Juntao Wu

An improved grouted enlarged toe (GET) precast pile is proposed in this study. The GET technology improves the bearing capacity of precast concrete piles by enlarging the pile toe and solidifying soil surrounding pile shaft. In this study, the soil densification due to the GET technology is first investigated by developing an analytical solution for the soil deformation around the pile shaft, in which the pile toe expansion is generalized as an oblate spheroid. The results of this solution show that the GET pile can alleviate ground surface deformation. Secondly, a multi-layer load transfer (MLT) method is proposed to describe the pile-solidified soil–ground interaction, which allows assessing the performance of the GET pile. In addition, full-scale conventional precast reinforced concrete pipe and GET piles were constructed and load tested at two field sites. The test results demonstrate that the bearing capacity of GET piles is significantly improved compared to the conventional pipe piles, with an increase ranging from 36% to over 60% under varying site and construction conditions. Finally, the load–settlement curves obtained from the proposed MLT method were in good agreement with the results obtained from the static load tests, which confirmed the ability of the MLT method to reliably predict the pile performance under axial loading.

Ground vibrations induced by underground metro systems can adversely affect the surrounding environment, particularly in the context of low-frequency vibrations. Despite the introduction of various mitigation techniques, such as floating slab track (FST) over steel springs or phononic crystal vibration barriers, their efficacy in attenuating low-frequency vibrations is limited. Therefore, the main objective of this study is to develop a novel Double-Resonator Phononic Crystal (DRPC) vibration isolator. The Finite Element Method (FEM) is utilized to analyze the locally resonant bandgap characteristics, and the isolator’s shielding efficacy has been assessed through the transmission spectrum (TS). Furthermore, a detailed parametric study investigates the influence of Young modulus (MOE), density, geometric dimensions, and the damping (ƞ) of different components on the TS and bandgap properties of the DRPC. This study also investigates the impact of damping by analyzing the distribution of energy and velocity within the DRPC. The findings indicate that the DRPC vibration isolator substantially develops a low-frequency bandgap (36 Hz to 151 Hz) alongside a TS closely corresponding with the bandgap, thereby verifying the model’s accuracy. Moreover, among all rubber components, the damping in the middle rubber mainly governs the vibration attenuation efficiency and the bandgap properties of the isolator.

To address the limitations of the conventional low-strain reflected wave method for pile foundation testing, this study proposes an intelligent multi-point vibration acquisition testing model based on machine learning to evaluate the integrity of in-service, high-cap pile foundations. The model’s performance was assessed using statistical error metrics, including the correlation coefficient R², mean absolute error (MAE), and variance accounted for (VAF), with comparative evaluations conducted across different model frameworks. Results show that both the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) consistently achieved high accuracy in identifying the location of the first reflection point in the pile shaft, with R² values greater than 0.98, MAE below 0.41 (m), and VAF greater than 98%. These findings demonstrate the model’s strong predictive capability, test stability, and practical utility in supporting operator decision-making. Among the evaluated models, CNN is recommended for analyzing the integrity of in-service pile foundation based on the multi-point vibration pickup signals and multi-sensor fusion signal preprocessed by the time series stacking method.