Geopolymer foam concrete (GFC) has emerged as a sustainable alternative in construction, utilizing waste-derived binders to enhance material properties while addressing environmental concerns. This review examines the impact of precursor materials, foam agents, nanomaterials, fibers, and curing temperatures on GFC’s pore structure and compressive strength. GFC typically achieves compressive strengths of 1–10 MPa, with porosity significantly influencing performance. For instance, GFC with densities of 280–865 kg/m³ exhibits compressive strengths of 1.10–8.13 MPa and thermal conductivities of 0.08–0.20 W/(m K), making it suitable for insulation applications. The integration of alternative materials, such as industrial by-products, enhances sustainability without compromising mechanical properties. Precise control over curing parameters, particularly heating rates, is critical to optimizing porosity and strength. The addition of nanomaterials improves mechanical performance and introduces self-sensing capabilities, expanding GFC’s potential for advanced industrial applications. Fibers further enhance toughness and crack resistance, broadening its usability. Machine learning algorithms offer promising tools for optimizing GFC formulations and structural designs, enabling the development of high-performance, sustainable materials tailored to specific engineering needs. By systematically evaluating the effects of alternative materials, nanotechnology, fibers, and curing conditions, this review highlights the potential of GFC to revolutionize sustainable construction. Finally, GFC represents a transformative advancement in eco-friendly construction materials. Its integration of waste-derived binders, nanomaterials, and advanced optimization techniques positions it as a key solution for sustainable infrastructure, balancing performance, durability, and environmental responsibility.

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is an exceptional type of cementitious composite with superior mechanical and durability performances. Achieving these properties involves maintaining a low water-to-cement ratio, optimizing aggregate size distribution, and integrating fiber reinforcement. Recently, there has been a notable trend in the development and application of UHPFRCs. However, there is still a requirement for artificial intelligence (AI) methods to predict the early-age compressive strength (CS) of UHPFRC and to define the key input factors for optimal mix design with appropriate proportions. Therefore, five AI models were chosen to assess the predictive accuracy of early-age CS in the current study. These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). As part of evaluating model performance and conducting error analysis, this study investigated differences in prediction accuracy among five models across training and testing datasets. Additionally, feature importance analysis was implemented to explore the influence of the input variables on the early-age CS. Results indicate that GPR and SVR models with high predictive accuracy (R² > 0.90) outperformed ANN, RF, and GB models. Water, superplasticizer, curing temperature, and fiber content emerged as the most significant controlling parameters affecting early-age CS. The analysis of the interaction among the significant input variables and early-age CS suggests recommended inclusion levels for optimal performance. Specifically, it is recommended that the water content be maintained between 145 and 155 kg/m², the superplasticizer content between 30 and 40 kg/m², and the fiber content exceed 200 kg/m². These recommendations are aimed at achieving desirable early-age CS characteristics. The overall findings reveal that the AI models can effectively improve the monitoring of early-age CS of UHPFRC.

To address the demands of the low-carbon era, this study proposed a solution by using eggshell powder (ESP), fly ash, and ground granulated blast furnace slag together with alkaline solution in the preparation of lightweight geopolymer foam concrete (LWGFC). The aim of this study is to investigate the influence of replacing precursor materials with 5–20% ESP on the expansion behavior, physical, mechanical characteristics, and thermal conductivity of LWGFC. Additionally, the study examines the effect of varying the silicate modulus (SiO₂/Na₂O ratios of 1.0, 1.25, and 1.5) on the properties of LWGFC. Incorporating ESP from 5% to 20% with a constant SiO₂/Na₂O ratio reduced the initial setting time, while a high SiO₂/Na₂O ratio controlled the setting time and expansion volume. The high SiO₂/Na₂O ratio decreased the porosity and enhanced the compressive strength of the LWGFC but increased the thermal conductivity. The inclusion of more than 10% ESP content negatively affected compressive strength; however, a high SiO₂/Na₂O ratio can mitigate this detrimental effect. The thermal conductivity of optimal-content ESP mixtures with a SiO₂/Na₂O ratio of 1.0 was about 0.84 W/m·K, which is 2.1% lower than mixtures with a ratio of 1.25 and 18.6% lower than those with a ratio of 1.5. High-content ESP mixtures had a density of 1707 kg/m³, 0.97 W/m·K, and a compressive strength of 18.9 MPa at a low SiO₂/Na₂O ratio. Finally, the inclusion of ESP in the LWGFC, along with the use of an appropriate silicate modulus, resulted in improved strength development while decreasing porosity.

This research investigates the impact of micro-encapsulated phase change materials (micro-PCMs) on the thermal performance and mechanical properties of concrete, specifically focusing on improving energy efficiency in buildings. Four concrete mixtures were studied: one control mix without micro-PCMs and three mixes containing 7.5%, 15%, and 30% micro-PCMs added to the cement content. The slump, density, compressive strength, thermal conductivity, and temperature changes over time were measured. The results show that incorporating micro-PCMs improves workability to 16% for the mix containing 30% PCM. The unit weight of the concrete decreased with an increase of PCM reduction of up to 4.6% at 30% micro-PCM content. However, there was a significant decline in compressive strength, with reductions to 12.2 and 20.1 MPa after 7 and 28 days for the mix of 30% PCM. Adding micro-PCMs significantly reduced thermal conductivity by as much as 49.2%, enhancing the insulation performance of 30% PCM. The heat transfer analysis indicated that integrating PCMs lowers peak temperatures to 25%, delaying the time to reach peak temperature.