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

Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders\u27 compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science

Hydration of High-Alumina Calcium Aluminate Cements with Carbonate and Sulfate Additives

This study investigated the influence of limestone (LS) and calcium sulfate (C$) mineral additives on the hydration kinetics of high-α-Al2O3 calcium aluminate cement (CAC) utilizing experimental techniques and thermodynamic simulations. Increasing the replacement level of limestone or calcium sulfate increased the cumulative heat of the hydration reaction. The limestone exhibited limited acceleratory effects to the CAC hydration kinetics due to the coarseness of the powder. The coarse particle size distribution limited any heterogenous nucleation that would have occurred with a finer particle size as well as the intrinsic insolubility kinetically limits the formation of monocarboaluminate phases. Conversely, the cumulative heat release increased as the limestone content increased; however, this was not due to any enhanced reactivity provided by the limestone. Instead, this increase in the cumulative heat is due to a combination of the LS and the increase in the amount of water available to react with CAC via the dilution effect. In comparison, the increase in the C$ replacement level accelerated the heat flow rate of CAC with the C$particles acting as a favorable surface for heterogenous nucleation of the hydrates during the initial stages of the hydration reaction. Increasing the C$ replacement level does not form more ettringite and does not translate in an increase in the compressive strength. After the 72-h hydration period, C$remains in the microstructure, showing that the complete dissolution of C$ is not responsible for the monotonic increase in heat flow rate. It is expected that the amount of hydrates or residual unreacted particles cannot compensate for the decrease in strength caused by the reduction of α-Al2O3 present in the CAC

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

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs\u27 compositions–plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions–state-of-the-art computational models are unable to produce a priori predictions of hydration kinetics or properties of [PC + SCM] binders. In the past 2 decades, the combination of Big data and machine learning (ML)—commonly referred to as the fourth paradigm of science–has emerged as a promising approach to learn composition-property correlations in materials (e.g., concrete), and capitalize on such learnings to produce a priori predictions of properties of materials with new compositions. Notwithstanding these merits, widespread use of ML models is hindered because they: 1) Require Big data to learn composition-property correlations, and, in general, large databases for concrete are not publicly available; and 2) Function as black-boxes, thus providing little-to-no insights into the materials laws like theory-based analytical models do. This study presents a deep learning (DL) model capable of producing a priori, high-fidelity predictions of composition- and time-dependent hydration kinetics and phase assemblage development in [PC + SCM] pastes. The DL is coupled with: 1) A fast Fourier transformation algorithm that reduces the dimensionality of training datasets (e.g., kinetic datasets), thus allowing the model to learn intrinsic composition-property correlations from a small database; and 2) A thermodynamic model that constrains the model, thus ensuring that predictions do not violate fundamental materials laws. The training and outcomes of the DL are ultimately leveraged to develop a simple, easy-to-use, closed-form analytical model capable of predicting hydration kinetics and phase assemblage development in [PC + SCM] pastes, using their initial composition and mixture design as inputs

Predicting Compressive Strength of Alkali-Activated Systems based on the Network Topology and Phase Assemblages using Tree-Structure Computing Algorithms

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 employed to predict the compressive strength of alkali-activated systems made from 26 aluminosilicate-rich precursors and distinct processing parameters. Results show that once the model is rigorously trained and optimized, the RF model can yield a priori, high-fidelity predictions of the compressive strength in relation to the physicochemical properties of aluminosilicate-rich precursors; processing parameters; and constraints. The topological network constraint provides the chemo structural properties and reactivity of the aluminosilicate-rich precursors. Whereas the thermodynamic constraint estimates the phase assemblages at different degrees of reaction of the aluminosilicate-rich precursors. Finally, the correlations between topological network constraint; phase assemblage; and compressive strength are demonstrated. When the topological network constraint equals 3.4, the alkali-activated systems can achieve their optimal compressive strength

Deep Learning to Predict the Hydration and Performance of Fly Ash-Containing Cementitious Binders

Fly ash (FA) – an industrial byproduct – is used to partially substitute Portland cement (PC) in concrete to mitigate concrete\u27s 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 – has emerged as a promising tool to predict and optimize properties of complex, heterogenous materials, including the aforesaid binders. That said, there are two issues that stand in the way of widespread use of ML models: (1) ML models require thousands of data-records to learn input-output correlations and developing such a large, yet consistent database is impractical; and (2) ML models – while good at producing predictions – are unable to reveal the underlying correlation between composition/structure of material and its properties. This study employs a deep forest (DF) model to predict composition- and time-dependent hydration kinetics and compressive strength of [PC + FA] binders. Data dimensionality-reduction and segmentation techniques – premised on theoretical understanding of composition-structure correlations in FAs, and hydration mechanism of PC – are used to boost the DF model\u27s prediction performance. And, finally, through inference of the intermediate and final outputs of the DF model, a simple, closed-form analytical model is developed to predict compressive strength, and reveal the correlations between mixture design and compressive strength of [PC + FA] binders

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

Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders’ compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science

Hydration of High-Alumina Calcium Aluminate Cements with Carbonate and Sulfate Additives

This study investigated the influence of limestone (LS) and calcium sulfate (C$) mineral additives on the hydration kinetics of high-α-Al2O3 calcium aluminate cement (CAC) utilizing experimental techniques and thermodynamic simulations. Increasing the replacement level of limestone or calcium sulfate increased the cumulative heat of the hydration reaction. The limestone exhibited limited acceleratory effects to the CAC hydration kinetics due to the coarseness of the powder. The coarse particle size distribution limited any heterogenous nucleation that would have occurred with a finer particle size as well as the intrinsic insolubility kinetically limits the formation of monocarboaluminate phases. Conversely, the cumulative heat release increased as the limestone content increased; however, this was not due to any enhanced reactivity provided by the limestone. Instead, this increase in the cumulative heat is due to a combination of the LS and the increase in the amount of water available to react with CAC via the dilution effect. In comparison, the increase in the C$ replacement level accelerated the heat flow rate of CAC with the C$particles acting as a favorable surface for heterogenous nucleation of the hydrates during the initial stages of the hydration reaction. Increasing the C$ replacement level does not form more ettringite and does not translate in an increase in the compressive strength. After the 72-h hydration period, C$remains in the microstructure, showing that the complete dissolution of C$ is not responsible for the monotonic increase in heat flow rate. It is expected that the amount of hydrates or residual unreacted particles cannot compensate for the decrease in strength caused by the reduction of α-Al2O3 present in the CAC

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

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

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

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs\u27 compositions-plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions-state-of-the-art computational models are unable to produce a priori predictions of hydration kinetics or properties of [PC + SCM] binders. In the past 2 decades, the combination of Big data and machine learning (ML) -- commonly referred to as the fourth paradigm of science -- has emerged as a promising approach to learn composition-property correlations in materials (e.g., concrete), and capitalize on such learnings to produce a priori predictions of properties of materials with new compositions. Notwithstanding these merits, widespread use of ML models is hindered because they: 1) Require Big data to learn composition-property correlations, and, in general, large databases for concrete are not publicly available; and 2) Function as black-boxes, thus providing little-to-no insights into the materials laws like theory-based analytical models do. This study presents a deep learning (DL) model capable of producing a priori, high-fidelity predictions of composition- and time-dependent hydration kinetics and phase assemblage development in [PC + SCM] pastes. The DL is coupled with: 1) A fast Fourier transformation algorithm that reduces the dimensionality of training datasets (e.g., kinetic datasets), thus allowing the model to learn intrinsic composition-property correlations from a small database; and 2) A thermodynamic model that constrains the model, thus ensuring that predictions do not violate fundamental materials laws. The training and outcomes of the DL are ultimately leveraged to develop a simple, easy-to-use, closed-form analytical model capable of predicting hydration kinetics and phase assemblage development in [PC + SCM] pastes, using their initial composition and mixture design as inputs

Predicting Compressive Strength of Alkali-Activated Systems based on the Network Topology and Phase Assemblages using Tree-Structure Computing Algorithms

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 employed to predict the compressive strength of alkali-activated systems made from 26 aluminosilicate-rich precursors and distinct processing parameters. Results show that once the model is rigorously trained and optimized, the RF model can yield a priori, high-fidelity predictions of the compressive strength in relation to the physicochemical properties of aluminosilicate-rich precursors; processing parameters; and constraints. The topological network constraint provides the chemostructural properties and reactivity of the aluminosilicate-rich precursors. Whereas the thermodynamic constraint estimates the phase assemblages at different degrees of reaction of the aluminosilicate-rich precursors. Finally, the correlations between topological network constraint; phase assemblage; and compressive strength are demonstrated. When the topological network constraint equals 3.4, the alkali-activated systems can achieve their optimal compressive strength