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.

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.

Engineering Cementitious Composites (ECC) has gained significant attention in civil engineering due to its excellent tensile strength, crack width control capability, and remarkable ductility. This study examines the influence of the ECC strength and reinforcement on the flexural behavior of ECC slabs through four-point flexural tests. The results demonstrate that ECC is well suited for flexural applications. During flexural tests, the fibers within the ECC provide a bridging effect, allowing the ECC in the tensile zone to sustain a load while developing a dense network of fine microcracks at failure. This characteristic significantly enhances the crack resistance of ECC slabs. Despite the relatively low flexural capacity of unreinforced ECC slabs, they achieve 59.2% of the capacity of reinforced ECC slabs with a reinforcement ratio of 1.02%, demonstrating the potential for using unreinforced ECC in low-load-bearing applications. Further findings reveal that high-strength ECC (HSECC) not only improves the flexural capacity of unreinforced ECC slabs but also maintains excellent ductility, enabling a better balance between the load-bearing capacity and deformation ability. However, while reinforcement enhances both the flexural capacity and energy absorption, an excessively high reinforcement ratio significantly compromises ductility. Additionally, this study proposes a simplified calculation method for the flexural capacity of ECC slabs based on the axial force and moment equilibrium, providing theoretical support for their design and application. Due to their excellent flexural behavior, ECC slabs exhibit significant potential for use in flexural components such as bridge deck slabs and link slabs in simply supported beam bridges. With continued research and optimization, their application in engineering practice is expected to become more widespread, thereby improving the cracking resistance and durability of concrete structures.

The interfacial shear performance between ultra-high-performance concrete (UHPC) and normal concrete (NC) is a critical factor in determining the overall performance of composite structures. This paper systematically reviews the research progress on the interfacial shear performance of UHPC-NC, revealing the core mechanisms of bond strength (dominated by mechanical interlocking with chemical bonding as a supplementary factor). It compares the advantages and disadvantages of single-shear, Z-shaped shear, double-shear, and inclined shear tests, clarifying the influence patterns of key parameters such as interface roughness, matrix wetness, curing conditions, and fiber content. This study found that interface treatment is the most significant factor in improving shear strength. Roughening or grooving treatments can increase the strength by more than 40%~80%, while the combination of rebar planting and grooving can further enhance ductility. The matrix wetness (saturated and moist) and UHPC age (within 7 days) need to be strictly controlled to avoid differences in shrinkage stress. Prediction models based on mechanics, finite element analysis, and experimental data each have their advantages and disadvantages and should be selected based on actual working conditions. To address common issues in practical engineering, such as insufficient interface roughness, shrinkage cracking, and fatigue degradation under cyclic loading, it is recommended to adopt composite interface treatment techniques (such as roughening + rebar planting), prestressing design, and optimized fiber distribution (with a steel fiber content of 1.5%~2.5%). This paper provides the theoretical basis and practical guidance for the design optimization and construction control of UHPC reinforcement projects and composite structures.

The use of engineered cementitious composite (ECC) has attracted extensive attention in recent years because of the highly enhanced ductility owing to its unique strain-hardening behavior. In this paper, an electromechanical impedance-based technique is used to monitor the structural damage of RC beams strengthened with an ECC layer at the tensile zone. To achieve this purpose, three specimens are tested under bending loads to evaluate the proposed damage detection methodology. Five externally bonded PZT transducers are uniformly distributed at the surface of the ECC layer of the beams to measure the output conductance signatures in a healthy state and in different damage scenarios induced by different load levels. Test results showed that discrepancies exist between the signals measured in the intact state and each damage state, which can be used to evaluate the structural integrity changes. To assess the damage of ECC-concrete composite beams quantitatively, the statistical scalar index-root mean square deviation (RMSD) is used as the index, which can be calculated from the variations of conductance measurements of PZT sensors. The damage index values of the uniformly distributed PZT sensors provided cogent evidence of damage and revealed the evolution of structural damage. The crack patterns of beams at different damage levels are compared with the damage index values, and it shows the damage location can be derived from the measured conductance signatures of an array of PZT transducers.