Modelling nonlinear shear creep behaviour of a structural adhesive using deep neural networks (DNN)

Abstract

Structural adhesives have seen a surge in applications for bonding Fibre reinforced polymers to strengthen structures. However, being polymeric materials, they can exhibit viscoelasticity, leading to creep, which can adversely affect bonded strengthening systems. This paper delves into the nonlinear shear creep behaviour of a typical structural adhesive using a machine learning approach. A deep neural networks (DNN) model was trained using a dataset of 7176 data points obtained from 25 sets of butt joint shear creep tests performed at various stress levels and temperatures. The DNN model successfully captures the complex relationship between temperature, stress, time, and shear creep with a performance value of R2 larger than 0.99. The creep compliance curves predicted by the model are in close agreement with the experimental results, highlighting the ability of the DNN model to effectively model the complex nonlinear shear creep behaviour, which is difficult to achieve with traditional physics-based viscoelastic models. The SHapley Additive exPlanations analysis further identified temperature as the most significant factor influencing the creep, followed by stress and time. The constructed DNN model and its corresponding analyses can provide a valuable reference for the structural adhesive to limit the negative effects of creep in practical applications.