Unsupervised learning methods are effective and suitable tools for damage detection. The main reason for the popularity of these methods in structural health monitoring originates from the fact that the process of learning can be implemented by information of the only normal condition called training data. In contrast, supervised learning methods require information of both normal and current conditions for the process of interest. Because civil engineering structures are expensive and complex, it is not reasonable and economical to impose intentional damage on providing training data. Hence, it is not simple to directly exploit supervised learning techniques in structural health monitoring. To deal with this limitation, this article proposes a novel two-level strategy including three algorithms for using the concepts of both unsupervised learning and supervised learning. The major contribution of this strategy is to consider supervised learning as a validation tool for damage detection. First, the results of damage detection are obtained from two unsupervised learning methods developed by Mahalanobis squared distance and a deep autoencoder neural network in the first two algorithms of the proposed strategy. The main objective is to separate accurate and confusing results of damage detection based on Type I and Type II errors. Second, the confusing results are fed into the third algorithm to train a classifier and compute their classification margins for making the final decision and validating damage detection. The effectiveness and applicability of the proposed strategy are assessed by a numerical concrete beam and an experimental laboratory frame. Results show that this strategy with the aid of the Naïve Bayes classifier enables the unsupervised learning methods to make accurate decisions.

无监督的学习方法是有效且合适的工具，用于进行损坏检测。这些方法在结构健康监测中普及的主要原因是这样的事实，即学习过程可以通过称为训练数据的唯一正常情况的信息来实现。相比之下，监督学习方法需要有关感兴趣过程的正常和当前状况的信息。由于土木工程结构昂贵且复杂，因此在提供培训数据时施加故意损害是不合理且不经济的。因此，在结构健康监测中直接利用监督学习技术并不简单。为了解决这个限制，本文提出了一种新颖的两级策略，其中包括使用无监督学习和有监督学习的概念的三种算法。该策略的主要贡献是将监督学习视为损坏检测的验证工具。首先，在提出的策略的前两种算法中，通过马哈拉诺比斯平方距离和深度自动编码器神经网络开发的两种无监督学习方法获得了损伤检测的结果。主要目的是根据类型I和类型II错误将准确而令人困惑的损坏检测结果分开。其次，将令人困惑的结果输入到第三种算法中，以训练分类器并计算其分类余量，以做出最终决策并验证损伤检测。通过数字混凝土梁和实验实验室框架评估了所提出策略的有效性和适用性。结果表明，借助朴素贝叶斯分类器的这种策略使无监督学习方法能够做出准确的决策。