Geopolymer concrete (GPC) has emerged as a sustainable and high-performance alternative to traditional Portland cement concrete (PCC) also known as ordinary Portland cement (OPC), offering significant environmental and engineering advantages. This review synthesizes current research on the properties, benefits, challenges, and innovations of GPC in construction applications. Key findings indicate that GPC achieves compressive strengths exceeding 80 MPa, demonstrates excellent resistance to acid and sulfate attack, and retains over 90% of its strength at temperatures up to 800 °C. Environmental analyses reveal a 40–80% reduction in CO₂ emissions and lower energy consumption compared to PCC, primarily due to the utilization of industrial by-products like fly ash and slag. Lifecycle assessments show that GPC is not only cost-effective over the long term but also enables material reuse rates up to 70%, supporting circular economy principles. Despite these advantages, challenges such as material variability, limited standardization, and curing sensitivity remain. Recent innovations—including nanomaterial integration, hybrid binder systems, self-healing mechanisms, and 3D printing—are enhancing GPC’s performance and expanding its applications. This review concludes that with continued research and regulatory support, GPC holds significant promise for transforming sustainable construction practices.

Bonding strength between asphalt and aggregate is considered an important feature that governs the future performance of flexible pavements. This study investigated five factors that have the potential to affect this bonding strength. Two asphalt penetration grades (60/70 and 85/100) in addition to various types of substrates (limestone, granite, marble and basalt) were used to prepare specimens to be tested by the Binder Bond Strength (BBS) test. Three aging levels (unaged, short-term aged and long-term aged) with four types of modifiers (Crumb Tire Rubber, Nano Silica, Nano Clay and Microcrystalline Synthetic Wax) were implemented for two specimen conditioning methods (dry and wet). Statistical analysis was performed to measure the influence of these factors on the asphalt-aggregate bonding strength. The results showed that aggregate type and modifier type had the most effect on the bonding strength with relatively no effect of binder type. Moreover, long term aging and two types of modifiers (Nano Silica and Crumb Tire Rubber) were found to significantly increase the Pull-Off Tensile Strength (POTS), which is a measure of the asphalt-aggregate bonding strength. Failure type was mainly cohesive in the dry condition then transformed to adhesive when specimens are exposed to moisture.

When designing pavements, there are three fundamental external design parameters to evaluate (1) the characteristics of the subgrade upon which the pavement is placed, (2) the applied loads and (3) the environment. This study is based on laboratory testing of cohesive subgrade materials to characterise their resilient modulus properties and to identify the main controlling factors of this property. Experimental results showed a slight increase in resilient modulus values with an increase in confining pressure and a noteworthy decrease in resilient modulus accompanied by an increase in deviatoric stress. This finding was verified statistically. Based on the independent samples t-test analysis, it was revealed that soil type and water content caused statistically significant difference in resilient modulus values. According to one-way ANOVA, deviatoric stress turned out to be influential while confining pressure did not. In addition, it was demonstrated using the two-way ANOVA that none of the two-factor combinations was significant in changing resilient modulus values of A-4a and A-6a soils. Finally, three-way ANOVA showed that changing the deviatoric stress, soil type and water content all together had a noticeable effect on resilient modulus behaviour. Furthermore, all predictive models were found statistically significant at the 0.05 significance level with high predictive power. Confining pressure and deviatoric stress are the two predictors whereas bulk stress was not included by the stepwise regression method. The non-linear regression relatively enhanced the predictive power of all the models.