Binary Segmentation for Structural Condition Classification Using Structural Health Monitoring Data

TL;DR: Li et al. as mentioned in this paper proposed an adversarial domain adaptation (DA) approach to transfer knowledge from a well-controlled monitoring test in one rail section to the rail section of interest, which can sufficiently eliminate the distribution discrepancy induced by the operational differences between two rail sections on which the train runs.

TL;DR: This study proposes an unsupervised deep learning approach, DCVAE-SVDD, for structural anomaly detection using probabilistic features extracted from SHM data, outperforming existing methods in detection accuracy and handling correlated sensor information and data variability.

TL;DR: Wang et al. as mentioned in this paper developed a more flexible multiple change-point detection method for the distributions of DSF data employed for data-driven structural condition assessment, which can detect structural changes in extracted damage-sensitive features (DSFs).

TL;DR: The main focus of this special collection is to present recent advances in damage detection, health monitoring, structure condition assessment, and innovative applications in repair, retrofitting, and rehabilitation of structures.

TL;DR: Technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner and the historical overview and summarizing the SPR paradigm are provided.

TL;DR: This work considers the problem of detecting multiple changepoints in large data sets and introduces a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost which is linear in the number of observations.

TL;DR: The motivations for and recent history of SHM applications to various forms of civil infrastructure are described, the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‘health’ are discussed.

TL;DR: It is shown that the common trend TPR and Sawa’s Bayes criteria procedures seem optimal for most climate time series, whereas the SNH procedure and its nonparametric variant are probably best for mostClimate time series.