研究得到验证。此外，它还与文献中描述的其他最先进的方法进行了比较。结果清楚地证明了新开发方法的优越性能。