| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Kumar, Aditya
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (15/15 displayed)
- 2024Identifying the Origin of Thermal Modulation of Exchange Bias in MnPS 3 /Fe 3 GeTe 2 van der Waals Heterostructurescitations
- 2024Identifying the Origin of Thermal Modulation of Exchange Bias in MnPS<sub>3</sub>/Fe<sub>3</sub>GeTe<sub>2</sub> van der Waals Heterostructurescitations
- 2024Mechanisms of electrical switching of ultrathin CoO/Pt bilayers
- 2022A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Modelscitations
- 2022Utilization of Tea Industrial Waste for Low-Grade Energy Recovery: Optimization of Liquid Oil Production and Its Characterizationcitations
- 2022Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Modelscitations
- 2021Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systemscitations
- 2020Development of Exothermic Flux for Enhanced Penetration in Submerged Arc Weldingcitations
- 2020Revisiting nucleation in the phase-field approach to brittle fracturecitations
- 2017The filler effect: The influence of filler content and type on the hydration rate of tricalcium silicatecitations
- 2014Comparison of Ca(NO3)2 and CaCl2 Admixtures on Reaction, Setting, and Strength Evolutions in Plain and Blended Cementing Formulationscitations
- 2014Water Vapor Sorption in Cementitious Materials—Measurement, Modeling and Interpretationcitations
- 2013Simple methods to estimate the influence of limestone fillers on reaction and property evolution in cementitious materialscitations
- 2013A comparison of intergrinding and blending limestone on reaction and strength evolution in cementitious materialscitations
- 2012The influence of sodium and potassium hydroxide on volume changes in cementitious materialscitations
Places of action
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article
Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models
Abstract
<jats:p>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 DF model takes into account several variables, including the measurement method (i.e., reactor connected to inductive coupled plasma spectrometer and flow chamber with vertical scanning interferometry), temperature, and physicochemical properties of solvents. Next, the DF model evaluates the influence of each variable on the dissolution rate of C3S, and this information is used to develop a closed-form analytical model that can predict the dissolution rate of C3S. The coefficients and constant of the analytical model are optimized in two scenarios: generic and alkaline solvents. The results show that both the DF and analytical models are able to produce reliable predictions of the dissolution rate of C3S when it is undersaturated and far from equilibrium.</jats:p>