This paper presents an ultrasonic method, based on the nonlinear acoustic mixing of coda waves with lower-frequency swept pump waves, for providing an efficient global detection of small cracks in cementitious materials. By simultaneously comparing, forboth uncracked and cracked mortars, the ultrasonic velocity variations and decorrelation coefficients between the unperturbed and perturbed signals with pump amplitude, this method makes it possible to accurately detect cracks with widths of around 20 μm in correlation with velocity variations of approximately 0.01%. The potential influence of certain material parameters such as microscopic damage is also discussed

This review provides an in-depth examination of machine learning applications in assessing concrete durability from 2013 to 2024, with a particular focus on critical degradation mechanisms, including carbonation, chloride-induced deterioration, sulfate attack, frost damage, shrinkage, and corrosion. It underscores the field’s heavy reliance on laboratory-based data and notes the limited use of field data and the scarcity of newly generated datasets. The review reveals that most studies utilize existing literature-based datasets, with few contributing novel data and limited open access to these databases, which hampers broader validation and application. The review classifies the features analyzed in studies into categories such as mixture proportions, engineering properties, exposure conditions, test parameters, and chemical compositions, highlighting a growing emphasis on chemical compositions. Modeling approaches are predominantly standalone, though ensemble and hybrid models are increasingly prevalent, with ensemble models showing particularly strong performance in recent years. High accuracy is observed across studies, with ensemble models, neural networks, and hybrid models leading in performance. Furthermore, the review stresses the growing importance of model explainability, noting that model-agnostic methods like SHAP are frequently used and that the focus on explainability has increased. To propel the field forward, the review advocates for the development of diverse new datasets that include both the chemical and physical properties of various mix ingredients and improved data-sharing practices. It recommends adopting a multi-task learning approach to simultaneously address multiple deterioration mechanisms, which can yield deeper insights and support the creation of more durable concrete structures.