A comprehensive assessment of the compression index of marine seabed soils

Abstract

The compression index (C_c) is a crucial parameter for evaluating the consolidation settlement of marine infrastructure, but measuring it experimentally is challenging. This study presents C_c prediction models for marine seabed soils using linear, nonlinear, and artificial neural network modeling techniques. Large experimental oedometer test results for marine clays, collected from the available literature, are used to develop valuable models based on easily measurable soil properties, applicable to a wide range of marine soils. The initial void ratio and plasticity index have a greater impact on C_c estimation compared to the liquid limit and natural water content. The predictive capacity of the models is validated with independent oedometer test data, confirming the reliability of the results. The proposed models aid geotechnical designers in determining the required C_c for initial settlement assessments for marine infrastructure, resulting in cost and time savings. The predicted C_c values can be further adjusted by conducting traditional consolidation tests on selected seabed samples collected from the coastal site.