使用具有强泛化能力的改进稀疏贝叶斯学习方案实现SHM测量的高精度数据建模

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Qi‐Ang Wang, Yang Dai, Zhan-guo Ma, Jun-Fang Wang, Jian‐Fu Lin, Y. Ni, W. Ren, Jian Jiang, Xuan Yang, Jia-Ru Yan
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引用次数: 5

摘要

结构健康监测(SHM)的核心是基于复杂的SHM系统的数据建模、操作和解释。尽管SHM技术不断发展,但如何在各种不确定因素下对SHM测量数据进行精确建模和预测,以提取与结构状态相关的知识,仍然是一个挑战。针对这一问题,提出了一种基于改进的稀疏贝叶斯学习(iSBL)方案的全概率高精度数据建模方法。提出的iSBL数据建模框架具有以下优点。该方法可以消除数据拟合函数中指定项数的需要,并根据SHM监测数据的特征对贝叶斯模型进行稀疏化自动化处理,增强了模型的泛化能力,进而提高了数据的预测精度。iSBL方案嵌入贝叶斯框架,该框架具有内置的防止过拟合问题的保护,对数据噪声具有很高的鲁棒性,特别是在数据预测方面。该模型对实际大型斜拉桥SHM振动场监测数据的有效性进行了验证。研究了两种不同振动模式下记录的加速度数据,即平稳环境振动数据和非平稳衰减振动数据,在时域和频域都得到了准确的概率预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability
Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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