Carbonation is one of the critical issues affecting the durability of reinforced concrete. Evaluating the depth of concrete carbonation is of great significance for ensuring the quality and safety of construction projects. In recent years, various prediction algorithms have been developed for evaluating concrete carbonation depth. This article provides a detailed overview of the existing prediction models for concrete carbonation depth. According to the data processing methods used in the model, the existing prediction models can be divided into mathematical curve models and machine learning models. The machine learning models can be further divided into the following categories: artificial neural network model, decision tree model, support vector machine model, and combined models. The basic idea of the mathematical curve model is to directly establish the relationship between the carbonation depth and age of concrete by using certain function curves. The advantage of the mathematical curve model is that only a small amount of experimental data is needed for curve fitting, which is very convenient for engineering applications. The limitation of the curve model is that it can only consider the influence of some factors on the carbonation depth of concrete, and the prediction accuracy cannot be guaranteed. The advantage of using the machine learning model to predict the carbonation depth of concrete is that many factors can be considered at the same time. When there are sufficient experimental data, the trained machine learning model can give more accurate prediction results than the mathematical curve model. The main defect of the machine learning model is that it needs a lot of experimental data as training samples, so it is not as convenient as the mathematical curve model in engineering applications. A future research direction may be to combine a machine learning model with a mathematical curve model to evaluate the carbonation depth of concrete more accurately.

Concrete bridges, as a vital component of modern transportation infrastructure, have their structural durability directly tied to safety and service life. In recent years, with the aging of bridge structures and increasingly complex environmental conditions, various durability-related deteriorations have become more prominent, significantly affecting structural performance and maintenance costs. This paper presents a systematic analysis of concrete carbonation as a key chemical process and its impact on durability-related pathologies. Particular attention is given to the formation mechanisms and influencing factors of critical deterioration modes such as cracking, reinforcement corrosion, and freeze–thaw damage. A multi-level prevention and mitigation strategy is proposed, encompassing optimized structural material design, strict construction quality control, and effective maintenance and repair techniques. The study concludes that the durability issues of concrete bridge structures exhibit a strong multi-factor coupling effect and proposes a core durability assurance framework. Finally, the paper briefly outlines emerging trends in intelligent monitoring and digital operation and maintenance, offering insights for future durability management of bridges.

With the continuous improvement in technology, the construction industry is constantly advancing. Traditional concrete can no longer meet modern market demands, making research on new types of concrete imperative. This study reviews the application of common nanomaterials in concrete and their impact on concrete performance. It provides a detailed explanation of the characteristics of three common nanomaterials: nano-silica, nano-calcium carbonate, and carbon nanotubes. This study analyzes how these materials improve the microstructure, accelerate hydration reactions, and enhance interfacial transition zones, thereby enhancing the mechanical properties, durability, and workability of concrete. For conventional engineering projects, nano-calcium carbonate is the preferred choice owing to its low cost and its capacity to improve workability and early-age strength. For high-strength and durable structures, nano-silica is selected due to its high specific surface area (ranging from 100 to 800 m²/g) and its superior compactness and impermeability. In the context of intelligent buildings, carbon nanotubes are the most suitable option because of their exceptional thermal conductivity and electrical conductivity (with axial thermal conductivity reaching 2000–6000 W/m*K and electrical conductivity ranging from 10³ to 10⁶ S/cm). However, it should be noted that carbon nanotubes are the most expensive among the three materials. Additionally, this study discusses the issues and challenges currently faced by the application of nanomaterials in concrete and looks ahead to future research directions, aiming to provide a reference for further research and engineering applications of nanomaterials in the field of concrete.

Chloride ion concentration significantly impacts the durability of reinforced concrete, particularly regarding corrosion. Accurately assessing how this concentration varies with the age of structures is crucial for ensuring their safety and longevity. Recently, several predictive models have emerged to analyze chloride ion concentration over time, classified into empirical models and machine learning models based on their data processing techniques. Empirical models directly relate chloride ion concentration to the age of concrete through specific functions. Their primary advantage lies in their low data requirements, making them convenient for engineering use. However, these models often fail to account for multiple influencing factors, which can limit their accuracy. Conversely, machine learning models can handle various factors simultaneously, providing a more detailed understanding of how chloride concentration evolves. When adequately trained with sufficient experimental data, these models generally offer superior prediction accuracy compared to mathematical models. The downside is that they necessitate a larger dataset for training, which can complicate their practical application. Future research could focus on combining machine learning and empirical models, leveraging their respective strengths to achieve a more precise evaluation of chloride ion concentration in relation to structural age.

The compressive strength of concrete is an important parameter in construction practice. At present, there are few reports on the prediction model of the compressive strength of concrete at a super early age. For some engineering vibration analyses, it is very necessary to study the growth law of compressive strength of concrete at a super early age. To this end, a new prediction model is proposed in this work to analyze the variation of compressive strength for the concrete at a super early age. The innovations of this work mainly lie in two aspects. The first innovation is to propose a new compressive strength-age mathematical model to predict the variation of compressive strength more accurately. The second innovation is to develop a new robust regression analysis method to obtain the fitting parameters in the mathematical model more effectively. Using the experimental data of the super early age concrete, the proposed prediction model is compared with the existing power function model and the hyperbolic function model. The results of the comparative study show that the prediction model proposed in this work is more reasonable and reliable. Taking C40 under natural curing as an example, it has been shown from the comparative study that: (1) The total fitting error of the proposed model is approximately 60% of that of the power function model, and approximately 17% of that of the hyperbolic model; (2) The fitting standard deviation of the proposed model is approximately 49% of that of the power function model, and approximately 15% of that of the hyperbolic model; (3) The 28 day strength of concrete predicted by the proposed model is more in line with the actual strength growth law of concrete.