Jet grouted piles are widely used to reduce post-construction settlement of soft clay roadbeds. Nevertheless, it is easy to cause ground heave due to the jet grouted pile. According to the analytical method and numerical method, a sensitivity analysis of the factors affecting ground heave caused by a single jet grouted pile was performed. It is found that the influence of each parameter on ground heave is in the following order: grout pump pressure > embankment load > soil type (including the cohesion, friction angle, and Young’s modulus) > pile diameter > pile length. Considering the effect of the pump pressure on the ground heave is more significant, based on the analytical method of ground heave caused by a single jet grouted pile combined with the solution of small-deflection bending of a circular thin plate, the calculation method for the suggested limit grout pressure for construction under different embankment heights was established. Suggested values of theoretical grout pump pressure were given to prevent ground heave from harming the pavement of operating highways. This study provides some theoretical basis for the subsequent research on the jet grouted pile.

Rolling dynamic compaction (RDC) has been found to be useful for compaction soils and is now widely used globally. Because RDC is not often used in soft soils with poor engineering properties, field monitoring was used to study the soft clay embankment responses under RDC conditions in this study. Analysis of the monitoring data revealed that the response of the soil occurred mainly in the first 20 passes. Field monitoring revealed a strong correlation between settlement, horizontal displacement, and pore water pressure. The depth of impact of RDC on the soft soil embankment was between 3 and 3.5 m. Although settlement prediction is an important issue for construction, there is a lack of prediction methods for RDC-induced soil settlement. In this study, we used three different machine learning algorithms: random forest regression (RFR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) to predict the total settlement and uneven settlement induced by RDC on the soft soil embankment. The three prediction models were compared, and the predictive accuracy of these models was assessed. This study analyzes and summarizes the effect of RDC application on a soft clay embankment and explores the machine learning method used for settlement prediction based on monitoring data, which provides some methods and ideas for research on the application of RDC on soft soil foundations.

Predicting soil deformation is critical for the success of building construction projects. The traditional methods used for this task, which rely on theoretical calculations and numerical simulations, require detailed information on soil characteristics and geological conditions. These essential details are often challenging to obtain in practical engineering, thereby limiting the accuracy of these methods in building construction contexts. Deep learning (DL) provides a direct approach for modeling soil deformation without having a detailed understanding of the soil properties and geological conditions. However, the existing DL algorithms mainly focus on modeling deformation directly. With advancements in monitoring technology, integrating diverse monitoring data has become crucial for accurately predicting deformation, a need often overlooked in current practices. This paper introduces a monitoring data fusion (MDF) model aimed at enhancing the utilization efficiency of diverse monitoring data. Validated against real-world engineering scenarios, this model significantly outperforms traditional single-feature and multi-feature long short-term memory (LSTM) models. It achieves a mean absolute percentage error (MAPE) of approximately 2.12%, representing reductions of 30% and 63%, and a root mean square error (RMSE) of around 12.5 mm, with reductions of 36% and 77%. Additionally, the DL interpretability method, Shapley additive explanations (SHAP), is utilized to elucidate how various model features contribute to generating predictions.

Cohesive soil containing calcareous nodules (CSCN), as a regional special soil composed of fine‐grained soil and calcareous nodules, is widely distributed in northern Jiangsu and Huaibei area in China. The presence of calcareous nodules leads to significant differences in the physical and mechanical properties compared to cohesive soils and gravelly soils. Currently, the primary method for studying the physical and mechanical properties of CSCN is large size direct shear testing. However, the direct shear test has issues including fixed shear planes, a reducing shear area during testing and stress concentration, which may compromise the accuracy and representativeness of the results. In this study, conducted through medium‐scale triaxial tests, explore the effects of calcareous nodule particle size (2–5 mm, 5–10 mm, 10–20 mm) and water content (10%, 25%, and 30%) on CSCN strength characteristics under different confining pressures. Additionally, discrete element method (DEM) simulations of the medium‐scale triaxial tests were used to reveal the impact of calcareous nodules on soil’s mechanical properties from a microscopic perspective. The results show that stress–strain curves of CSCN samples basically exhibit hardening behavior. Under the same confining pressure, the shear strength of CSCN sample initially decreases and then increases with the increase in calcareous nodule particle size. With increasing water content, both the shear strength and the axial strain at peak values of the CSCN sample consistently decrease. The initial tangent modulus exhibits varying patterns with increasing calcareous nodule particle size under different confining pressures. In contrast, the initial shear modulus remains largely unaffected by water content below 25%, but significantly decreases at 30%. Numerical simulation results show that during the low strain stage, the calcareous nodules are surrounded by fine‐grained soil. The shear stresses are primarily carried by the fine‐grained soil. In the high‐strain stage, the calcareous nodules come into contact with each other to form a skeleton structure in samples to resist the shear.

This paper proposes a real-time calculation method of single point safety factor in soil slope. Firstly, the expressions of a point in the slope safety factor regarding the maximum principal stress $\sigma_{1}$ and the minimum principal stress $\sigma_{3}$ are established by using the mole stress circle expression. It is assumed that the maximum principal stress is the gravity stress of soil and the minimum principal stress is the lateral earth pressure of soil. The safety factor formula can be regarded as a function of the normal stress $(\sigma)$. The minimum value of $\sigma$ can be found by taking the derivative of $\sigma$ in the formula. $\sigma_{min}$ is substituted into the safety factor expression of one point in the slope and the expression of the stability safety factor $(F_{s}) min$ at one point in the slope is established. Finally, based on the results of centrifugal model test of slope excavation, the real-time safety factor of a point in the slope is calculated. Combined with the test results, the calculation method of safety factor in this paper is feasible and reliable.