This paper explores the capabilities of decision tree ensembles (DTEs) for detecting and localizing damage in structural health monitoring (SHM). Unlike research on many other learning models, the goal of this study is to identify damage with a localization accuracy down to the single structural element, rather than limiting the evaluation to the story scale. The SHM methodology herein discussed, denoted as D²-DTE, is based on decision trees ensemble and belongs to the class of vibration-based approaches, being the health assessment of the structure obtained by analyzing dynamic properties of the structural system, namely, mode shapes and natural frequencies. The proposed damage detection method is validated for three different test cases, including both numerical simulations and experimentally recorded data, which consider a wide array of damage configurations, including single and multiple damages; different damage types and severities; and the presence of random noise levels associated with dynamic properties acquisition. The performances of the D²-DTE are evaluated in terms of accuracy, confidence of probabilistic predictions, and measurements of physical distances in localization errors. Additionally, two of the investigated test cases are based on available benchmarks, thus allowing a direct comparison with a state-of-the-art methodology. This comparative analysis evidences competitive performances of the DTE learning method.

中文翻译：

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使用决策树集成和振动数据进行结构损伤检测和定位

本文探讨了决策树集成 (DTE) 在结构健康监测 (SHM) 中检测和定位损坏的能力。与对许多其他学习模型的研究不同，本研究的目标是通过定位精确到单个结构元素来识别损坏，而不是将评估限制在故事尺度上。此处讨论的 SHM 方法，表示为 D ²-DTE，基于决策树集成，属于基于振动的方法类，是通过分析结构系统的动态特性（即振型和固有频率）获得的结构健康评估。所提出的损伤检测方法在三个不同的测试案例中得到验证，包括数值模拟和实验记录的数据，它们考虑了广泛的损伤配置，包括单一和多重损伤；不同的损坏类型和严重程度；以及与动态属性获取相关的随机噪声水平的存在。D ²的表现-DTE 根据准确性、概率预测的置信度以及定位误差中物理距离的测量进行评估。此外，两个调查的测试用例基于可用的基准，因此可以与最先进的方法进行直接比较。这种比较分析证明了 DTE 学习方法的竞争性能。