Investigation of deformation behaviour of uniaxially loaded sand grains using a novel high-resolution imaging apparatus and ensemble machine learning models

Abstract

ABSTRACT In geotechnical engineering, the time-dependent behaviour or ageing behaviour is vital for applications such as earthwork compaction and liquefaction potential assessment. This study introduces a novel test apparatus to understand micromechanical factors and deformations at grain contacts. Using a non-contact Digital Image Correlation (DIC) technique, deformations were measured with a 10 μϵ spatial resolution. This enabled quantification of grain creep and contact maturing deformations, surpassing previous experimental methods. To model this complex behaviour, Machine Learning (ML) models, including an artificial neural network (ANN) and long-short term memory neural network (LSTM), were used, achieving a 1-2% error rate with experimental results. The integration of ML offers a promising tool for predicting long-term grain strains, enhancing the assessment of structures' serviceability with the studied materials.