Machine learning based prediction and analysis of bond strength between steel reinforcement and geopolymer concrete under cyclic loading for seismic applications: a review
The bond behavior between steel reinforcement and geopolymer concrete under cyclic loading is a critical factor for the performance of reinforced concrete structures in seismic regions. Traditional empirical models often fail to capture the complex, nonlinear interactions at the steel concrete interface, particularly under repeated loading conditions. This systematic literature review aims to synthesize and critically analyze existing research on machine learning-driven approaches for predicting and interpreting bond strength in this context. We systematically identified and evaluated studies that develop, validate, or apply machine learning algorithms including artificial neural networks, support vector machines, and ensemble methods to model bond-slip relationships, failure modes, and degradation mechanisms. The review methodology involved a structured search and thematic analysis of peer-reviewed articles, focusing on how these models incorporate key variables such as concrete compressive strength, fiber reinforcement type, confinement conditions, and loading history. Our analysis reveals that Several reviewed studies reported improved predictive performance of machine learning models compared with selected empirical equations. However, differences in datasets, validation strategies, and performance metrics limit direct comparison among studies and prevent definitive conclusions regarding consistent superiority and generalizability., achieving higher accuracy in predicting bond strength under both monotonic and cyclic regimes. Furthermore, we found that feature importance analyses from these models provide new insights into the relative influence of material properties for instance, the critical role of fiber volumetric ratio and lateral confinement in mitigating bond degradation under reversed cyclic loads. The review also identifies significant gaps, including the scarcity of experimental datasets for high-magnitude seismic loading and the limited generalizability of models across different geopolymer mix designs. We conclude that machine learning offers a powerful framework for advancing bond strength prediction in geopolymer concrete systems, but future work must prioritize the development of robust, transferable models trained on more diverse, large-scale cyclic test data. These findings provide a foundation for more reliable seismic design guidelines and inform the selection of machine learning strategies for structural performance assessment.