Applicability of waste foundry sand stabilization by fly ash geopolymer under ambient curing conditions.

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.

Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model.

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.