Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement.

The cross-river subway in the Hangzhou Bay area often passes through deep, thick, soft soil at the bottom of the river. At the same time, overlying erosion, siltation, and changes in water levels adversely affect the deformation of the subway, thereby causing hidden dangers to its safe operation. Using two-way dynamic triaxial testing, the effects of cyclic loading of the cross-river subway on the soft clay foundation were investigated for the first time, using simulation methodology as the prime objective of the present study. A strain development curve for the soft clay was obtained as a result. Considering the effects of effective confining pressure (p′) and radial cyclic stress ratio (τ_r), an explicit model of accumulative strain on soft clay under cyclic loading of the cross-river subway was established. The results showed that the accumulative axial strain (ε_d) was closely related to p′ and τ_r. Under certain conditions, as p′ and τ_r increased, the ε_d produced by the soil tended to decrease. Furthermore, through non-destructive testing based on nuclear magnetic resonance (NMR), pore distribution and pore size changes in soft clay during cyclic loading were analyzed. It was observed that under the action of the cross-river loading, the large internal soft clay pores were transformed into small pores, which manifested as a significant decrease in the number of large pores and an increase in the proportion of small pores. Lastly, the macroscopic dynamic soil characteristics observed during triaxial testing closely correlated with the microscopic pore size of the soil obtained in the NMR test, which indicated that using pore distribution and pore size changes to describe microscopic changes was a valid method.

In this study, the properties of ultra-high-performance concrete (UHPC) were enhanced by adding modified polyvinyl alcohol (PVA) fibers. The specimens with different curing ages were evaluated in various aspects to investigate the effects of different dosages, lengths, and surface treatments of PVA fibers on the performance of UHPC. The performance was compared with that of steel fiber-reinforced UHPC with the same ratio and multiple dosages. At the same time, the distribution of fibers and the morphology of fibers were observed by a scanning electron microscope, and the mechanism of fiber reinforcement was discussed. The results showed that the mechanical properties were significantly affected by the fiber dosage, length, and surface treatment. Based on the test results, the optimum PVA fiber addition can increase the compressive strength and flexural strength by 12.0% and 6.0% compared to the control UHPC without fibers. A comprehensive evaluation was carried out and indicated that the optimum PVA fiber addition has the potential to replace 0.5% steel fiber in certain conditions.

Cement asphalt mortar (CAM) in high‐speed lines is subjected to varying environmental conditions, which lead to the deterioration of CAM. The nanoparticles of nano‐SiO₂ and nano‐TiO₂ are added to improve the performance of CAM, and the temperature sensitivity of CAM with nanoparticles is primarily studied in this paper. The flexural strength and compressive strength of CAM with and without nano‐SiO₂ and nano‐TiO₂ were measured and compared to study the effect of nanoparticles on the performance of CAM subjected to different temperatures. Temperature sensitivity factors of flexural and compressive strength based on Arrhenius model were proposed to evaluate the temperature dependence of CAM by nanoparticles. Furthermore, the microstructure of hardened CAM was studied using scan electronic microscope (SEM) to investigate the mechanism of temperature sensitivity of CAM with and without nanoparticles. The results indicated that the flexural and compressive strength of CAM with and without nanoparticles decreased with the increasing temperature; however, the decreasing rate of strength with temperature was different in CAM with and without nanoparticles. The reduction in the decreasing rate of strength was obtained by using nano‐SiO₂ rather than nano‐TiO₂ in CAM. Based on the SEM results, the free asphalt was seen to be greatly reduced in the CAM with nano‐SiO₂, suggesting a positive effect of nano‐SiO₂ on the mitigation of temperature sensitivity of CAM.