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Continuous dynamic monitoring brings an important opportunity to evaluate the health and integrity of civil structures in a long-term manner. However, high dimensionality and sparsity of data caused by long-term monitoring and negative influences of environmental and/or operational variability are major challenges in this process. To address these important issues, this article proposes an innovative unsupervised data normalization method based on a novel hybrid feature weighting-selection algorithm and the idea of natural nearest neighbor (NN) searching emanated from the theory of mutual friendships in human societies. The proposed hybrid algorithm is a combination of global feature weighting with a new weighting measure and local feature selection. For this algorithm, this article leverages the natural NN searching that seeks to find adequate NNs automatically. The main objective of the proposed method is to remove the environmental and/or operational effects and provide normalized weighted features for reliable continuous dynamic monitoring. Using such features, an anomaly detector based on the Mahalanobis-squared distance is developed to assess and detect structural damage. The key innovations of this paper contain proposing a fully nonparametric unsupervised learning technique in two parts of data normalization and anomaly detection and developing a novel hybrid algorithm for removing the environmental and/or operational variations. Long-term dynamic features (modal frequencies) of a three-span box-girder concrete bridge (Z24 Bridge) and a long-span concrete arch bridge (Infante Dom Henrique Bridge) are considered to verify the proposed technique with several comparisons. Results indicate that this technique is successful and reliable in mitigating the environmental and/or operational effects and notifying accurate structural states. [ABSTRACT FROM AUTHOR]

Summary: Environmental variability is still a major challenge in structural health monitoring. Due to the similarity of changes caused by environmental variations to damage, false positive and false negative errors are prevalent in detecting damage that cause serious economic and safety issues. To address this challenge and its disadvantages, this article proposes a novel ensemble learning‐based method in a nongenerative sequential algorithm for structural health monitoring under varying environmental conditions by three kinds of Mahalanobis distance metrics in three main levels. At each level, one attempts to find a few and adequate nearest neighbors of each feature to remove environmental variability via an innovative approach. The major contribution of this article is to develop a novel data‐based method by the concepts of ensemble learning and unsupervised learning. The great advantages of the proposed method include developing a nonparametric data‐based framework without estimating any unknown parameter, dealing with the negative effects of environmental variability, improving the performance of Mahalanobis distance, and increasing damage detectability. The performance and effectiveness of this method are validated by modal features of two real bridge structures along with several comparisons. Results demonstrate that the proposed ensemble learning‐based method highly succeeds in detecting damage under environmental variability, and it is superior to some state‐of‐the‐art techniques. [ABSTRACT FROM AUTHOR]

Damage localization of damaged structures is an important issue in structural health monitoring. In data-based methods based on statistical pattern recognition, it is necessary to extract meaningful features from measured vibration signals and utilize a reliable statistical technique for locating damage. One of the challenging issues is to extract reliable features from non-stationary vibration signals caused by ambient excitation sources. This article proposes a new energy-based method by using ensemble empirical mode decomposition and Mahalanobis-squared distance to obtain energy-based multivariate features and locate structural damage under ambient vibration and non-stationary signals. The main components of the proposed method include extracting intrinsic mode functions of vibration signals by ensemble empirical mode decomposition, choosing adequate and optimal intrinsic mode functions, partitioning the selected intrinsic mode functions at each sensor into segments with the same dimensions, calculating the intrinsic mode function energy at each segment, preparing energy-based multivariate features at each sensor, computing Mahalanobis-squared distance values, and obtaining a vector of average Mahalanobis-squared distance quantities of all sensors. The major contributions of the proposed method consist of proposing an innovative non-parametric strategy for feature extraction, presenting generalized Pearson correlation function for the selection of optimal intrinsic mode functions, using a simple and effective segmentation algorithm, and applying energy-based features to the process of damage localization. The main advantage of the proposed method is its great applicability to locating single and multiple damage cases. The measured vibration responses of the well-known IASC-ASCE structure are applied to verify the effectiveness and reliability of the proposed energy-based method along with several comparative studies. Results will demonstrate that this approach is highly capable of locating damage under stationary and non-stationary vibration signals attributable to ambient excitations. [ABSTRACT FROM AUTHOR]