The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry synthetic aperture radar (SBAS-InSAR) method in identifying potential geological hazards in such areas, this study processes and analyzes 129 SAR images covering Ninghai County, China. By processing coherence coefficients using the Stacking technique, errors introduced by low-coherence images during phase unwrapping are mitigated. Subsequently, interferograms with high coherence are selected for time-series deformation analysis based on the statistical parameters of coherence coefficients. The results indicate that, after mitigating errors from low-coherence images, applying the SBAS-InSAR method to only high-coherence SAR datasets provides reliable surface deformation results. Additionally, when combined with field geological survey data, this method successfully identified landslide boundaries and potential landslides not accurately detected in previous geological surveys. This study demonstrates that using the SBAS-InSAR method and selecting high-coherence SAR images based on interferogram coherence statistical parameters significantly improves measurement accuracy and effectively identifies potential geological hazards.

In the construction process of large-scale bridges, there are uncertainties and time-varying factors in the environment and construction loads. It is difficult to make accurate estimates of the theoretical calculation models of construction control in advance. In view of this situation, Bayesian dynamic updating method is introduced to re-estimate the predicted results of the theoretical model. When applying this method, first, the finite element calculation model is determined based on the response surface method, and its calculation results are used as prior information. Then, combined with the actual detection data during the construction process, the Bayesian update formula is derived based on the conjugate prior distribution to correct the theoretical prediction results of bridge construction monitoring. Finally, the actual stress detection data of the control section of high-pier and large-span continuous rigid frame bridges during the construction process illustrate the application process of Bayesian updating in improving the theoretical prediction model. Results indicate that the internal force of the bridge control section obtained by re-evaluating by Bayesian theory not only incorporates the priori information models but also actual monitors sample information during the construction process. The predicted results reflect the true deformation and stress state of the bridge during the bridge construction process and improve the precision of construction monitoring.

Recently, the light-type pier can be easily observed in urban elevated roads and bridge structures. This type of pier is broadly used in the field of structural engineering. The Thin-Walled Steel-Reinforced Concrete Pier (TSRCP), as the typical pier of the light-type piers, shows a better mechanical performance than the conventional reinforced concrete pier due to the enhancement of the H-shape steel. In terms of the TSRCP, the designed parameters, including the depth–width ratio (DWR) and axial compression ratio (ACR), have been determined in this study as the concerned variables. Then, a low cyclic loading experiment is conducted based on the three test specimens to investigate the effects of the concerned variables on the failure of the specimens. Meanwhile, some comparative studies are carried out based on the failure processes and modes, critical loading values (cracking, yielding and ultimate state), strain and ductility. The obtained experimental results demonstrate that all specimens illustrate a bending failure mode under the vertical and horizontal low cyclic loadings. Furthermore, for the concerned variables, the increasing DWR will reduce the ultimate bearing capacity of the TSRCP but enhance the plastic deformation. For the ACR, the decrease positively affects the cracking load, further improving the ultimate bearing capacity; however, the deformation capacity will be restrained. Finally, Abaqus is adopted to model the failure processes of three specimens; the comparative study has been conducted based on the simulated and experimental results. After that, the effects of the concerned variables on the failure performances are discussed. Meanwhile, the horizontal ultimate bearing capacity calculated equation for the TSRCP was deduced and validated based on the experimental and simulated data; the verification proves that the proposed equation can obtain a good result. In this study, the complex calculated process of the original equation can be simplified, which not only provides a convenient and useful way to design and manufacture this type of component but can serve as a guideline for validating the practical engineering applications.

The soil parameters of artificial soil foundations exhibit significant spatial variability due to particle sorting during the filling process, which follows a trend that is distinct from that of natural soft foundations. Based on this observation, a non-stationary random field model is proposed, in which the undrained shear strength decreases along the horizontal filling direction and increases along the vertical deposition direction. The impact of the non-stationary random field model on the vertical bearing capacity of pile foundations is analyzed and discussed with Monte Carlo simulations. The results indicate that the mean and coefficient of variation of the undrained shear strength significantly affect the vertical ultimate bearing capacity of pile foundations, whereas the effects of the random field correlation distance and vertical non-stationary trend coefficient are relatively minor. Under the influence of the horizontal non-stationary trend coefficient, the mean vertical ultimate bearing capacity of pile foundations decreases with the horizontal distance between the pile and the backfill entrance. Moreover, the rate of decrease diminishes linearly with the horizontal non-stationary trend coefficient. A predictive formula for the vertical ultimate bearing capacity of pile foundations is proposed, and its applicability is verified.