Ultra-high-performance concrete (UHPC) is widely used in the field of large-span and ultra-high-rise buildings due to its advantages such as ultra-high strength and durability. However, the large amount of cementitious materials used results in the cost and carbon emission of UHPC being much higher than that of ordinary concrete, limiting the wide application of UHPC. Therefore, optimizing the design of the UHPC mix proportion to meet the basic properties of UHPC with low carbon and low cost at the same time will help to realize the wide application of UHPC in various application scenarios. In this study, the basic properties of UHPC, including the compressive strength, flexural strength, fluidity, and shrinkage properties, were predicted by machine-learning algorithms. It is found that the XGBoost algorithm outperforms others in predicting basic properties, with MAPE lower than 5% and R² higher than 0.9 in four output properties. To evaluate the comprehensive performance of UHPC, a further analysis was conducted to calculate the cost- and carbon-emissions-per-unit volume for 50,000 UHPC random mixes. Combined with the analytical hierarchy process (AHP) model, the comprehensive performance of UHPC, including basic properties, cost-per-unit volume, and carbon-emissions-per-unit volume, was evaluated. This study proposes an optimized UHPC mix proportion, based on low-cost or low-carbon emission, oriented to comply with the excellent overall performance and obtain its corresponding various properties.

Using recycled powders from solid waste is accepted as an effective strategy to realize the sustainable development of the construction industry. In our study, the cement was substituted by two kinds of recycled powders, i.e., spontaneous combustion gangue powder (SCGP) and recycled concrete powder (RCP), with a certain replacement ratio of 30%. The experimental variables were mainly the type of replacement powder (e.g., SCGP, RCP, and SCGP + RCP) and the grinding time of RCP (e.g., 25 min, 50 min, and 75 min). The fundamental properties, including mechanical properties, long-term properties, and carbon emission, were analyzed for all the mortar mixtures. Experimental results indicate that incorporation of RCP contributes to enhancing the toughness and dry shrinkage resistance of eco-efficient mortar, while SCGP positively affects the compressive strength and chloride resistance. The grinding process improves the activity of RCP to a certain extent, while a long grinding time leads to fusion and aggregation between powders. Investigation on CO₂ emission demonstrates that carbon emission from cement production accounts for the largest proportion, 80~95%, in the total emission from mortar production. Combined with the AHP model, eco-efficient mortar containing 15% RCP ground for 50 min and 15% SCGP displays optimal fundamental properties.

Adding conductive materials to cement-based composites can lead to pressure-sensitive properties. In this study, different scales of conductive materials were incorporated, including macro-scale steel fibers, micro-scale carbon black powder, and nano-scale graphene. The coupling effect of three scales of materials ensured that the intelligent concrete had improved strength, lower cost, and comparable pressure-sensitive performance. The results show that the strength of intelligent concrete with multi-scale conductive materials is higher than that of the contrast group of ordinary concrete and intelligent concrete when adding nano-scale graphene alone. Especially, the addition of steel fibers significantly improved the crack resistance of the intelligent concrete. In the elastic stage, the resistivity of intelligent concrete of multi-scale conductive materials decreases with the increase in compression, and the decrease range of resistivity is approximately proportional to the external force. After reaching the peak load, the resistivity of the intelligent concrete gradually increases and can illustrate the damage evolution. This study lays a foundation for the application of intelligent concrete in deformation and damage monitoring.