Fracture Response of Metallic Particulate-reinforced Cementitious Composites: Insights from Experiments and Multiscale Numerical Simulations

This paper presents an experimental and numerical investigation into the fracture response of mortars containing up to 30% waste iron powder by volume as OPC-replacement. The iron powder-modified mortars demonstrate significantly improved strength and fracture properties as compared to the control mortars due to presence of elongated iron particulates in the powder. With a view to develop a predictive tool towards materials design of such particulate-reinforced systems, fracture responses of iron powder-modified mortars are simulated using a multiscale numerical approach. The approach implements multi-scale numerical homogenization involving cohesive zone-based damage at the matrix-inclusion interface and isotropic damage in the matrix to obtain composite constitutive response and fracture energy. Consequently, these results serve as input to macro-scale XFEM-based three-point-bend simulations of notched mortar beams. The simulated macroscopic fracture behavior exhibit excellent match with the experimental results. Thus, the numerical approach links the material microstructure to macroscopic fracture parameters facilitating microstructure-guided material design

Microstructure-guided numerical simulation to evaluate the influence of phase change materials (PCMs) on the freeze-thaw response of concrete pavements

The use of phase change materials in infrastructure has gained significant attention in the recent years owing to their robust thermal performance. This study implements a numerical simulation framework using finite element analysis to evaluate the influence of phase change materials (PCMs) on the thermal response of concrete pavements in geographical regions with significant winter weather conditions. The analysis is carried out at different length scales. The latent-heat associated with different PCMs is efficiently incorporated into the simulation framework. Besides, the numerical simulation framework employs continuum damage mechanics to evaluate the influence of PCMs on the freeze-thaw induced damage in concretes. The simulations show significant reductions in the freeze-thaw induced damage when PCMs are incorporated in concrete. The numerical simulation framework, developed here, provides efficient means of optimizing the material design of such durable PCM-incorporated concretes for pavements by tailoring the composition and material microstructure to maximize performance

Dynamics of Confined Water and its Interplay with Alkali Cations in Sodium Aluminosilicate Hydrate Gel: Insights from Reactive Force Field Molecular Dynamics

This paper presents the dynamics of confined water and its interplay with alkali cations in disordered sodium aluminosilicate hydrate (N-A-S-H) gel using reactive force field molecular dynamics. N-A-S-H gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. Despite attractive mechanical properties, geopolymers suffer from durability issues, particularly the alkali leaching problem which has motivated this study. Here, the dynamics of confined water and the mobility of alkali cations in N-A-S-H is evaluated by obtaining the evolution of mean squared displacements and Van Hove correlation function. To evaluate the influence of the composition of N-A-S-H on the water dynamics and diffusion of alkali cations, atomistic structures of N-A-S-H with Si/Al ratio ranging from 1 to 3 are constructed. It is observed that the diffusion of confined water and sodium is significantly influenced by the Si/Al ratio. The confined water molecules in N-A-S-H exhibit a multistage dynamic behavior where they can be classified as mobile and immobile water molecules. While the mobility of water molecules gets progressively restricted with an increase in Si/Al ratio, the diffusion coefficient of sodium also decreases as the Si/Al ratio increases. The diffusion coefficient of water molecules in the N-A-S-H structure exhibit a lower value than those of the calcium-silicate-hydrate (C-S-H) structure. This is mainly due to the random disordered structure of N-A-S-H as compared to the layered C-S-H structure. To further evaluate the influence of water content in N-A-S-H, atomistic structures of N-A-S-H with water contents ranging from 5-20% are constructed. Qn distribution of the structures indicates significant depolymerization of N-A-S-H structure with increasing water content. Increased conversion of Si–O–Na network to Si–O–H and Na–OH components with an increase in water content helps explain the alkali-leaching issue in fly ash-based geopolymers observed macroscopically. Overall, the results in this study can be used as a starting point towards multiscale simulation-based design and development of durable geopolymers

Realistic atomic structure of fly ash-based geopolymer gels: Insights from molecular dynamics simulations

Geopolymers, synthesized through alkaline activation of aluminosilicates, have emerged as a sustainable alternative for traditional ordinary Portland cement. In spite of the satisfactory mechanical performance and sustainability-related benefits, the large scale acceptance of geopolymers in the construction industry is still limited due to poor understanding of the composition-property relationships. Molecular simulation is a powerful tool to develop such relationships, provided that the adopted molecular structure represents the experimental data effectively. Toward this end, this paper presents a new molecular structure of sodium aluminosilicate hydrate geopolymer gels, inspired from the traditional calcium silicate hydrates gel. In contrast to the existing model—where water is uniformly distributed in the structure—we present a layered-but-disordered structure. This new structure incorporates water in the interlayer space of the aluminosilicate network. The structural features of the new proposed molecular structure are evaluated in terms of both short- and medium-range order features such as pair distribution functions, bond angle distributions, and structure factor. The structural features of the newly proposed molecular structure with interlayer water show better correlation with the experimental observations as compared to the existing traditional structure signifying an increased plausibility of the proposed structure. The proposed structure can be adopted as a starting point toward the realistic multiscale simulation-based design and development of geopolymers

Fracture Toughness of Fly Ash-Based Geopolymer Gels: Evaluations Using Nanoindentation Experiment and Molecular Dynamics Simulation

This paper presents the fracture toughness of sodium aluminosilicate hydrate (N-A-S-H) gel formed through alkaline activation of fly ash. While the fracture toughness of N-A-S-H is obtained experimentally from nanoindentation experiment implementing the principle of conservation of energy, the numerical investigation is performed via reactive force field molecular dynamics. A statistically significant number of indentations are performed on geopolymer paste yielding frequency distribution of Young’s modulus. Four distinct peaks are observed in the frequency distribution plot from which the peak corresponding to N-A-S-H was separated using statistical deconvolution technique. The young’s modulus of N-A-S-H, thus obtained from statistical deconvolution shows excellent match with the values reported in the literature, thus confirming successful identification of indentations corresponding to N-A-S-H. From the load-penetration depth responses of N-A-S-H, fracture toughness was obtained following the principle of conservation of energy. The experimental fracture toughness shows good correlation with the simulated fracture toughness of N-A-S-H, obtained from reactive force field molecular dynamics. The fracture toughness of N-A-S-H presented in this paper paves the way for multiscale simulation-based design of tougher geopolymer binders

Prediction of Concrete Strengths Enabled by Missing Data Imputation and Interpretable Machine Learning

Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes requires a large, complete, and consistent dataset. However, the availability of such datasets is limited as the datasets often suffer from incompleteness because of missing data corresponding to different input features, which makes the development of robust ML-based predictive models challenging. Besides, as the degree of complexity in these ML models increases, the interpretation of the results becomes challenging. These interpretations of results are critical towards the development of efficient materials design strategies for enhanced materials performance. To address these challenges, this paper implements different data imputation approaches for enhanced dataset completeness. The imputed dataset is leveraged to predict the compressive and tensile strength of concrete using various hyperparameter-optimized ML approaches. Among all the approaches, Extreme Gradient Boosted Decision Trees (XGBoost) showed the highest prediction efficacy when the dataset is imputed using k-nearest neighbors (kNN) with a 10-neighbor configuration. To interpret the predicted results, SHapley Additive exPlanations (SHAP) is employed. Overall, by implementing efficient combinations of data imputation approach, machine learning, and data interpretation, this paper develops an efficient approach to evaluate the composition-strength relationship in concrete. This work, in turn, can be used as a starting point toward the design and development of various performance-enhanced and sustainable concretes

P09-15. Selection of higher avidity HLA-restricted T cell responses as a viral adaptation strategy

Loss of immune reactivity due to HIV mutational escape is well described. Data generated from a large population-based study (n>800) suggested that certain CD8 T cell epitopes are created as a result of HIV adaptation and are associated with enhanced viral replication. Here we sought to investigate the HLA-restricted T-cell responses associated with seven such adaptations

Elucidating the auxetic behavior of cementitious cellular composites using finite element analysis and interpretable machine learning

With the advent of 3D printing, auxetic cellular cementitious composites (ACCCs) have recently garnered significant attention owing to their unique mechanical performance. To enable seamless performance prediction of the ACCCs, interpretable machine learning (ML)-based approaches can provide efficient means. However, the prediction of Poisson’s ratio using such ML approaches requires large and consistent datasets which is not readily available for ACCCs. To address this challenge, this paper synergistically integrates a finite element analysis (FEA)-based framework with ML to predict the Poisson’s ratios. In particular, the FEA-based approach is used to generate a dataset containing 850 combinations of different mesoscale architectural void features. The dataset is leveraged to develop an ML-based prediction tool using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent prediction efficacy. To shed light on the relative influence of the design parameters on the auxetic behavior of the ACCCs, Shapley additive explanations (SHAP) is employed, which establishes the volume fraction of voids as the most influential parameter in inducing auxetic behavior. Overall, this paper develops an efficient approach to evaluate geometry-dependent auxetic behaviors for cementitious materials which can be used as a starting point toward the design and development of auxetic behavior in cementitious composites

Dynamic compressive behavior of metallic particulate-reinforced cementitious composites: SHPB experiments and numerical simulations

An experimental and numerical evaluation on the dynamic compressive response of mortars containing up to 20% waste iron powder as sand replacement is presented in this paper. The dynamic response is evaluated using split Hopkinson pressure bar (SHPB) apparatus under high strain rates (up to 250/s). The elongated iron particulates present in the iron powder-incorporated mortars warrant significantly improved compressive strength and energy absorption capacity at high strain rates. Multiscale numerical simulations are performed with a view to develop a tool that facilitates microstructure-guided design of these particulate-reinforced mortars for efficient dynamic performance. The dynamic compressive response of particulate-reinforced mortars is simulated adopting a numerical approach that incorporates strain rate-dependent damage in a continuum micromechanics framework. The simulated dynamic compressive strengths and energy absorption capacities for mortars with various iron powder content exhibit good correlation with the experimental observations thereby validating the efficacy of the simulation approach

Cooling rate effects on the structure of 45S5 bioglass: Insights from experiments and simulations

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions materials are extensively used for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of its atomic structure remain debated. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline structure is highly dependent on their thermal history, namely, the cooling rate used during quenching. Herein, combining molecular dynamics (MD) simulations with cooling rates ranging over several orders of magnitude and experimental studies using nuclear magnetic resonance (NMR), we investigate the structure of the nominal 45S5 bioglass composition. These results suggest that the MD simulation results when extrapolated to experimental cooling rates can provide a reasonable estimate of the structure of 45S5 bioglass. Finally, based on these results, we suggest the propensity of the phosphate group to form isolated orthophosphate species. Overall, these results reconcile the simulation and experimental results on the structure of 45S5 bioglass, and particularly on the speciation of the phosphate group, which may be key in controlling the bioactivity of 45S5 bioglass