The construction industry is essential to the development and economy, but is also the largest generator of construction and demolition waste (CDW). While efforts are made to minimize such generation, the construction industry has been developing applications for it in the form of aggregates to replace the commonly used natural aggregates. However, in structural applications, it is necessary to ensure that the properties of concrete produced with CDW, as recycled aggregates (CDW-concrete), guarantee adequate performance and do not put the structure at risk. For this, one of the alternatives is improving the properties of CDW aggregates through carbonation, a process called carbonate curing or accelerated carbonation. In this sense, this paper aims to investigate the carbonation of CDW aggregates, clarifying how this process occurs, the existing carbonation methods, the main properties that affect this process, and their influence on the properties of recycled aggregates and the CDW-concrete. To this end, the SREE (systematic review for engineering and experiments) method was used to search and analyze scientific manuscripts published without a time limit. The results revealed that the most widely used method for carbonate curing is recommended by Chinese standard GB50082, and highlighted the need for further research to investigate the CDW-concrete, focusing on its eco-friendly potential to capture CO₂ from the atmosphere.

Advances and innovations in science and engineering have been increasingly supported by nanotechnology, and the modification of cementitious materials by nanoengineering is an expanding field. With this perspective, this paper aims to elucidate the behavior of steel bars in concrete with the addition of carbon nanotubes (CNTs) as a function of the characteristics of the cement-based material, the dispersion techniques and dosage of CNTs, the bond tests and specimen geometry, and the rebar characteristics. To reach this proposed goal, the ProKnow-C methodology was applied to select the most relevant publications from the last ten years, and then seven articles were fully analyzed. The results of the present systematic review of the literature revealed both consolidated knowledge and gaps to be filled in future research, as the need to study the chemical effect of adding these nanomaterials for improving steel–concrete adhesion, the bonding of thin bars in concrete, and the real influence of anchorage length on the steel–concrete bond, regardless of the use of CNTs, is vital.

The durability of reinforced concrete (RC) structures is strongly influenced by carbonation, a phenomenon governed by material and environmental interactions. This study applied machine learning (ML) techniques—Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs)—to predict carbonation depth using a synthetic dataset of 20,000 instances generated from the validated Possan equation. Model performances were evaluated across multiple scenarios, with compressive strength and exposure time identified as the most influential features, while relative humidity and exposure conditions had intermediate effects. SVR consistently captured linear and nonlinear trends, the ANN achieved the highest R² values but showed minor overestimations, and RF exhibited lower adaptability to feature variations. The results highlight the applicability of ML models for durability assessments, particularly under complex conditions where traditional approaches are limited. Moreover, this study reinforces the strategic value of synthetic datasets in developing predictive models when experimental data collection is time-consuming or impractical. The methodologies developed here can be extended beyond carbonation modeling to other deterioration processes, supporting data-driven strategies for maintenance planning and resilience design in RC structures.

Investigations into the fire resistance of high-strength concrete (HSC) is extremely important to optimize structural design in construction engineering. This work describes the influence of polypropylene fibers on the mechanical properties and durability of HSC at high temperatures (25, 100, 200, 400, 600 and 800 °C). HSC specimens with 2 kg/m³ composed of polypropylene fibers are tested in a temperature range of 25 to 800 °C, followed by microstructural analysis. In addition, a statistical analysis is designed to identify the effect of factors, namely temperature and polypropylene fibers, and their interactions on mechanical properties and water absorption, electrical resistivity, mass loss and ultrasonic velocity. Most of the properties are improved by the incorporation of fibers, obtaining highly predictable regression models. However, the polypropylene fibers reduce compressive strength but improve the residual mechanical properties up to 400 °C.