This article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were collected and analysed according to ten input variables (all relevant mix-design parameters) and the output variable (cylindrical compressive strength). The developed optimal FLNN model proved to be a powerful tool for predicting the compressive strength of FA geopolymer concrete with a small range of mean squared error (MSE = 10.4 and 15.0), a high correlation coefficient with the actual values (R = 96.0 and 97.5) and a relatively small root mean squared error (RMSE = 3.22 and 3.87 MPa) for the training and testing data, respectively. Based on the optimised model, a powerful design chart for determining the mix-design parameters of FA geopolymer concretes was generated. It is applicable for both one- and two-part geopolymer concretes, as it takes a wide range of mix-design parameters into account. The design chart (with its relatively small error) will ensure cost- and time-efficient geopolymer production in future applications.

Recently, utilizing industrial waste in the construction industry has gained significant attention to meet sustainability demands and mitigate the adverse environmental impacts caused by the construction industry. This study evaluates the engineering properties of waste foundry sand as a target material after stabilization with an environmentally friendly stabilizing agent (fly ash geopolymer), focusing on achieving adequate strength under ambient curing conditions as a feasible choice for road bases in geotechnical applications. While fly ash geopolymer application is typically linked with temperature curing, this research explores its application under ambient curing to enhance feasibility and reduce production costs. The fly ash geopolymer was synthesized by activating fly ash using a combination of sodium hydroxide and sodium silicate. The experimental program investigated the geopolymer-stabilized waste foundry sand at varying dosages of 7, 15, and 25 %, examining physical properties, non-destructive tests, mechanical properties, XRD phase analysis, and SEM observation. The results demonstrated that increasing fly ash dosage significantly enhanced the physical properties, mechanical properties, and microstructure of the geopolymer-stabilized waste foundry sand samples. Dry density improved from 1.75 to 2.02 g/cm³; longitudinal wave velocity increased from 897.3 to 2028.4 m/s, and unconfined compressive strength rose from 109 to 5261 kPa. Notably, only samples with 25% fly ash achieved the requisite strength to satisfy the road base limit (4100 kPa). These outcomes instill confidence in the potential use of waste foundry sand as a construction material and transition it from mere filling material to a valuable resource, furthermore encouraging the adoption of fly ash geopolymer as an environmentally friendly stabilizing agent in geotechnical applications.

Despite the unremitting efforts to model the modulus of elasticity of low-calcium-based geopolymer concrete, the state-of-the-art models need much improvement to reduce the error signals and increase the reliability. This study represents a comprehensive regression analysis to model the modulus of elasticity of low-calcium-based geopolymer concrete in terms of its compressive strength. The proposed models assumptions are based on taking into account the chemical composition and compressive strength class and considering the normal density of concrete. The modelling is based on 67 different mix-design samples collected from peer-reviewed literature, which are divided into two groups. The first group consists of 59 samples that are used to construct the proposed model, while the second group consists of 8 samples that are used to test the validity of the proposed model. The analysis showed that the proposed model gives the root mean squared error value (RMSE) of 3.122GPa and the mean absolute percentage error value (MAPE) of 15.0. Therefore, the proposed model gives 41 and 52.2 reductions in RMSE and MAPE, respectively, from the state-of-the-art model in the literature. Furthermore, other statistical parameters to evaluate the goodness of fitness of the proposed model have been considered, such as the relative root mean squared error (RRMSE) (15.5), the coefficient of determination (R-squared) (0.773), and the coefficient of correlation (R) (0.88); all of which indicated that the goodness of fitness is good and the proposed model has a high correlation to the actual values. Applying the proposed model in future applications will help reduce the time and cost of geopolymer production, as the proposed model has significantly reduced the error signals.

The research aims to determine the best combination of the controlling factors that govern geopolymer concrete’s mechanical and physical properties by utilizing industrial waste. Therefore, a review on the controlling factors was conducted. Firstly, it is to identify the controlling factors, namely chemical composition, alkali activation solution, water content, and curing condition. Secondly, understanding the relationship between these controlling factors and the properties of geopolymer concrete. These factors are analysed to the mix proportion components. Finally, a new proportion method is proposed based on combining ACI 211 standard and recommended molar ratios of oxides involved in geopolymer synthesis. The effect of aggregate has been taken into account by applying the absolute volume method in mix design. Based on the results of the study, it is expected to determine the optimal mix proportions based on multi-responses.