Fracture behaviour assessment of high-performance fibre-reinforced concrete at high strain rates using interpretable modelling approaches

Quang Dang Nguyen; Khoa Tan Nguyen; Tuan Kiet Tran; Kihak Lee; An Thao Huynh

doi:10.1016/j.heliyon.2024.e24704

Fracture behaviour assessment of high-performance fibre-reinforced concrete at high strain rates using interpretable modelling approaches

Heliyon. 2024 Jan 17;10(2):e24704. doi: 10.1016/j.heliyon.2024.e24704. eCollection 2024 Jan 30.

Authors

Quang Dang Nguyen¹, Khoa Tan Nguyen², Tuan Kiet Tran³, Kihak Lee⁴, An Thao Huynh⁵

Affiliations

¹ Centre for Complex Systems, Faculty of Engineering, The University of Sydney, New South Wales, Australia.
² Institute of Research and Development, Duy Tan University, 550000, Da Nang, Viet Nam.
³ Department of Civil Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, 700000, Viet Nam.
⁴ Deep Learning Architectural Research Center, Department of Architectural Engineering, Sejong University, Seoul, 05006, South Korea.
⁵ School of Built Environment, Engineering and Computing, Leeds Beckett University, City Campus, Leeds, LS1 3HE, UK.

Abstract

High-performance fibre-reinforced concrete (HPFRC), a type of cementitious composite material known for its exceptional mechanical performance, has widespread applications in structures exposed to severe dynamic loading conditions. However, understanding nonlinear HPFRC fracture behaviour, particularly under high strain rates, remains challenging given the complexities of assessment procedures and cost-intensive nature of experiments. This study presents an interpretable framework for modelling and analysing HPFRC fracture strength at high strain rates. A wide range of machine learning methods, including ensemble techniques, were employed to capture multivariate effects of eight essential input features (e.g., mortar compressive strength, fibre physical and mechanical properties, cross-sectional area, and strain rate) on fracture strength response. To assess the derived models, a novel evaluation procedure was proposed involving a data-based analysis, employing established metrics (i.e., coefficient of determination, root mean squared error, and mean absolute error via K-fold cross-validation) and a domain experts-involved evaluation utilising global sensitivity analysis to discern first-order and higher-order interactions among input factors. The proposed approach efficiently yielded both quantitative and qualitative insights into crucial input factors governing HPFRC fracture strength with limited experimental data. The obtained findings highlight the significance of multivariate effects, such as the interaction between strain rate and fibre tensile strength, and between fibre volume and fibre diameter, on fracture behaviour. The proposed interpretable framework aims to provide a powerful tool for proactive material failure analysis by understanding fracture behaviour and identifying potential weak and strong interactions among input factors of HPFRC-based samples. Moreover, the utilisation of the proposed approach enables researchers and civil engineers to efficiently focus on the most critical input parameters during the early design stage and ensuring the structural integrity and safety of HPFRC-based constructions.

Keywords: Fracture strength analysis; Global sensitivity analysis; High-performance fibre-reinforced concrete; Interpretable approach; Machine-learning-based modelling; Proactive failure analysis.