In order to achieve low-carbon optimization in the intelligent mix ratio design of concrete materials, this work first constructs a concrete mix ratio database and performs a statistical characteristics analysis. Secondly, it employs a standard back propagation (BP) and a genetic algorithm-improved BP (GA-BP) to predict the concrete mix ratio. The NSGA-II algorithm is then used to optimize the mix ratio. Finally, the method’s accuracy is validated through experiments. The study’s results indicate that the statistical characteristics of the concrete mix ratio data show a wide distribution range and good representativeness. Compared to the standard BP, the fitting accuracies of each GA-BP set are improved by 4.9%, 0.3%, 16.7%, and 4.6%, respectively. According to the Fast Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization for meeting C50 concrete strength requirements, the optimal concrete mix ratio is as follows: cement 331.3 kg/m³, sand 639.4 kg/m³, stone 1039 kg/m³, fly ash 56 kg/m³, water 153 kg/m³, and water-reducing agent 0.632 kg/m³. The 28-day compressive strength, material cost, and carbon emissions show relative errors of 2.1%, 0.6%, and 2.9%, respectively. Compared with commercial concrete of the same strength grade, costs and carbon emissions are reduced by 7.2% and 15.9%, respectively. The methodology used in this study not only significantly improves the accuracy of concrete design but also considers the carbon emissions involved in the concrete preparation process, reflecting the strength, economic, and environmental impacts of material design. Practitioners are encouraged to explore integrated low-carbon research that spans from material selection to structural optimization.

In order to reuse waste resources (waste engine oil and waste asphalt mixture), the regeneration process of waste oil on aged asphalt is systematically explained. The BA was treated by aging test, and the basic mechanical properties, molecular dynamics simulation, and infrared spectrum test were carried out on this basis. The results showed that the WEO can restore the physical properties of the aged asphalt, and the recommended amount of WEO is 3%. The density of the asphalt model corresponds to the actual situation of the asphalt. The aged asphalt components are more aggregated. After the WEO was added, the components of asphalt aggregation were reduced and the diffusion ability was improved. Finally, infrared spectroscopy tests were conducted on asphalt specimens, and the results showed that the process of rejuvenated asphalt was dominated by physical reactions. Consequentially, the results of this study build a bridge between the performance and simulation of aged asphalt rejuvenated by WEO. Consequentially, the results of this research promote the recycling of WEO and waste asphalt pavement materials, ultimately advocating the sustainability of pavement construction.

Magnesium slag is a by-product of the magnesium industry. As an auxiliary cementitious material incorporated into concrete, it can make full use of waste resources and has a certain potential for hydration and carbonation. To improve the mechanical properties of the concrete, the influence mechanism and strengthening mechanism of the carbon curing method on mechanical properties of magnesium slag concrete were investigated. The effects of different magnesium slag content and water-cement ratio on mechanical properties and ecological properties of carbon cured magnesium slag concrete were analyzed. Based on the phase composition and thermogravimetric composition of magnesium slag concrete, the carbonation mechanism of magnesium slag was revealed. The mechanical properties models of magnesium slag concrete with different carbon curing were constructed. The study shows that with the increase of the magnesium slag, the mechanical properties of carbon curing concrete first increase and then decrease. The optimum mechanical properties of concrete are 30% magnesium slag, and the compressive strength reaches 42.3 MPa. The content of magnesium slag increased from 0% to 60%, and the carbon fixation content was 14.60%, 11.87%, 11.69%, 16.90%, 19.80%, 14.78%, and 13.09%, respectively. With the increase of magnesium slag content, the content and grain size of magnesium oxide in concrete increase, which leads to more micro-bumps and depressions on the surface of the concrete structure. The hydration reaction and carbonation reaction of gelled materials are affected by magnesium ions, resulting in changes in the morphology and crystal structure of CaCO₃ and MgCO₃ reactants.

With accelerating urbanization, China generates large volumes of construction waste annually, much of which is landfilled, causing environmental damage and wasting recyclable resources. Traditional recycling technologies struggle with complex material compositions and performance degradation, limiting large-scale reuse. This highlights the need for intelligent, data-driven solutions to enhance solid waste recycling efficiency. Recent developments in machine learning (ML) have enabled more accurate performance prediction and optimization of waste concrete, offering new pathways for material regeneration. ML’s nonlinear modeling and pattern recognition capabilities are particularly suited to capturing the complex behaviors of recycled materials. However, systematic reviews on ML applications across various waste concretes are still lacking, and future research directions remain unclear. This study conducts a scientometric analysis of 1762 publications (2011–2024) from the Web of Science Core Collection (WOSCC) and CNKI, aiming to map current trends and guide future research on ML applications in waste concrete recycling. The findings show a clear evolution: research has expanded from recycled concrete to a wider range of modified solid waste concretes. ML applications have advanced from compressive strength prediction to multi-objective optimization involving durability, cost, and mechanical performance. Algorithm development and model accuracy have improved steadily. Key challenges persist, including limited data quality and scale, which constrain complex model training. Research on specific properties such as salt-frost resistance is scarce due to high testing costs. Models often lack generalizability in coupled conditions (e.g., salt-frost–carbonation) and suffer from poor physical interpretability, hindering understanding of durability mechanisms. Nevertheless, unresolved issues remain, including the scarcity of standardized datasets, the limited integration of domain knowledge with ML methods, and the lack of comprehensive evaluations across multiple degradation mechanisms, all of which represent critical research gaps for future studies.