Probabilistic Classification of Near-Surface Shallow-Water Sediments using A Portable Free-Fall Penetrometer

TL;DR

This paper presents a machine learning-based system for classifying seabed sediments using portable free-fall penetrometer data, achieving over 91% accuracy and providing uncertainty estimates for improved site characterization.

Contribution

It introduces a novel classification approach for PFFP data using machine learning, including uncertainty quantification, enhancing in situ seabed sediment analysis.

Findings

Achieved 91.1% classification accuracy.

Successfully distinguished four sediment classes.

Provided uncertainty estimates for sediment predictions.

Abstract

The geotechnical evaluation of seabed sediments is important for engineering projects and naval applications, offering valuable insights into sediment properties, behavior, and strength. Obtaining high-quality seabed samples can be a challenging task, making in situ testing an essential part of site characterization. Free-fall penetrometers (FFPs) are robust tools for rapidly profiling seabed surface sediments, even in energetic nearshore or estuarine conditions and shallow as well as deep depths. Although methods for interpretation of traditional offshore cone penetration testing (CPT) data are well-established, their adaptation to FFP data is still an area of research. This study introduces an innovative approach that utilizes machine learning algorithms to create a sediment behavior classification system based on portable free- fall penetrometer (PFFP) data. The proposed model leverages PFFP measurements obtained from multiple locations, such as Sequim Bay (Washington), the Potomac River, and the York River (Virginia). The results show 91.1% accuracy in the class prediction, with the classes representing cohesionless sediment with little to no plasticity (Class 1), cohesionless sediment with some plasticity (Class 2), cohesive sediment with low plasticity (Class 3), and cohesive sediment with high plasticity (Class 4). The model prediction not only predicts classes but also yields an estimate of inherent uncertainty associated with the prediction, which can provide valuable insight into different sediment behaviors. Lower uncertainties are more common, but they can increase significantly depending on variations in sediment composition, environmental conditions, and operational techniques. By quantifying uncertainty, the model offers a more comprehensive and informed approach to sediment classification