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Full-Text Articles in Electrical and Computer Engineering
Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar
Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar
Electrical and Computer Engineering Faculty Research & Creative Works
Fly ash (FA) – an industrial byproduct – is used to partially substitute Portland cement (PC) in concrete to mitigate concrete's environmental impact. Chemical composition and structure of FAs significantly impact hydration kinetics and compressive strength of concrete. Due to the substantial diversity in these physicochemical attributes of FAs, it has been challenging to develop a generic theoretical framework – and, therefore, theory-based analytical models – that could produce reliable, a priori predictions of properties of [PC + FA] binders. In recent years, machine learning (ML) – which is purely data-driven, as opposed to being derived from theorical underpinnings – …
Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar
Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar
Electrical and Computer Engineering Faculty Research & Creative Works
Fly ash (FA) – an industrial byproduct – is used to partially substitute Portland cement (PC) in concrete to mitigate concrete's environmental impact. Chemical composition and structure of FAs significantly impact hydration kinetics and compressive strength of concrete. Due to the substantial diversity in these physicochemical attributes of FAs, it has been challenging to develop a generic theoretical framework – and, therefore, theory-based analytical models – that could produce reliable, a priori predictions of properties of [PC + FA] binders. In recent years, machine learning (ML) – which is purely data-driven, as opposed to being derived from theorical underpinnings – …
Predicting Dissolution Kinetics Of Tricalcium Silicate Using Deep Learning And Analytical Models, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar
Predicting Dissolution Kinetics Of Tricalcium Silicate Using Deep Learning And Analytical Models, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar
Electrical and Computer Engineering Faculty Research & Creative Works
The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C3S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C3S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C3S in the undersaturated solvent. The …