Review of Research on Prediction Models for Residual Life of Concrete Structures

The performance degradation of concrete structures directly impacts their safety. As such, accurately predicting their remaining service life is critical for effective operation and maintenance management. This paper reviews the key factors influencing the degradation of concrete structures, providing a comprehensive summary of current research on deterioration mechanisms, steel corrosion, crack propagation, and chloride ion penetration. It also compares the advantages and limitations of physical, empirical, statistical, and machine learning models used for life prediction. A critical aspect highlighted in this paper is the importance of model validation based on real-world field data, which can more effectively determine the applicability of prediction models in actual engineering practice. Model validation incorporates evaluation metrics like sensitivity analysis to gauge how fluctuations in input parameters, such as temperature, influence life prediction models and thereby reveal the uncertainties inherent in complex engineering environments. Currently, life prediction models are widely applied to infrastructure projects like bridges and tunnels. By incorporating environmental factors such as chloride ion concentration, temperature, and humidity, as well as real-time monitoring data, these models effectively predict the remaining service life, aiding engineers in developing optimized maintenance strategies. However, current models still face challenges in terms of data requirements and accuracy. Future research should focus on the integration of hybrid models and intelligent technologies. By leveraging the combined strengths of physical and data-driven approaches, hybrid models can enhance prediction accuracy. Additionally, intelligent technologies and real-time monitoring will help dynamically update and optimize models, resulting in more precise and efficient life predictions.