Structural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required to further reduce the burden of manual analysis. Thus, this study proposed a dynamic warning method for SHM data based on ARIMA and applied it to the concrete strain data of the Hong Kong–Zhuhai–Macao Bridge (HZMB) immersed tunnel. First, wavelet threshold denoising was applied to filter noise from the SHM data. Then, the feasibility and accuracy of establishing an ARIMA model were verified, and it was adopted to predict future time series of SHM data. After that, an anomaly detection scheme was proposed based on the dynamic model and dynamic threshold value, which set the confidence interval of detected anomalies based on the statistical characteristics of the historical series. Finally, a hierarchical warning system was defined to classify anomalies according to their detection threshold and enable hierarchical treatments. The illustrative example of the HZMB immersed tunnel verified that a three-level (5.5 σ, 6.5 σ, and 7.5 σ) dynamic warning schematic can give good results of anomalies detection and greatly improves the efficiency of SHM data management of the tunnel.

The evolution, habitat, and lifestyle of the cryptic clade II of Escherichia, which were first recovered at low frequency from non-human hosts and later from external environments, were poorly understood. Here, the genomes of selected strains were analyzed for preliminary indications of ecological differentiation within their population. We adopted the delta bitscore metrics to detect functional divergence of their orthologous genes and trained a random forest classifier to differentiate the genomes according to habitats (gastrointestinal vs external environment). Model was built with inclusion of other Escherichia genomes previously demonstrated to have exhibited genomic traits of adaptation to one of the habitats. Overall, gene degradation was more prominent in the gastrointestinal strains. The trained model correctly classified the genomes, identifying a set of predictor genes that were informative of habitat association. Functional divergence in many of these genes were reflective of ecological divergence. Accuracy of the trained model was confirmed by its correct prediction of the habitats of an independent set of strains with known habitat association. In summary, the cryptic clade II of Escherichia displayed genomic signatures that are consistent with divergent adaptation to gastrointestinal and external environments.

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