Machine Learning for High-Fidelity Prediction of Cement Hydration Kinetics in Blended Systems

The production of ordinary Portland cement (OPC), the most broadly utilized man-made material, has been scrutinized due to its contributions to global anthropogenic CO2 emissions. Thus -- to mitigate CO2 emissions -- mineral additives have been promulgated as partial replacements for OPC. However, additives -- depending on their physiochemical characteristics -- can exert varying effects on OPC\u27s hydration kinetics. Therefore -- in regards to more complex systems -- it is infeasible for semi-empirical kinetic models to reveal the underlying nonlinear composition-property (i.e., reactivity) relationships. In the past decade or so, machine learning (ML) has arisen as a promising, holistic approach to predict the properties of heterogeneous materials, even without an across-the-board comprehension of the underlying composition-properties correlations. This paper describes the use of a Random Forests (RF) model to enable high-fidelity predictions of time-dependent hydration kinetics of OPC-based systems -- more specifically [OPC + mineral additive(s)] systems -- using the system\u27s physiochemical attributes as inputs. Results show that the RF model can also be used to formulate mixture designs that satisfy user-imposed kinetics-related criteria. Lastly, the presented results can be expanded to formulate mixture designs that satisfy target kinetic criteria, even without knowledge of the underlying kinetic mechanisms

Proportion and performance evaluation of fly ash-based geopolymer and its application in engineered composites

It is well known that the use of Portland cement (PC) in concrete construction is causing severe environmental issues primarily due to vast quantity of carbon dioxide released to the atmosphere during the manufacture of PC. On the other hand, disposal of industrial solid wastes such as fly ash and slag in landfills is creating another threat to the environment. The development of a fly ash geopolymer binder, produced from the reaction of fly ash and alkaline solution, may replace Portland cement as a construction material and at the same, reduce the disposal of fly ash in landfills. This dissertation reports the efforts in optimizing mix proportion, predictive modeling on early age properties, shrinkage control and mechanical performance of an engineered composite made with fly ash-based geopolymer. This dissrtation consists of four papers: (1) Optimization of Mix Design Parameters on Thermal, Setting and Stiffening Behaviors of High Calcium Fly Ash Geopolymer; (2) Prediction of Strength, Setting Time and Heat Generation of Fly Ash Geopolymer Using Artificial Neural Network; (3) The Effects of Activator and Shrinkage Reducing Admixture on Shrinkage Behavior of Fly Ash Geopolymer, and (4) The Effect of Slag on Mechanical Properties of Engineered Geopolymer Composite. Due to the lack of knowledge to optimize the mix proportion of fly ash based geopolymer in the published literature, Paper 1 is focused on the effects of design parameters including SiO2/Na2O mole ratio (Module), solute (NaOH and Na2SiO3) mass concentration on the fresh and hardened properties (i.e., setting time, compressive strength and heat of hydration). The knowledge gained from this study is expected to assist in the optimization of the mix proportions for thefly ash geopolymer. Results from Paper 1 have shown that modules less than 1.5, concentrations between 40% and 50%, L/F ratios less than 0.40, and higher curing temperature, such as 50oC, were preferred to synthesize a geopolymer system using high calcium fly ash. In Paper 2, an artificial neural network (ANN) approach was applied to analyze the complexity between geopolymer properties and various parameters forgeopolymer mix proportion design. The predictive models for setting time and compressive strength of geopolymer were established for the ease of mix design. Paper 2 concluded that ANN was an effective tool for parametric study of the properties of fly ash geopolymer. The effects of geopolymer mix design parameters on setting time, compressive strength and heat generation were discussed in accordance with the prediction profiler generated by the ANN models. The proposed model can be used as a guidance for high calcium fly ash geopolymer mix design in the future. Shrinkage of cement-based materials is a major cause of cracking. The work discussed in Paper 3 was to characterize the shrinkage behavior (e.g., free drying shrinkage and restrained ring shrinkage) of fly ash-based geopolymer in comparison with that of PC paste. The effects of activator (Module and Concentration) and shrinkage reducing admixture (SR) on the shrinkage behavior of fly ash-based geopolymer have been explored. In addition, the flowability of the geopolymer using a mini slump test and compressive strength test were also carried out. The results indicate that the fly ash geopolymer has comparable flowability properties as compared to that of PC. SR slightly decreased flowability of PC and fly ash geopolymer. It was also found that the drying shrinkage of fly ash geopolymer was of similar magnitude to that of PC, but was not due to mass loss for fly ash geopolymer. The SR significantly reduced the drying shrinkage of fly ash geopolymer up to 52% as well as in PC. The SR decreased the restrained shrinkage up to 16%, delayed the cracking time, reduced the crack width and lowered the cracking potential for both PC and fly ash geopolymer. The fly ash geopolymer mixtures had lower cracking potential than PC. The effects of Module and Concentration on drying shrinkage and restrained ring shrinkage were also concluded. The last paper (Paper 4) investigated the mechanical performance of fly ash-based geopolymer in a fiber reinforced composite, namely an engineered geopolymer composite (EGC). Fly ash was replaced with slag in the geopolymer. The physical and chemical interactions of these two cementitious materials have resulted in a high strength (up to 110 MPa) and workable EGC. The mechanical properties including compressive strength, tensile strength, tensile strain capacity, toughness, elasticity, flexural bending strength, ductility and pullout bond strength were assessed. Experimental results in Paper 4 revealed that all EGCs exhibited strain hardening behavior. Twenty percent slag addition improved the engineering strength most. However, as slag addition increased, the tensile strain capacity, ultimate deflections, toughness and ductility decreased. In addition, bond strength can be estimated precisely based on the compressive strength of EGCs

Primjena umjetne neuronske mreže u predviđanju čvrstoće na raslojavanje cementne iverice proizvedene od divovske trske i ostataka od prerade šećerne trske

The present article investigates the microstructure of the cement matrices and the products of cement hydration by means of scanning electron microscopy, Fourier transform infrared spectroscopy and X-Ray diffraction. Then, the internal bonding strength (IB) is measured for the mixtures containing various amounts of nanosilica (NS), reed and bagasse particles. Finally, an Artificial Neural Network (ANN) is trained to reproduce these experimental results. The results show that the hardened cement paste including NS features the highest level of C-S-H. However, it has a lower level of C-S-H polymerization if reed or bagasse particles are applied. A relatively new dense microstructural degree is considered in the cement pastes containing NS, and a lower agglomeration is observed in the samples including reed or bagasse particles with NS. According to the microstructural analysis, the addition of NS to the samples containing reed or bagasse particles increases the unhydrated amount of C2S and C3S in the cement paste due to the decrease in the water needed for fully hydrated cement grains through portlandite (Ca(OH)2), C-S-H and ettringite increase. Besides, it is shown that the ANN prediction model is a useful, reliable and quite effective tool for modeling IB of cement-bonded particleboard (CBPB). It is indicated that the mean absolute percentage errors (MAPE) are 1.98 % and 1.45 % in the prediction of the IB values for the training and testing datasets, respectively. The determination coeffi cients (R2) of the training and testing data sets are 0.972 and 0.997 in the prediction of the bonding strength by ANN, respectively.U radu se opisuje istraživanje mikrostrukture cementnih matrica i proizvoda hidratacije cementa uz pomoć pretražnoga elektronskog mikroskopa, Fourierove transformirane infracrvene spektroskopije i rendgenske difrakcije. Pritom je izmjerena i čvrstoća na raslojavanje (IB) za smjese koje sadržavaju različite količine čestica nanosilike (NS), trske i ostataka od prerade šećerne trske. Na kraju su uz pomoć umjetne neuronske mreže (ANN) reproducirani eksperimentalni podatci. Rezultati su pokazali da otvrdnuta cementna pasta s nanosilikom ima najvišu razinu C-S-H. Međutim, ako se rabe čestice trske ili ostataka od prerade šećerne trske, cementna pasta ima niži stupanj polimerizacije C-S-H. Detaljno je analiziran relativno nov stupanj gustoće mikrostrukture cementne paste koja sadržava nanosiliku, pri čemu su uočene i manje nakupine u uzorcima koji su, osim nanosilike, sadržavali i čestice trske ili ostataka od njezine prerade. Prema analizi mikrostrukture, dodatkom nanosilike uzorcima koji sadržavaju čestice trske ili ostataka od prerade šećerne trske povećava se nehidratizirana količina C2S i C3S u cementnoj pasti zbog smanjenja vode potrebne za potpunu hidratizaciju cementnih zrna putem portlandita (Ca(OH)2) te povećanjem C-S-H i etringita. Osim toga, pokazalo se da je ANN model predviđanja koristan, pouzdan i vrlo učinkovit alat za modeliranje čvrstoće cementne iverice na raslojavanje. Srednja apsolutna pogreška (MAPE) u predviđanju čvrstoće na raslojavanje za eksperimentalne i izmjerene skupove podataka iznosi 1,98 %, odnosno 1,45 %. Koefi cijenti korelacije R2 eksperimentalnih i izmjerenih skupina podataka u predviđanju čvrstoće na raslojavanje uz pomoć ANN modela iznose 0,972 odnosno 0,997

Early-Age Shrinkage of Ultra High-Performance Concrete: Mitigation and Compensating Mechanisms

The very high mechanical strength and enhanced durability of ultra high-performance concrete (UHPC) make it a strong contender for several concrete applications. However, UHPC has a very low water-to-cement ratio, which increases its tendency to undergo early-age shrinkage cracking with a risk of decreasing its long-term durability. To reduce the magnitude of early-age shrinkage and cracking potential, several mitigation strategies have been proposed including the use of shrinkage reducing admixtures, internal curing methods (e.g. superabsorbent polymers), expansive cements and extended moist curing durations. To appropriately utilize these strategies, it is important to have a complete understanding of the driving forces behind early-age volume change and how these shrinkage mitigation methods work from a materials science perspective to reduce shrinkage under filed like conditions. This dissertation initially uses a first-principles approach to understand the interrelation mechanisms between different shrinkage types under simulated field conditions and the role of different shrinkage mitigations methods. The ultimate goal of the dissertation is to achieve lower early-age shrinkage and cracking risk concrete along with reducing its environmental and economic impact. As a result, a novel environmentally friendly shrinkage reducing technique based on using partially hydrated cementitious materials (PHCM) from waste concrete is proposed. The PHCM principle, mechanisms and efficiency were evaluated compared to other mitigation methods. Furthermore, the potential of replacing cement with wollastonite microfibers was investigated as a new strategy to produce UHPC with lower carbon foot-print, through reducing the cement production. Finally, an artificial neural networks (ANN) model for early-age autogenous shrinkage of concrete was proposed. The evidence and insights provided by the experiments can be summarized in: drying and autogenous shrinkage are dependant phenomena and applying the conventional superposition principle will lead to an overestimation of the actual autogenous shrinkage, adequately considering in-situ conditions in testing protocols should allow gaining a better understanding of shrinkage mitigation mechanisms, the PHCM technique provides a passive internal restraining system that resists deformation as early as the cementitious materials are mixed, wollastonite microfibers can act as an internal restraint for shrinkage, reinforcing the microstructure at the micro-crack level and leading to an enhancement of the early-age engineering properties, along with gaining environmental benefits, and ANN showed success in predicting autogenous shrinkage under simulated field conditions

Modeling Hydration Kinetics Of Sustainable Cementitious Binders Using An Advanced Nucleation And Growth Approach

Supplementary cementitious materials (SCMs) are utilized to partially substitute Portland cement (PC) in binders, reducing carbon-footprint and maintaining excellent performance. Nonetheless, predicting the hydration kinetics of [PC + SCM] binders is challenging for current analytical models due to the extensive diversity of chemical compositions and molecular structures present in both SCMs and PC. This study develops an advanced phase boundary nucleation and growth (pBNG) model to yield a priori predictions of hydration kinetics—i.e., time-resolved exothermic heat release profiles—of [PC + SCM] binders. The advanced pBNG model integrates artificial intelligence as an add-on, enabling it to accurately simulate hydration kinetics for [PC + SCM] binders. This study utilizes a database that includes calorimetry profiles of 710 [PC + SCM] binders, encompassing a diverse range of commonly used SCMs as well as both commercial and synthetic PCs. The results show that the advanced pBNG model predicts the heat evolution profiles of [PC + SCM] in a high-fidelity manner

Machine Learning Enables Prompt Prediction of Hydration Kinetics of Multicomponent Cementitious Systems

Carbonaceous (e.g., limestone) and aluminosilicate (e.g., calcined clay) mineral additives are routinely used to partially replace ordinary portland cement in concrete to alleviate its energy impact and carbon footprint. These mineral additives—depending on their physicochemical characteristics—alter the hydration behavior of cement; which, in turn, affects the evolution of microstructure of concrete, as well as the development of its properties (e.g., compressive strength). Numerical, reaction-kinetics models—e.g., phase boundary nucleation-and-growth models; which are based partly on theoretically-derived kinetic mechanisms, and partly on assumptions—are unable to produce a priori prediction of hydration kinetics of cement; especially in multicomponent systems, wherein chemical interactions among cement, water, and mineral additives occur concurrently. This paper introduces a machine learning-based methodology to enable prompt and high-fidelity prediction of time-dependent hydration kinetics of cement, both in plain and multicomponent (e.g., binary; and ternary) systems, using the system’s physicochemical characteristics as inputs. Based on a database comprising hydration kinetics profiles of 235 unique systems—encompassing 7 synthetic cements and three mineral additives with disparate physicochemical attributes—a random forests (RF) model was rigorously trained to establish the underlying composition-reactivity correlations. This training was subsequently leveraged by the RF model: to predict time-dependent hydration kinetics of cement in new, multicomponent systems; and to formulate optimal mixture designs that satisfy user-imposed kinetics criteria

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

Advances in Binders for Construction Materials

The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry

Research posters’ eBook: according to 1st WORKSHOP with “Focus on experimental testing of cement based materials”

COST Action TU 140