As a special geological phenomenon, the character of collapsible loess foundation is collapsible when penetrated by water. This character leads to the soil losing load bearing capacity largely and may lead to foundation failure. Pile is a popular foundation used in collapsible loess. The squeezed branch and plate pile is a new type of pile developed in recent years and has not be used in a project before. In this paper three squeezed branch and plate piles are tested in collapsible loess after immersion processing. The results may be used for reference in similar construction project, and to provide theoretical references for designing of the squeezed branch and plate piles in engineering practice.

Cement mortar is one of the most important components of semiflexible pavement materials; however, the effects of cement mortar formulation on the performance and the grouting rate are rarely studied. Therefore, the optimum formulation of high‐performance cement mortar (HPCM) for different types and contents was studied, and the grouting effect of the cement mortar was studied by rutting tests. The results show that polycarboxylate superplasticizer, expansion admixture, and accelerating admixture have different influences on the workability, the strength, and the drying shrinkage of HPCM, and the working ability of HPCM is good by adding these three admixtures. The strength at 7 days is 1.3 to 4 times that of the existing specifications, and the shrinkage rate is less than 0.2. The HPCM has higher early strength, and the strength development is stable in the later period compared with the other research studies. The semiflexible material has better pavement performance when the grouting rate is greater than 90%.

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 three-point bending fatigue tests were carried out in order to accurately predict the fatigue life of an asphalt mixture based on the plateau value (PV) of the dissipated strain energy ratio (DSER). The relations of the dissipated strain energy (DSE) to the stress-strength ratio, temperature and loading rate were studied, and the constructions of the mathematical models of DSE and DSER were completed based on the change laws of the DSE. The relation of the fatigue life to the PV was determined based on the analysis of damage evolution, based on which the fatigue equation was established and used to predict the fatigue life. The results show that the change laws of DSE and DSER can be well described by the proposed mathematical models. The PV is defined as the average value of the DSER in the second stage and the fatigue life decreases in power function with the increase of PV, based on which the fatigue equation of N_f = A(PV)^B was established, and the established fatigue equation is very close to that is used in the MEPDG. The fatigue equation can well predict the fatigue life asphalt mixture.

This study has investigated the impact of graphene oxide (GO) in enhancing the performance properties of an asphalt binder. The control asphalt binder (60/70 PEN) was blended with GO in contents of 0%, 0.5%, 1%, 1.5%, 2%, and 2.5%. The permanent deformation behavior of the modified asphalt binders was evaluated based on the zero shear viscosity (ZSV) parameter through a steady shear test approach. Superpave fatigue test and the linear amplitude sweep (LAS) method were used to evaluate the fatigue behavior of the binders. A bending beam rheometer (BBR) test was conducted to evaluate the low-temperature cracking behavior. Furthermore, the storage stability of the binders was investigated using a separation test. The results of the ZSV test showed that GO considerably enhanced the steady shear viscosity and ZSV value, showing a significant contribution of the GO to the deformation resistance; moreover, GO modification changed the asphalt binder’s behavior from Newtonian to shear-thinning flow. A notable improvement in fatigue life was observed with the addition of GO to the binder based on the LAS test results and Superpave fatigue parameter. The BBR test results revealed that compared to the control asphalt, the GO-modified binders showed lower creep stiffness (S) and higher creep rate (m-value), indicating increased cracking resistance at low temperatures. Finally, the GO-modified asphalt binders exhibited good storage stability under high temperatures.