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Full-Text Articles in Architecture
In Situ Monitoring Of The Hydration Of Calcium Silicate Minerals In Cement With A Remote Fiber-Optic Raman Probe, Bohong Zhang, Wenyu Liao, Hongyan Ma, Jie Huang
In Situ Monitoring Of The Hydration Of Calcium Silicate Minerals In Cement With A Remote Fiber-Optic Raman Probe, Bohong Zhang, Wenyu Liao, Hongyan Ma, Jie Huang
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
This study utilized a novel in situ fiber-optic Raman probe to continuously monitor the hydration progress of tricalcium silicate (C3S) and dicalcium silicate (C2S) without the need for sampling, from early hydration stage to later stages, and from fresh to hardened states of paste samples. By virtue of the remarkable ability of this technique in characterizing either dry or wet and crystalline or amorphous samples, the hydration processes of C3S and C2S pastes with different water-to-solid (w/s) ratios could be monitored from the start of the hydration reaction. The main hydration products, …
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 …