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Detecting damage constitutes the primary and pivotal stage in monitoring a structure's health. Early identification of structural issues, coupled with a precise understanding of the structure's condition, represents a cornerstone in the practices of structural health monitoring (SHM). While many existing methods prove effective when the number of data points in both healthy and damaged states is equal, this article employs algorithms tailored for detecting damage in situations where data are imbalanced. Imbalance, in this context, denotes a significant difference in the number of data points between the healthy and damaged states, essentially introducing an imbalance within the dataset. Four imbalanced classification algorithms are applied to two benchmark structures: the first, a numerical model of a four-story steel building, and the second, a bridge constructed in China. This research thoroughly assesses the performance of these four algorithms for each structure, both individually and collectively. [ABSTRACT FROM AUTHOR]

As intelligent sensing and sensor network systems have made progress and low‐cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large‐scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high‐accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deep‐learning‐enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one‐dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high‐accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long‐span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data. [ABSTRACT FROM AUTHOR]