The strength, pore structure, morphology and mineralogical composition of hydration products of natural pozzolan (NP) and nanosilica (nSiO₂) based alkali activated binder (AAB) were examined. It was noted that AAB without nSiO₂ was relatively porous with the presence of micro-, meso- and macro-level pores. In contrast, as the proportion of nSiO₂ increased in the mixture, the pore structure started to refine with a reduction in the pore volume due to transformation of source material to polymeric compound, such as C-A-S-H with a higher Al absorption in the binder structure. The incorporation of 5% and 7.5% nSiO₂ resulted in the refinement of the pore structure due to enhanced polycondensation. Consequently, there was considerable improvement in the mechanical properties. The experimental data indicated that the pore structure evaluation through NMR has served as a tool to understand the relation between the micro- and macro-level performance of the developed AAC.

The development of sustainable, environmentally friendly alkali-activated binder has emerged as an alternative to ordinary Portland cement. The engineering and durability properties of alkali-activated binder using various precursor combinations have been investigated; however, no study has focused on the impact of high-volume natural pozzolan (NP) on the acid resistance of alkali-activated NP and limestone powder. Therefore, the current study assesses the impact of high-volume natural pozzolan (volcanic ash) on the durability properties of alkali-activated natural pozzolan (NP) and limestone powder (LSP) mortar by immersion in 6% H₂SO₄ for 365 days. The samples were prepared with different binder ratios using alkaline activators (10 M NaOH_(aq) and Na₂SO₄) combined in a 1:1 ratio and cured at 75 °C. NP was combined with the LSP at three different combinations: NP:LSP = 40:60 (AAN₄₀L₆₀), 50:50 (AAN₅₀L₅₀), and 60:40 (AAN₆₀L₄₀), representing low-volume, balanced, and high-volume binder combinations. Water absorption, weight change, and compressive strength were examined. The microstructural changes were also investigated using FTIR, XRD, and SEM/EDS characterization tools. Visual examination showed insignificant deterioration in the sample with excess natural pozzolan (AAN₆₀L₄₀) after 1 year of acid exposure, and the maximum residual strengths were 20.8 MPa and 6.68 MPa in AAN₆₀L₄₀ and AAN₄₀L₆₀ with mass gain (1.37%) and loss (10.64%), respectively. The high sulfuric acid resistance of AAN₆₀L₄₀ mortar was attributed to the high Ca/Si = 10 within the C-A-S-H and N-A-S-H formed. The low residual strength recorded in AAN₄₀L₆₀ was a result of gypsum formation from an acid attack of calcium-dominated limestone powder. The controlling factor for the resistance of the binder to acid corrosion was the NP/LSP ratio, whose factor below 0.6 caused significant debilitating effects.

Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO₂ emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS_1D), and 7-day CS (CS_7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R² = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS_28D) of cementitious grouts. Among them, the CS_7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS_28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.