Mass redistribution within the Earth system deforms the surface elastically. Loading theory allows us to predict loading induced displacement anywhere on the Earth’s surface using environmental loading models, e.g., Global Land Data Assimilation System. In addition, different publicly available loading products are available. However, there are differences among those products and the differences among the combinations of loading models cannot be ignored when precisions of better than 1 cm are required. Many scholars have applied these loading corrections to Global Navigation Satellite System (GNSS) time series from mainland China without considering or discussing the differences between the available models. Evaluating the effects of different loading products over this region is of paramount importance for accurately removing the loading signal. In this study, we investigate the performance of these different publicly available loading products on the scatter of GNSS time series from the Crustal Movement Observation Network of China. We concentrate on five different continental water storage loading models, six different non-tidal atmospheric loading models, and five different non-tidal oceanic loading models. We also investigate all the different combinations of loading products. The results show that the difference in RMS reduction can reach 20% in the vertical component depending on the loading correction applied. We then discuss the performance of different loading combinations and their effects on the noise characteristics of GNSS height time series and horizontal velocities. The results show that the loading products from NASA may be the best choice for corrections in mainland China. This conclusion could serve as an important reference for loading products users in this region.

Bridge dynamic monitoring based on GNSS has become an important means of monitoring bridge structure. GNSS dynamic monitoring signals are often overwhelmed by strong noises and multipath errors. Thus the conventional data processing methods such as Fourier transform, wavelet analysis, and others have poor denoising effect or obtain unconspicuous dynamic characteristics from weak vibration signals. To solve this problem, the present study proposes a new adaptive stochastic resonance method based on quantum genetic algorithm with known frequency as optimal parameter. Analyzing the simulation signals not only verifies the validity and scientificity of the method, but also analyzes its frequency extraction effect in the approximate error range of target frequency with different noise intensity. A notable bridge vibration frequency is obtained when the new method is applied to analyze the bridge dynamic monitoring data based on GNSS.

Bridges in mining areas deform primarily because of surface subsidence caused by underground mining. Analysis of these deformations should consider the synergistic effects between the foundation soil and the bridge superstructure. The geology of mining areas, which is inherently complex, significantly effects the selection of soil mechanics parameters, potentially leading to errors in model calculations. This study focuses on the bridge in the Fengfeng mining area of Handan, Hebei, and uses the probabilistic integral method to predict subsidence at the bottom section below the surface over a specified period. A finite element model of the bridge and foundation soil is constructed, utilising these predicted subsidence contour lines as boundary conditions for simulating the surrounding surface subsidence. Additionally, monitoring data of surface and bridge subsidence from the same period are collected to validate the simulation accuracy, analyse error types and causes, and propose model improvements. After the feasibility of the improvement schemes is validated, the revised model is employed to predict the bridge failure trend. The model parameters can be adjusted based on surface subsidence monitoring data, and by integrating these adjustments with the predicted subsidence results at the bottom section during various mining stages, the bridge failure trend can be accurately predicted.

Blasting is widely used in mining, subway, demolition and groundwater-sealed tunnel, among them, the last one is widely concerned because of its many adjacent tunnels, high anti-seepage requirements, strict blasting control, etc. The identification of blasting characteristics is of great significance to the blasting construction and the safety evaluation of the groundwater-sealed tunnel. In view of the problem that conventional feature identification methods are less explored in groundwater-sealed tunnel, a complementary ensemble empirical mode decomposition with adaptive noise and multiscale permutation entropy and Hilbert–Huang transform (HHT) method was proposed. Then, the proposed method was verified by the numerical simulation and the Huangdao groundwater-sealed tunnel engineering. The results show that the proposed method can suppress modal aliasing and signal noise and identify the blasting characteristics of groundwater-sealed tunnel effectively. In addition, the blasting vibration energy which accounts for 94.7% in the frequency range of 0–200 Hz, 72.5% of 0–50 Hz was summarized. Furthermore, the safety status of each monitoring point was evaluated through HHT and the feasibility of millisecond blasting was identified. The method proposed can identify the vibration characteristics and safety status of groundwater-sealed tunnel from the perspective of time–frequency and energy effectively.