Predicting compressive strength of alkali-activated systems based on the network topology and phase assemblages using tree-structure computing algorithms. (20th June 2022)

• Record Type: Journal Article
• Authors: Bhat, Rohan; Han, Taihao; Akshay Ponduru, Sai; Reka, Arianit; Huang, Jie; Sant, Gaurav; Kumar, Aditya
• Is Part Of: Construction & building materials. Volume 336(2022)
• Publication Date: 2022-06-20
• Subjects: Alkali-activated system -- Compressive strength -- Topological constraint theory -- Thermodynamic simulation -- Machine learning; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2022.127557
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: The machine learning models produce reliable predictions of the compressive strength of alkali-activated systems. Twenty-six aluminosilicate-rich precursors are utilized in the database. Topological network constraints of aluminosilicate-rich precursors and phase assemblages of alkali-activated systems are employed to regulate machine learning models. Correlations between topological network constraint; phase assemblage; and compressive strength are demonstrated. Abstract: Alkali-activated system is an environment-friendly, sustainable construction material utilized to replace ordinary Portland cement (OPC) that contributes to 9% of the global carbon footprint. Moreover, the alkali-activated system has exhibited superior strength at early ages and better corrosion resistance compared to OPC. The current state of analytical and machine learning models cannot produce highly reliable predictions of the compressive strength of alkali-activated systems made from different types of aluminosilicate-rich precursors owing to substantive variation in the chemical compositions and reactivity of these precursors. In this study, a random forest model with two constraints (i.e., topological network and thermodynamic constraints) is employed to predict the compressive strength of alkali-activated systems made from 26 aluminosilicate-rich precursors and distinct processing parameters. Results show that once the model is rigorously trained and optimized, the RF model can yield a priori, high-fidelity predictions of the compressive strength in relation to the physicochemical properties of aluminosilicate-rich precursors; processing parameters; and constraints. The topological network constraint provides the chemostructural properties and reactivity of the aluminosilicate-rich precursors. Whereas the thermodynamic constraint estimates the phase assemblages at different degrees of reaction of the aluminosilicate-rich precursors. Finally, the correlations between topological network constraint; phase assemblage; and compressive strength are demonstrated. When the topological network constraint equals 3.4, the alkali-activated systems can achieve their optimal compressive strength.