The North China Plain is an important summer maize production region in China. Investigating spatiotemporal variation patterns of precipitation during the summer maize growing season will guide the prevention of droughts and floods and ensure food production. Daily precipitation data during the summer maize growing season in the North China Plain from 1960–2020 were used to analyze spatiotemporal changes in precipitation, examine the migration patterns of precipitation barycenters, and quantitatively analyze the effects of ENSO (El Niño-Southern Oscillation) warm and cold events on precipitation variation characteristics. Results revealed that in the past 61 years, precipitation showed an insignificant decreasing trend; however, there were considerable differences detected in the spatial distribution layouts of precipitation between different developmental stages. The precipitation distribution layout during the sowing–jointing stage was mainly “North–South”, the zero contour was near 36° N, and the other developmental stages were mainly “global” with phases that were the opposite of one another. Moreover, the precipitation barycenter during the jointing–flowering stage showed a significant southward migration. Precipitation during the three developmental stages negatively correlated with warm events, precipitation during the flowering–maturation stage positively correlated with cold events, the relationship between precipitation changes during warm and cold events and the intensity of warm and cold events was not significant, and Pacific Decadal Oscillation (PDO) was the main climatic factor that affected precipitation changes during the summer maize-growing season in the North China Plain.

Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season.

Rescue-induced vibrations easily trigger dynamic instability and secondary collapse of post-disaster metastable structures, threatening rescue safety and efficiency. This paper comprehensively reviews research on vibration-induced instability and dynamic responses and mitigation strategies of these discontinuous structural systems. We analyze vibration propagation, energy concentration and progressive collapse mechanisms, and summarize parameterized modeling, physical tests and mainstream numerical methods including FEM, DEM and F-DEM, with their pros and cons compared. Typical vibration-mitigation technologies such as passive support, damping reinforcement, and semi-active and active control are classified and discussed, and nonlinear energy sinks as well as anti-phase control are elaborated on. Validation studies in rescue-training bases are also presented. Finally, the study is synthesized to clarify the interconnections among dynamic monitoring, structural modeling, and vibration mitigation. This review-derived synthesis identifies current knowledge gaps and outlines future research directions for rescue-oriented dynamic safety assessment. This review provides theoretical and engineering references for safe disaster rescue.

Reinforced concrete frame structures (RCFSs) subjected to strong seismic excitation may enter a metastable semi-ruin state before global collapse, characterized by severe local damage, degraded stability, and high secondary collapse risk. However, systematic experimental investigations and quantitative identification techniques for this critical transitional state are still lacking in existing seismic engineering literature, forming a notable research gap for post-earthquake safety evaluation. To investigate this critical transition, a Digital Image Correlation (DIC)-assisted shaking table test was conducted on a 1/25-scale RCFS specimen derived from an earthquake-damaged exterior-corridor teaching building, using the Wolong ground motion recorded during the 2008 Wenchuan earthquake as input. DIC was employed to track the full-field evolution of cracking, through-crack development, and concrete cover spalling under incremental seismic loading. Four local damage indices—crack line density (CLD), crack propagation rate (CPR), through-crack ratio (TCR), and concrete spalling ratio (CSR)—were extracted and evaluated with the inter-story drift ratio (IDR) to quantify local-to-global degradation. The results show that visible cracks initiated at PGA = 0.3 g, while accelerated crack propagation occurred at 0.7–0.8 g, with CPR peaks of 1187.5 and 1140 mm/g, respectively. At 0.5–1.0 g, the crack number increased from 13 to 26, total crack length reached 0.443 m, CLD increased to 3.9 × 10⁻⁴, and TCR reached 37.04%. At 1.1–1.5 g, crack development approached saturation, with total crack length of 0.552 m, maximum TCR of 63.6%, and CLD of 4.8 × 10⁻⁴. Under ultimate excitation of 1.6–1.8 g, the crack number stabilized at 33–34, TCR remained around 63%, cumulative spalling area reached 1026 mm², CSR reached 0.015, and the third-floor IDR approached the 1/50 elastoplastic limit. Severe through-cracking, reinforcement exposure, concrete spalling, and residual inclination indicated the onset of the semi-ruin state. The proposed multi-index framework provides quantitative support for semi-ruin-state identification and post-earthquake secondary collapse risk assessment of RCFSs.