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Three quarters of the world's significant urban centers are located in coastal regions where clay minerals are prevalent. The microstructures and physico-mechanical properties of clay sediments are highly susceptible to alterations in pore water chemistry. The pore water chemistry in soil changes easily due to groundwater pollution, seawater erosion, degradation, etc. However, there are several inconsistencies in the literature on the change of physical and mechanical properties of clay soils caused by the change of pore water chemical properties, i.e., disparate experimental results were reported for the same soil property, or varying explanations were proposed for the identical experimental results. Currently, the research in this field mainly focuses on the macroscopic behavioral modifications while lacking the explanations of microscopic mechanism. Therefore, this paper presents a comprehensive review of researches conducted over the past four decades on the impact of pore water chemistry (including salt type, salinity, ion valence, pH) on soil properties such as the microstructure, settlement, Atterberg limits, shear strength, conductivity, unsaturated soil mechanics properties and thermal properties. The fundamental mechanisms underlying the varied experimental observations on clay sediments affected by salt and pH are systematically explored, and deficiencies in this field are identified. The observed dispersion in the literatures is attributed to the spatial variation and heterogeneity of soil compositions, different test methods, and insufficiency of the fundamental microscopic mechanisms of the observed soil behaviors, etc. Therefore, it is necessary to further explore the essential mechanisms of the test results in the future to establish a unified conclusion. [ABSTRACT FROM AUTHOR]

In recent times, large-scale infrastructural projects are being constructed on varieties of soil, especially in highly compressible marine clays and soft soil. The coefficient of consolidation (cv) is one of the most important technical parameters used to estimate the consolidation characteristics of the soil. The experimental laboratory techniques used to obtain cv are time-consuming and possess different practical limitations. In this study, a reliable method for predicting cv is presented based on machine learning (ML). The study considered 11 inherent soil variables, among which the least significant variables are discarded using univariate feature selection technique. Different ML models were developed like the random forest, artificial neural network, and support vector machine for nonlinear mapping of the cv using dimensionally reduced independent variables. Verification against experimental data demonstrates that the Random Forest model accurately predicts the cv (with MAE = 0.0231, MSE= 0.00148, and RMSE = 0.03854). Further, a comparative study of the proposed model is presented with available empirical equations and numerically simulated data. Moreover, the strengths and shortcomings of different ML algorithms are also discussed in detail. [ABSTRACT FROM AUTHOR]

Suction bucket installation in pure clay or pure sand is well established. However, in layered soils, particularly in sand over clay soils, the soil plug uplift is considered a major installation risk and feasibility of installation is often questioned. In this paper, the experience from installation of two suction bucket jackets at two offshore wind farms in layered soils is reported. The installation at both sites was successful and no excess plug uplift was observed. The transfer of suction to the clay-sand interface is believed to have contributed to the success of the installations. The two cases presented in this paper provides confidence in suction bucket foundation installation in similar soil conditions. [ABSTRACT FROM AUTHOR]