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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.

Owing to the pavement cracking of heightening and thickening levees (HTL) caused by differential settlement, it is necessary to study the settlement prediction and differential settlement criterion for HTLs. In this study, an HTL was taken as the study object. First, leveling observation and layered settlement observation were adopted, and a generalized settlement prediction model (GSPM) that can describe both S-shaped and hyperbolic settlement⁻time curves was proposed. The physical meanings of GSPM parameters were analyzed. Moreover, the effectiveness of the GSPM was verified using field monitoring data. Second, finite element analysis based on a viscoelastic constitutive model was used to reveal the quantitative relationship between the differential settlement and horizontal stress of the pavement. On this basis, the differential settlement criterion was obtained. The results showed that the GSPM can unify the logistic model and hyperbolic model by introducing two morphological change parameters. In addition, the GSPM can be more widely used, more accurate, and more stable than the traditional logistic model and hyperbolic model. When an asphalt concrete pavement (whose ultimate tensile stress is 0.12 MPa) is used, the corresponding differential settlement criterion is approximately 4.3 cm. The research results can provide a reference for reasonable differential settlement control for HTLs in order to prevent pavement cracking.