Stress-strain relationships and brittleness characteristic analysis for ternary blended marine geopolymer mortars prepared with seawater and sea-sand

To address the shortage of marine engineering construction materials and the high carbon emissions of ordinary Portland cement (OPC), this study utilized seawater, sea sand, and ternary solid waste to prepare marine geopolymer mortar (MGM). A total of 23 groups of MGM with different alkaline contents and varying volume fractions of basalt fibers (BF) and polypropylene fibers (PPF) were subjected to cubic compressive, axial compressive, and splitting tensile tests. The strength development of MGM and corresponding OPC mixes was investigated at different curing ages. The stress-strain characteristics of all specimens at 3 d, 7 d, and 28 d were analyzed, and the energy absorption capacity (EAC), compressive toughness index (CTI), and brittleness index (BI) of each mortar were discussed in detail. Additionally, a comprehensive evaluation index (CEI) was introduced to fully assess the effects of alkaline content and fiber volume fractions on the mortar samples, considering mechanical properties and brittleness parameters. The research found that increasing alkaline content enhanced the compressive strength and elastic modulus of MGM and gradually increased its brittle failure characteristics. Incorporating BF improved the elastic modulus of both MGM and OPC, while PPF slightly reduced the elastic modulus of the mortar. Increasing the fiber volume fraction enhanced the EAC and CTI of MGM and reduced the BI. The EAC of fiber reinforced MGM was significantly higher than that of OPC, and the CTI and BI were comparable. The CEI of hybrid fiber reinforced MGM and OPC was notably higher than that of mono-fiber reinforced samples. The optimal hybrid fiber content for MGM is recommended to be between 0.3 % and 0.6 %. The stress-strain model proposed in this study can account for the effects of alkaline content and fiber volume fraction on the constitutive relationship of the mortar, demonstrating good predictive accuracy.