Investigating the dynamic response patterns and failure modes of concrete gravity dams subjected to strong earthquakes is a pivotal area of research for addressing seismic safety concerns associated with gravity dam structures. Dynamic shaking table testing has proven to be a robust methodology for exploring the dynamic characteristics and failure modes of gravity dams. This paper details the dynamic test conducted on a gravity dam model on a shaking table. The emulation concrete material, featuring high density, low dynamic elastic modulus, and appropriate strength, was meticulously designed and fabricated. Integrating the shaking table conditions with the model material, a comprehensive gravity dam shaking table model test was devised to capture the dynamic response of the model under various dynamic loads. Multiple operational conditions were carefully selected for in-depth analysis. Leveraging the dynamic strain responses, the progression of damage in the gravity dam model under these diverse conditions was thoroughly examined. Subsequently, the recorded acceleration responses were utilized for identifying dynamic characteristic parameters, including the acceleration amplification factor in the time domain, acceleration response spectrum characteristics in the frequency domain, and modal parameters reflecting the inherent characteristics of the structure. To gain a comprehensive understanding, a comparative analysis was performed by aligning the observed damage development with the identified dynamic characteristic parameters, and the sensitivity of these identified parameters to different levels of damage was discussed. The findings of this study not only offer valuable insights for conducting and scrutinizing shaking table experiments on gravity dams but also serve as crucial supporting material for identifying structural dynamic characteristic parameters and validating damage diagnosis methods for gravity dam structures.

Modal parameter estimation is crucial in vibration-based damage detection and deserves increased attention and investigation. Concrete arch dams are prone to damage during severe seismic events, leading to alterations in their structural dynamic characteristics and modal parameters, which exhibit specific time-varying properties. This highlights the significance of investigating the evolution of their modal parameters and ensuring their accurate identification. To effectively accomplish the recursive estimation of modal parameters for arch dams, an adaptive recursive subspace (ARS) method with variable forgetting factors was proposed in this study. In the ARS method, the variable forgetting factors were adaptively updated by assessing the change rate of the spatial Euclidean distance of adjacent modal frequency identification values. A numerical simulation of a concrete arch dam under seismic loading was conducted by using ABAQUS software, in which a concrete damaged plasticity (CDP) model was used to simulate the dam body’s constitutive relation, allowing for the assessment of damage development under seismic loading. Utilizing the dynamic responses obtained from the numerical simulation, the ARS method was implemented for the modal parameter recursive estimation of the arch dam. The identification results revealed a decreasing trend in the frequencies of the four initial modes of the arch dam: from an undamaged state characterized by frequencies of 0.910, 1.166, 1.871, and 2.161 Hz to values of 0.895, 1.134, 1.842, and 2.134 Hz, respectively. Concurrently, increases in the damping ratios of these modes were observed, transitioning from 4.44%, 4.28%, 5.42%, and 5.56% to 4.98%, 4.91%, 6.61%, and 6.85%%, respectively. The correlation of the identification results with damage progression validated the effectiveness of the ARS method. This study’s outcomes have substantial theoretical and practical importance, facilitating the immediate comprehension of the dynamic characteristics and operational states of concrete arch dam structures.

Numerical simulations, unlike experimental studies, eliminate material and setup costs while significantly reducing testing time. In this study, a random distribution program for steel-recycled polyethylene terephthalate hybrid fiber recycled concrete (SRPRAC) was developed in Python (3.11), enabling direct generation in Abaqus. Mesoscopic simulation parameters were calibrated through debugging and sensitivity analysis. The simulations examined the compressive failure mode of SRPRAC and the influence of different factors. Results indicate that larger recycled coarse aggregate particle sizes intensify tensile and compressive damage in the interfacial transition zone between the coarse aggregate and mortar. Loading rate strongly affects outcomes, while smaller mesh sizes yield more stable results. Stronger boundary constraints at the top and bottom surfaces lead to higher peak stress, peak strain, and residual stress. Failure was mainly distributed within the specimen, forming a distinct X-shaped damage zone. Increasing fiber content reduced the equivalent plastic strain area above the compressive failure threshold, though the effect diminished beyond 1% total fiber volume. During initial loading, steel fibers carried higher tensile stresses, whereas recycled polyethylene terephthalate fibers (rPETF) contributed less. After peak load, tensile stress in rPETF increased significantly, complementing the gradual stress increase in steel fibers. The mesoscopic model effectively captured the stress–strain damage behavior of SRPRAC under compression.

The soil squeezing effect of pile groups may cause displacements and deformation at the pile tops and ground surface around piles. In severe cases, it can cause problems such as broken piles, cracking of adjacent buildings or cracking of pipes. Artificial intelligence provides a new way to predict horizontal displacements of the pile tops and ground surface around piles caused by soil squeezing effect. The adaptive boosting (AdaBoost) algorithm was applied to the back propagation (BP) neural network model to form the Adaboost-BP model, which improved the learning ability of the BP neural network. For small sample datasets, the prediction accuracy of AdaBoost-BP model, Random Forest (RF) model and Deep Neural Networks (DNN) model is higher than that of BP model. For large sample datasets, the prediction accuracy of various models has improved, but the BP model is lower than that of other models. Analysis shows that the horizontal distance and angle between the center of the bearing platform and the center of the pile tops (or ground surface monitoring points) are the two most important influencing factors. The resting time is also an important influencing factor. Moisture content, relative density, and internal friction angle have a more significant influence on the horizontal displacements of the pile tops and ground surface around piles than other soil property indexes. Quantile regression analysis shows that the horizontal displacements is negatively correlated with the horizontal distance, and positively correlated with the rest time and moisture content. The prediction accuracy of machine learning algorithms (such as DNN) is higher than that of the cylindrical hole expansion method.

In order to study the squeezing effect of static press large‐diameter single pile and piles group in layered soil, field tests of static press large‐diameter pipe piles were carried out based on a project under construction, and numerical simulations of the squeezing effect of single piles and piles group were conducted using finite element software. It is shown that during the process of pile penetration, the pore water pressure in the soil surrounding the pile rapidly increases to a higher initial value. Subsequently, the excess pore pressure will rapidly dissipate and gradually stabilize. The simulated and measured values of pile top displacement show a pattern of larger displacement at the pile top when the pile is first penetrated, and smaller displacement at the pile top when the pile is penetrated later. The measured horizontal displacement of the soil layer at each observation point of the group of 7 piles showed a turning point at a depth of about 2.5 m and fluctuated with increasing depth. The measured displacement reached its maximum value between 25 and 30 m and then rapidly decreased. The finite element simulation results of squeezing effect of the group of 20 piles show that the squeezing effect around the pile is very obvious within the depth range of the pile length. The horizontal displacement of the soil below the pile length rapidly decreases, and the maximum horizontal displacement of the soil at different depths around the pile mainly occurs at the surface. In addition, the reasons for the errors between the finite element simulation values and the measured values were analyzed.