- Authors
- Khalil AL-Bukhaiti; Yanhui Liu; Shichun Zhao; Han Daguang
- Journal
- International Journal of Protective Structures, 2024
- DOI
- 10.1177/20414196241271447
- Citations
- 1 (OpenAlex, 3 Aug 2026)
Abstract
Externally bonded carbon fiber-reinforced polymer (CFRP) composites have been widely adopted for strengthening and repairing aging concrete structures. However, premature fracture at the CFRP-concrete interface remains a significant concern, necessitating a better understanding of the interfacial bond behavior and fracture mechanisms. Traditional experimental studies and numerical simulations have limitations in accurately capturing the complex nonlinear factors influencing this fracture behavior. This paper explores the novel application of machine learning (ML), specifically artificial neural networks (ANNs), for predicting the fracture strength of CFRP-reinforced concrete (CFRP-RC) members. ANNs enable mapping the intricate relationships between input parameters (e.g., CFRP/concrete properties, geometric configurations) and fracture responses through iterative training on experimental data. Key aspects include comprehensive data collection, preprocessing techniques, feature selection methods like mean impact value (MIV) analysis, and neural network architecture design. The paper highlights successful ANN applications in predicting CFRP-RC bond strength, shear capacity, and overall fracture behavior. A systematic approach is proposed for developing robust ANN models tailored to CFRP-RC fracture prediction, encompassing data curation, input variable screening, network training/validation, and model interpretability analyses. By leveraging ML’s ability to handle multifaceted nonlinearities, this data-driven framework offers a powerful alternative to traditional methods, potentially enhancing the design, analysis, and performance evaluation of CFRP-strengthened concrete structures.
Cite this work
@article{han2024cfrprcfractures,
title = {CFRP-RC fracture strength prediction: A novel approach using deep learning},
author = {Khalil AL-Bukhaiti and Yanhui Liu and Shichun Zhao and Han Daguang},
journal = {International Journal of Protective Structures},
year = {2024},
doi = {10.1177/20414196241271447},
}
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Author identity: ORCID 0000-0003-3787-963X · Google Scholar