Microstructure-driven prediction of undrained shear strength of deep-sea sediments: A multivariate approach bridging physical-mechanical properties

Abstract

An innovative framework for correlating physical-mechanical properties of deep-sea sediments is established through a comprehensive database integrating microstructural, mineralogical, and geotechnical data from over 300 samples. Advanced cold field emission SEM analyses reveal unique flocculated-laminated microstructures dominated by organic components and smectite-rich clay minerals. Microstructural parameters and relationships between macroscopic and microscopic characteristics are further examined, which enhances the fundamental understanding of the correlations between physical and mechanical properties. Statistical analyses demonstrate strong interdependencies among water content, buoyant unit weight, and void ratio, confirming their equivalence as physical descriptors. Crucially, conventional terrestrial soil models show limited applicability for predicting undrained shear strength in deep-sea environments, particularly underestimating strength parameters by neglecting sediment sensitivity and liquidity index. Through multiple nonlinear regression and the construction of multivariate distribution, predictive models are developed incorporating buoyant unit weight, liquidity index, and sensitivity as key governing factors, achieving superior accuracy compared to existing methods. This investigation advances the understanding of physical-mechanical properties of deep-sea sediments, thus providing critical insights for assessing subsea geo-hazards.

Keywords

Deep-sea sediments / Geotechnical property / Variability / Correlation

CRediT authorship contribution statement

Yu-Min Shi: Writing - original draft, Methodology, Investigation, Formal analysis, Data curation. Fu-Ping Gao: Writing - review & editing, Supervision, Project administration, Investigation, Formal analysis, Conceptualization. Ning Wang: Validation, Investigation, Data curation. Wen-Gang Qi: Validation, Methodology, Formal analysis. Jian-Tao Liu: Validation, Project administration, Formal analysis, Data curation. Jun-Qin Wang: Project administration, Investigation, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors are grateful for the financial support from the National Natural Science Foundation of China (Grant Nos. 12302506, 52201346, 12061160463).

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