Yanxia Liu

With the rapid development of artificial intelligence (AI) technologies, machine learning (ML) has been widely applied in concrete material design, performance prediction, and intelligent structural engineering. Compared with traditional empirical approaches, ML can efficiently establish complex nonlinear relationships among concrete mix proportions, environmental factors, and performance indicators, thereby improving prediction efficiency, reducing experimental costs, and enabling multi-objective optimization of mix proportions. This paper systematically reviews the recent research progress of ML technologies in the field of concrete engineering, with particular emphasis on typical algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Their applications in predicting workability, mechanical properties, durability performance, and mix proportion optimization are comprehensively summarized. In addition, recent advances in ML applications for crack detection and digital twin technologies are also discussed. Moreover, deep learning and computer vision (CV) technologies have significantly promoted the development of crack identification and structural health monitoring, whereas the integration of digital twin and Internet of Things (IoT) technologies has further expanded the application of ML in smart infrastructure. Finally, the current challenges associated with data quality, model interpretability, and engineering applications are summarized, and future research directions are discussed. Overall, by linking algorithm choice to specific concrete performance-prediction tasks, this review clarifies the conditions under which ML delivers reliable results and provides a structured reference for both researchers and practitioners. The comparative analysis indicates that ensemble tree-based models—particularly random forest and gradient-boosting variants such as XGBoost—together with well-tuned neural networks consistently achieve the highest predictive accuracy across most concrete properties, with reported test-set coefficients of determination commonly between 0.90 and 0.99, whereas limited data availability, inconsistent validation protocols, and restricted model interpretability remain the principal obstacles to engineering deployment.

The development of polymeric materials for the repair and reinforcement of damaged sites in water has many practical applications, especially in ocean engineering. However, it is difficult to construct an anticorrosion coating in water. In addition, curing kinetics, which are the key to enhance the performance of coatings, seem to hardly be observed and regulated in an underwater condition. Herein, a novel underwater in situ repairing coating was prepared. Meanwhile, electrochemical impedance spectroscopy (EIS) was applied to observe its curing behavior underwater. Adhesion tests showed that the coatings cured underwater had good adhesion to different substrate surfaces and the ideal ratio of curing agent to epoxy resin was 0.6. Long-term anticorrosive tests demonstrated that the coatings had an excellent anti-corrosion performance. The viscosity changes in different curing stages were well reflected by frequency response characteristics from Bode and Nyquist curves by EIS. Two equivalent electrical circuits were selected to simulate the impedance date at the initial and final curing stage. A formula was put forward to evaluate the curing degree during the curing process. Finally, the effects of temperature and the ingredient ratio on the reaction rate and curing degree were also investigated here. This underwater in situ repairing coating may find applications in many offshore engineering structures in marine environments, and the EIS technique has attractive development and application prospects when observing the curing information of thermosetting resin systems under special circumstances.