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Full-Text Articles in Engineering
On The Use Of Machine Learning And Data-Transformation Methods To Predict Hydration Kinetics And Strength Of Alkali-Activated Mine Tailings-Based Binders, Sahil Surehali, Taihao Han, Jie Huang, Aditya Kumar, Narayanan Neithalath
On The Use Of Machine Learning And Data-Transformation Methods To Predict Hydration Kinetics And Strength Of Alkali-Activated Mine Tailings-Based Binders, Sahil Surehali, Taihao Han, Jie Huang, Aditya Kumar, Narayanan Neithalath
Electrical and Computer Engineering Faculty Research & Creative Works
The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement …
Understanding Roles And Evaluating Reactivity Of Fly Ashes In Calcium Aluminate Binders, Sai Akshay Ponduru, Taihao Han, Jie Huang, Narayanan Neithalath, Gaurav Sant, Aditya Kumar
Understanding Roles And Evaluating Reactivity Of Fly Ashes In Calcium Aluminate Binders, Sai Akshay Ponduru, Taihao Han, Jie Huang, Narayanan Neithalath, Gaurav Sant, Aditya Kumar
Electrical and Computer Engineering Faculty Research & Creative Works
Calcium aluminate cement (CAC) is an alternative to Portland cement, valued for its superior early strength and thermal resistance. Partially replacing CAC with Fly ash (FA) can reduce carbon footprint and production costs of CAC, producing sustainable cementitious binders. This research investigates on various properties (i.e., hydration kinetics; phase assemblage evolution; compressive strength) of [CAC + FA] binders. Using 13 distinct FAs, up to 50% of CAC was substituted. The study measures hydration kinetics, compressive strength, and employs the number of constraints to estimate FA reactivity. Advanced quantitative analysis draws links between hydration kinetics and compressive strength and elucidate the …
Predicting Compressive Strength Of Alkali-Activated Systems Based On The Network Topology And Phase Assemblages Using Tree-Structure Computing Algorithms, Rohan Bhat, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar
Predicting Compressive Strength Of Alkali-Activated Systems Based On The Network Topology And Phase Assemblages Using Tree-Structure Computing Algorithms, Rohan Bhat, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar
Electrical and Computer Engineering Faculty Research & Creative Works
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 …
Machine Learning Enabled Closed-Form Models To Predict Strength Of Alkali-Activated Systems, Taihao Han, Eslam Gomaa, Ahmed Gheni, Jie Huang, Mohamed Elgawady, Aditya Kumar
Machine Learning Enabled Closed-Form Models To Predict Strength Of Alkali-Activated Systems, Taihao Han, Eslam Gomaa, Ahmed Gheni, Jie Huang, Mohamed Elgawady, Aditya Kumar
Electrical and Computer Engineering Faculty Research & Creative Works
Alkali-activated mortar (AAM) is an emerging eco-friendly construction material, which can complement ordinary Portland cement (OPC) mortars. Prediction of properties of AAMs—albeit much needed to complement experiments—is difficult, owing to substantive batch-to-batch variations in physicochemical properties of their precursors (i.e., aluminosilicate and activator solution). In this study, a machine learning (ML) model is employed; and it is shown that the model—once trained and optimized—can reliably predict compressive strength of AAMs solely from their initial physicochemical attributes. Prediction performance of the model improves when multiple compositional descriptors of the aluminosilicate are combined into a singular, composite chemostructural descriptor (i.e., network ratio …