论文标题
用于结构损伤检测的无参数学习方法
A Nonparametric Unsupervised Learning Approach for Structural Damage Detection
论文作者
论文摘要
在一个老化的基础设施世界中，结构性健康监测（SHM）是迈向韧性和可持续社会的主要一步。与传统的非破坏性测试方法相比，机器学习和传感器技术的当前进步使SHM成为更有前途的损害检测方法。 SHM使用无监督的学习方法为更常用的监督学习提供了一种有吸引力的替代方法，因为它仅需要在培训过程中正常条件下结构的数据。基于密度的新颖性检测方法为损伤检测过程提供了一个统计元素，但在很大程度上取决于估计的概率密度函数（PDF）的准确性。在这项研究中，提出了一种新型的SHM学习方法。它是基于内核密度最大熵方法，它通过利用贝叶斯优化进行超参数调整，并通过使用独立的组件分析将方法扩展到多变量空间。在数值模拟的三层钢筋混凝土力矩框架上评估了所提出的方法，其中在结构损伤检测中获得了94％的精度。
In a world of aging infrastructure, structural health monitoring (SHM) emerges as a major step towards resilient and sustainable societies. The current advancements in machine learning and sensor technology have made SHM a more promising damage detection method than the traditional non-destructive testing methods. SHM using unsupervised learning methods offers an attractive alternative to the more commonly used supervised learning since it only requires data of the structure in normal conditions for the training process. The density-based novelty detection method provides a statistical element to the damage detection process but it relies heavily on the accuracy of the estimated probability density function (PDF). In this study, a novel unsupervised learning approach for SHM is proposed. It is based on the Kernel Density Maximum Entropy method by leveraging Bayesian optimization for hyperparameter tuning and also by extending the method into the multivariate space by the use of independent components analysis. The proposed approach is evaluated on a numerically simulated three-story reinforced concrete moment frame, where 94% of accuracy is achieved in structural damage detection.
