Blast Attenuation in Cohesive Soils

Soil-filled wire and geotextile gabions are essential components of defensive infrastructure in military bases, leveraging the attenuating properties of soils to safeguard personnel and critical assets against blast and fragmentation effects. Understanding the behaviour of cohesive soils under extreme loading conditions opens new avenues for design engineers, enabling the development of robust soil constitutive models to address evolving threats effectively.

This study investigates the response of cohesive soils, focusing primarily on kaolin clay due to its homogeneity, widespread availability, and consistent properties. Initially, quasi-static tests were conducted using a triaxial compression (TXC) and an oedometer apparatus to validate how moisture content influences cohesive soil behaviour at low-strain-rates and to gain insight into the dynamic behaviour of cohesive soils under such conditions. High-strain-rate experimental testing was then conducted on approximately 150 kaolin clay specimens using the split-Hopkinson pressure bar (SHPB) apparatus. These tests were performed under both unconfined and confined conditions across varying moisture contents,
from unsaturated to fully saturated states. The analysis of the experimental results reveals the strain rate dependence of cohesive soils and identifies distinct phase behaviour for transmitted and radial stresses, influenced by factors such as strain rate, moisture content and confinement.

Utilising LS-DYNA and the finite element method (FEM), SHPB tests are modelled for comparison against experimental findings. While LS-DYNA, supplemented by smooth particle hydrodynamics (SPH) node modelling, offers valuable insights, significant disparities between modelled and practical results underscore the challenges inherent in accurately simulating the phase behaviour of cohesive soils. This comprehensive exploration of cohesive soil’s high-strain-rate behaviour yields critical insights for engineers, enabling them to effectively adapt defensive strategies to diverse threats and loading scenarios.

Furthermore, by harnessing cutting-edge machine learning models such as the Proper Orthogonal Decomposition (POD) and sparse Proper Generalised Decomposition (sPGD), data-driven parametric models were developed using SHPB test data. These models provide precise predictions of cohesive soil behaviour under specified strain rates and moisture content levels, empowering engineers to swiftly anticipate soil responses to emerging threats and ground conditions.