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The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications. [ABSTRACT FROM AUTHOR]

• Suggesting a non-parametric method for early damage alerts using limited vibration data. • Focusing on unsupervised learning without relying on labeled data. • Introducing a new non-parametric clustering method with automated cluster selection. • Developing a novel density-based anomaly detector for damage detection without hyperparameter tuning. • Validating the method effectiveness on real-world bridges. Machine learning-assisted vibration monitoring is an intelligent, automated, and popular strategy for evaluating civil structures and damage alarming. However, implementing this strategy under a short-term monitoring program may encounter challenges such as limited vibration data, profound environmental and operational variations, and the limitations of state-of-the-art solutions under these conditions. The main purpose of this paper is to propose a novel machine learning technique in terms of unsupervised learning for damage alarming with limited vibration data. The crux of this technique lies in two fully non-parametric parts of data partitioning and anomaly detection. Initially, a non-parametric clustering approach with a novel procedure is presented to divide limited vibration data into clusters. Subsequently, a new density-based anomaly detector is developed to prepare indicators for damage alarming. Limited eigenfrequencies of full-scale bridge structures are used to validate the proposed solution. Results can substantiate its effectiveness and practicability in short-term monitoring programs. [ABSTRACT FROM AUTHOR]

• Proposing a novel double-hybrid learning method for damage assessment under different environmental variation patterns. • Leveraging a self-adaptive neighbor searching algorithm for automatically determining the number of nearest neighbors. • Improving a hybrid clustering algorithm with a simple but effective approach to selecting the number of clusters. • Proposing a non-parametric hybrid anomaly detector based on local outlier factor. Monitoring of modal frequencies under an unsupervised learning framework is a practical strategy for damage assessment of civil structures, especially bridges. However, the key challenge is related to high sensitivity of modal frequencies to environmental and/or operational changes that may lead to economic and safety losses. The other challenge pertains to different environmental and/or operational variation patterns in modal frequencies due to differences in structural types, materials, and applications, measurement periods in terms of short and/or long monitoring programs, geographical locations of structures, weather conditions, and influences of single or multiple environmental and/or operational factors, which may cause barriers to employing state-of-the-art unsupervised learning approaches. To cope with these issues, this paper proposes a novel double-hybrid learning technique in an unsupervised manner. It contains two stages of data partitioning and anomaly detection, both of which comprise two hybrid algorithms. For the first stage, an improved hybrid clustering method based on a coupling of shared nearest neighbor searching and density peaks clustering is proposed to prepare local information for anomaly detection with the focus on mitigating environmental and/or operational effects. For the second stage, this paper proposes an innovative non-parametric hybrid anomaly detector based on local outlier factor. In both stages, the number of nearest neighbors is the key hyperparameter that is automatically determined by leveraging a self-adaptive neighbor searching algorithm. Modal frequencies of two full-scale bridges are utilized to validate the proposed technique with several comparisons. Results indicate that this technique is able to successfully eliminate different environmental and/or operational variations and correctly detect damage. [ABSTRACT FROM AUTHOR]