Dataset for: Mechanical, Durability, and Life Cycle Analysis of Industrial Waste-Based Geopolymers as Alternatives to Portland Cement
Description
This dataset accompanies the research article titled "Industrial Wastes for Manufacturing Green Geopolymers: Carbon-Neutral Alternatives to Portland Cement." It contains experimental data from the synthesis and performance evaluation of geopolymer binders formulated using industrial by-products including fly ash, ground granulated blast furnace slag (GGBFS), and red mud. Contents: Mix Design Data: Proportions of precursors, alkali activators (NaOH, Na₂SiO₃), molar ratios (Si/Al, Na/Al), liquid-to-solid ratios, and curing conditions. Mechanical Test Results: Compressive strength and flexural strength values for various geopolymer formulations under different curing regimes. Durability Test Data: Resistance to sulfate attack, chloride penetration, and freeze-thaw cycles compared to ordinary Portland cement (OPC). Life Cycle Inventory (LCI) Data: Embodied energy and CO₂ emission calculations for geopolymer and OPC production. Optimisation Data: Input and output parameters used in Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models for mix optimisation. Microstructural Analysis: SEM/EDS and XRD data summaries for selected samples.
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Steps to reproduce
Data Collection Methods and Protocol The data in this study were generated through systematic laboratory synthesis and testing of industrial waste-based geopolymers. Fly ash, ground granulated blast furnace slag, and red mud were used as aluminosilicate precursors, activated using sodium hydroxide and sodium silicate solutions. Geopolymer paste was prepared by dry mixing precursors, adding alkali activators, and curing under controlled temperature (ambient, heat, or steam curing). Mechanical strength was assessed via compressive and flexural tests using a Universal Testing Machine (ASTM C109/C348). Durability evaluations included sulfate immersion, rapid chloride permeability (ASTM C1202), and freeze-thaw cycling (ASTM C666). Microstructural analysis was conducted using SEM-EDS and XRD. Life cycle assessment was performed using OpenLCA with the Ecoinvent database. Mix optimization employed Response Surface Methodology and Artificial Neural Networks (Python/TensorFlow). All tests were performed in triplicate with calibrated instruments, and raw data were logged with timestamps. Detailed protocols are included in the dataset files.