A series of cyclic triaxial tests were conducted on marine soft clay deposits to establish and validate a predictive model for cumulative plastic strain. Additionally, a numerical model of particle flow code in cyclic triaxial tests was developed. The effects of confining pressure, moisture content, and dynamic stress ratio on the dynamic properties of marine soft clay were examined, considering factors such as volume deformation and microscopic failure patterns. The results indicated that both the predictive model and numerical model showed strong consistency with the experimental data. The plastic strain of marine soft clay was influenced by moisture content, stress ratio, and confining pressure in a consistent manner, with moisture content being the primary factor. A predictive model for the cumulative plastic strain of marine soft clay was successfully established, allowing for the evaluation of dynamic properties from the perspective of cumulative plastic strain. During the loading process in the numerical model, microcracks within the soil structure gradually compacted, and the main displacement of the specimen extended from the vertical center axis to the sides, ultimately resulting in shear damage.

Accurately predicting the setup of jacked piles in marine soft clays is crucial for effective construction, load- bearing design, and maintenance of offshore foundations. This paper integrated UMAT subroutines into the ABAQUS platform using two numerical integration methods: the cutting plane algorithm (CPA) and the NewtonRaphson iterative algorithm (NRIA), to simulate the entire life cycle of jacked piles in marine soft clays. The study incorporates the advanced elastoplastic constitutive model (S-CLAY1S) and the elastoviscoplastic constitutive model (ANICREEP), addressing soil fabric anisotropy, structural effects, and, specifically, soil creep effects in the ANICREEP model. A two-dimensional axisymmetric model is established for jacked piles in marine soft clays, involving unloading and consolidation stages, followed by static load tests on test piles at various post- installation rest periods to assess their time-dependent bearing performance. Finite element modeling enables simulations of field and laboratory pile tests, validating models against measurements. Parameter analysis includes variations in excess pore water pressure (EPWP), ultimate skin friction resistance, and pile bearing capacity in both soil models, examining the impact of initial soil structure ratio on pile performance. Key findings reveal differences in EPWP dissipation rates and long-term bearing capacity evolution between elastoplastic and elastoviscoplastic soils, highlighting the ANICREEP model's capability to capture both short-term and creep- induced long-term effects. Integrating complex soil mechanics into ABAQUS enhances the ability to predict and optimize jacked pile performance in various geotechnical engineering applications.

The traditional cement-based stabilization cannot effectively stabilize the marine soft clay under submerged conditions. In order to solve this problem, the enhancement of cement-stabilized marine soft clay was investigated in this study by adding the ionic soil stabilizer (ISS) and polyacrylamide (PAM). For this purpose, varying contents of ISS and PAM (ISS-P) were added into cement-stabilized marine soft clay and subjected to curing under submerged conditions. Atterberg limits tests, direct shear tests, unconfined compression strength (UCS) tests, water-stability tests, scanning electron microscopy analysis, and X-ray diffraction analysis were carried out. The results show that using 1.8% ISS and 0.9% PAM as the optimal ratio, the cohesion, internal friction angle, UCS, and water-stability of the samples increased by 182.7%, 15.4%, 176.5%, and 368.5% compared to the cement-stabilized soft clay after 28 d. The increment in soil cohesion with increasing ISS-P content was more apparent than that in the internal friction angle. The combined action of ion exchange attraction and electrostatic adsorption altered the failure characteristics of the samples, resulting in localized micro-cracking and multiple failure paths. Increasing the content of ISS-P strengthened the skeletal structure of soil, reduced inter-particle spacing, and enhanced the water-stability. Additionally, ISS promotes the hydration of cement and compensates for the inhibitory effect of PAM on early cement hydration. ISS-P can effectively enhance the strength and stability of submerged cement-based stabilized marine soft clay.

This study endeavors to realize the concurrent utilization of marine soft clay (MSC) and industrial waste, specifically calcium carbide residue (CCR) and fly ash (FA), through a series of experimental investigations. The optimal ratio between CCR and FA, as well as the efficacy of the composite agent (CF-1), were examined, and an empirical equation associating the unconfined compressive strength (q(u)) of stabilized MSC was developed through unconfined compressive strength (UCS) tests. Microscopic analyses, including X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS), were employed to unveil the intrinsic mechanisms underlying CF-1 stabilized MSC. Subsequently, the suitability of CF-1 solidified MSC as a roadbed filler was ascertained through laboratory tests. Results revealed the optimum CCR:FA ratio for CF-1 to be 4:1, demonstrating superior curing effects compared to individual components such as Portland cement (PC), CCR, and FA, with commendable environmental and economic benefits. The developed empirical equation exhibited effectiveness in predicting the q(u) of CF-1 solidified MSC under varying curing dates (T) and dosages (W-g) conditions. Characterization through XRD, SEM, and EDS identified the primary products formed within the stabilized MSC matrix with CF-1 as comprising calcium-silicate-hydrate (C-S-H) gel, calcium-aluminate-hydrate (C-A-H) gel, and a minor amount of calcite. As T and W-g increased, the reduction in pores between soil particles enhanced the structural integrity and macro-strength of the cured MSC. The failure pattern of CF-1-solidified MSC elementary samples depended on the CF-1 dosage and curing duration. The solidification mechanism of CF-1 on MSC involved pozzolanic, ion exchange, and carbonation reactions. CF-1 solidified MSC satisfied all the specified requirements for roadbed filler in the relevant code, demonstrating substantial potential for in-situ solidification projects involving MSC.

In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying-wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.