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An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete

Construction & building materials, 2020-05, Vol.244, p.118271, Article 118271

2020

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Prediction of surface chloride concentration of marine concrete using ensemble machine learning

Cement and concrete research, 2020-10, Vol.136, p.106164, Article 106164

2020

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Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems

Scientific reports, 2021-02, Vol.11 (1), p.3922-3922, Article 3922

2021

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Machine learning for high-fidelity prediction of cement hydration kinetics in blended systems

Materials & design, 2021-10, Vol.208, p.109920, Article 109920

2021

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Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach

Materials, 2023-01, Vol.16 (2), p.654

2023

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A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models

Frontiers in materials, 2022-01, Vol.8

2022

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On the Prediction of the Mechanical Properties of Limestone Calcined Clay Cement: A Random Forest Approach Tailored to Cement Chemistry

Minerals (Basel), 2023-10, Vol.13 (10), p.1261

2023

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Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models

Algorithms, 2023-01, Vol.16 (1), p.7

2023

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Experimental and Machine Learning Studies on Chitosan-Polyacrylamide Copolymers for Selective Separation of Metal Sulfides in the Froth Flotation Process

Colloids and interfaces, 2023-06, Vol.7 (2), p.41

2023

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Machine learning to predict properties of fresh and hardened alkali-activated concrete

Cement & concrete composites, 2021-01, Vol.115, p.103863, Article 103863

2021

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A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time

Biosensors (Basel), 2022-12, Vol.12 (12), p.1159

2022

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Mechanism underlying effect of expansive agent and shrinkage reducing admixture on mechanical properties and fiber-matrix bonding of fiber-reinforced mortar

Cement and concrete research, 2023-10, Vol.172, p.107247, Article 107247

2023

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Predicting mechanical properties of ultrahigh temperature ceramics using machine learning

Journal of the American Ceramic Society, 2022-11, Vol.105 (11), p.6851-6863

2022

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On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders

Construction & building materials, 2024-03, Vol.419, p.135523, Article 135523

2024

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Machine learning enabled closed‐form models to predict strength of alkali‐activated systems

Journal of the American Ceramic Society, 2022-06, Vol.105 (6), p.4414-4425

2022

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Resistive switching in atomic layer deposited HfO2/ZrO2 nanolayer stacks

Applied surface science, 2020-06, Vol.515, p.146015, Article 146015

2020

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Deep learning to predict the hydration and performance of fly ash-containing cementitious binders

Cement and concrete research, 2023-03, Vol.165, p.107093, Article 107093

2023

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Machine Learning Enabled Models to Predict Sulfur Solubility in Nuclear Waste Glasses

ACS applied materials & interfaces, 2021-11, Vol.13 (45), p.53375-53387

2021

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Toward smart and sustainable cement manufacturing process: Analysis and optimization of cement clinker quality using thermodynamic and data-informed approaches

Cement & concrete composites, 2024-03, Vol.147, p.105436, Article 105436

2024

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Modeling hydration kinetics of sustainable cementitious binders using an advanced nucleation and growth approach

Construction & building materials, 2023-11, Vol.404, p.133327, Article 133327

2023

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Understanding roles and evaluating reactivity of fly ashes in calcium aluminate binders

Construction & building materials, 2024-02, Vol.414, p.135062, Article 135062

2024

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Early-age reactivity and strength development in high volume mine tailings-based alkali activated binders and their application potential

Minerals engineering, 2024-08, Vol.213, p.108759, Article 108759

2024

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Predicting compressive strength of alkali-activated systems based on the network topology and phase assemblages using tree-structure computing algorithms

Construction & building materials, 2022-06, Vol.336, p.127557, Article 127557

2022

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Development and Application of Ensemble Machine Learning Algorithms to Enable a Priori, High-Fidelity, and Prompt Predictions and Optimizations of Complex Materials and Systems

Han, Taihao

2022

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A comprehensive analysis of hydration kinetics and compressive strength development of fly ash-Portland cement binders

Journal of Building Engineering, 2024-07, Vol.88, p.109191, Article 109191

2024

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An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete

Prediction of surface chloride concentration of marine concrete using ensemble machine learning

Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems

Machine learning for high-fidelity prediction of cement hydration kinetics in blended systems

Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach

A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models

On the Prediction of the Mechanical Properties of Limestone Calcined Clay Cement: A Random Forest Approach Tailored to Cement Chemistry

Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models

Experimental and Machine Learning Studies on Chitosan-Polyacrylamide Copolymers for Selective Separation of Metal Sulfides in the Froth Flotation Process

Machine learning to predict properties of fresh and hardened alkali-activated concrete

A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time

Mechanism underlying effect of expansive agent and shrinkage reducing admixture on mechanical properties and fiber-matrix bonding of fiber-reinforced mortar

Predicting mechanical properties of ultrahigh temperature ceramics using machine learning

On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders

Machine learning enabled closed‐form models to predict strength of alkali‐activated systems

Resistive switching in atomic layer deposited HfO2/ZrO2 nanolayer stacks

Deep learning to predict the hydration and performance of fly ash-containing cementitious binders

Machine Learning Enabled Models to Predict Sulfur Solubility in Nuclear Waste Glasses

Toward smart and sustainable cement manufacturing process: Analysis and optimization of cement clinker quality using thermodynamic and data-informed approaches

Modeling hydration kinetics of sustainable cementitious binders using an advanced nucleation and growth approach

Understanding roles and evaluating reactivity of fly ashes in calcium aluminate binders

Early-age reactivity and strength development in high volume mine tailings-based alkali activated binders and their application potential

Predicting compressive strength of alkali-activated systems based on the network topology and phase assemblages using tree-structure computing algorithms

Development and Application of Ensemble Machine Learning Algorithms to Enable a Priori, High-Fidelity, and Prompt Predictions and Optimizations of Complex Materials and Systems

A comprehensive analysis of hydration kinetics and compressive strength development of fly ash-Portland cement binders

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