Integrating Multiscale Numerical Simulations with Machine Learning to Predict the Strain Sensing Efficiency of Nano-Engineered Smart Cementitious Composites

Prediction of in-situ strain sensing efficiency of self-sensing cementitious composites using machine learning (ML) requires a large, representative, consistent, and accurate dataset. However, such large experimental dataset is not readily available. Moreover, the success of the ML approach depends on its ability to abide by the fundamental laws of physics. To address these challenges this paper synergistically integrates a validated finite element analysis (FEA)-based multiscale simulation framework with ML to predict the strain-sensing ability of self-sensing cementitious composites enabled by incorporating nano-engineered interfaces. The multiscale simulation framework is leveraged to develop a balanced, representative, complete, and consistent dataset containing 3000 combinations of strain-dependent electromechanical responses. This large dataset is used to predict the strain-sensing ability of the nanoengineered cementitious composites using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent prediction efficacy. This paper also applies a Shapley Additive Explanations (SHAP) algorithm to interpret the NN predictions in light of the relative importance of different design parameters on the strain-sensing ability of the composite. Overall, the synergistic and comprehensive approach presented here can be used as a starting point toward the development of reliable performance standards to accelerate the acceptance of these self-sensing cementitious composites for large-scale applications

Elucidating the Interfacial Bonding Behavior of Over-Molded Hybrid Fiber Reinforced Polymer Composites: Experiment and Multiscale Numerical Simulation

This paper implements molecular dynamics (MD) simulation using reactive force field (ReaxFF) to evaluate the atomistic origin of the interfacial behavior in the overmolded hybrid unidirectional continuous carbon fiber low-melt PAEK (CFR- LMPAEK)-short carbon fiber reinforced PEEK (SFR-PEEK) polymer composites. From the MD simulation, it was observed that the interfacial properties improve with increasing maximum processing temperature and injection pressure although such an improving trajectory gets saturated beyond specific limits. The interfacial strength and fracture response of the hybrid polymer system at the interface are also evaluated. The mechanical responses obtained from MD simulation are used as adhesive properties in the macroscale finite element analysis (FEA)-based single lap joint (SLJ) model where the interfacial behavior between the adherends (CFR-LMPAEK and SFR-PEEK) is implemented using cohesive zone model (CZM). The simulated FE results show a good correlation with the SLJ experimental data. Thus, by linking the interfacial properties at the molecular scale to the performance of the interfacial bond at the macroscale, the comprehensive approach presented here opens up various efficient avenues toward atomistically engineered performance tuning in hybrid overmolded fiber-reinforced polymer composites to meet desired large-scale performance needs

Influence of Microencapsulated Phase Change Materials (PCMs) on the Chloride Ion Diffusivity of Concretes Exposed to Freeze-thaw Cycles: Insights from Multiscale Numerical Simulations

Use of phase change materials (PCMs) to tailor the thermal performance of concretes by efficient energy storage and transmission has gained traction in recent years. This study incorporates microencapsulated PCMs as sand-replacement in concrete bridge decks and performs numerical simulation involving multiple interactive length scales to elucidate the influence of PCM-incorporation in concretes subjected to combined freeze-thaw and chloride ingress-induced deterioration. The simulations show significant increase in durability against combined freeze-thaw and chloride ingress-induced deterioration in concretes when microencapsulated PCMs are incorporated. In addition, a reliability-based probabilistic analysis shows significant increase in life expectancy of bridge decks with PCM-incorporation. The numerical approach presented here provides efficient means to develop design strategies to tune dosage and transition temperature of PCMs to maximize durability of concrete structures in regions that experience significant winter weather conditions

Influence of Microencapsulated Phase Change Materials (PCMs) on the Chloride Ion Diffusivity of Concretes Exposed to Freeze-thaw Cycles: Insights from Multiscale Numerical Simulations

Use of phase change materials (PCMs) to tailor the thermal performance of concretes by efficient energy storage and transmission has gained traction in recent years. This study incorporates microencapsulated PCMs as sand-replacement in concrete bridge decks and performs numerical simulation involving multiple interactive length scales to elucidate the influence of PCM-incorporation in concretes subjected to combined freeze-thaw and chloride ingress-induced deterioration. The simulations show significant increase in durability against combined freeze-thaw and chloride ingress-induced deterioration in concretes when microencapsulated PCMs are incorporated. In addition, a reliability-based probabilistic analysis shows significant increase in life expectancy of bridge decks with PCM-incorporation. The numerical approach presented here provides efficient means to develop design strategies to tune dosage and transition temperature of PCMs to maximize durability of concrete structures in regions that experience significant winter weather conditions

Finite Element-Based Numerical Simulations to Evaluate the Influence of Wollastonite Microfibers on the Dynamic Compressive Behavior of Cementitious Composites

This paper investigates the dynamic compressive behavior of wollastonite fiber-reinforced cementitious mortars using multiscale numerical simulations. The rate dependent behavior of the multiphase heterogeneous systems is captured in a multiscale framework that implements continuum damage towards effective property prediction. The influence of wollastonite fiber content (% by mass) as cement replacement on the dynamic compressive strength and energy absorption capacity is thereafter elucidated. An average compressive strength gain of 40% is obtained for mortars with 10% wollastonite fiber content as cement replacement, as compared to the control mortar at a strain rate of 200/s. The rate dependent constitutive responses enable the computation of energy absorption, which serves as a comparative measure for elucidating the material resistance to impact loads. Approximately a 45% increase in the dynamic energy absorption capacity is observed for the mixture containing 10% wollastonite fibers, as compared to the control case. Overall, the study establishes wollastonite fibers as a sustainable and dynamic performance-enhanced alternative for partial cement replacement. Moreover, the multiscale numerical simulation approach for performance prediction can provide an efficient means for the materials designers and engineers to optimize the size and dosage of wollastonite fibers for desired mechanical performance under dynamic loading conditions

Fracture response of wollastonite fiber-reinforced cementitious composites: Evaluation using micro-indentation and finite element simulation

The paper presents indentation studies on wollastonite fiber incorporated cementitious systems. The acicular nature of the fibers is poised to delay the coalescence of micro-cracks in such systems thus leading to tougher building materials. Towards that end, load-penetration depth results from the indentation studies are employed to ascertain elastic and fracture properties of wollastonite-incorporated cementitious composites. While up to 10% mass-based cement-replacement by wollastonite results in comparable elastic moduli as compared to conventional binders, the fracture toughness increases by as much as 33%. In order to gain insights into the toughening mechanisms brought about by the fine fibers, a microstructure-guided numerical simulation strategy is adopted towards effective fracture performance prediction. The performance enhancement of the wollastonite systems is corroborated by the finite element-based simulations carried out on the virtual microstructures that accurately capture the heterogeneity of such systems. Besides fracture performance enhancement, the wollastonite-incorporated cementitious systems also contribute towards development of sustainable cement replacing compositions. Moreover, the micromechanical predictive tool developed in this study facilitate efficient means to tune the materials structure for desired performance

Predicting the Near Field Underwater Explosion Response of Coated Composite Cylinders using Multiscale Simulations, Experiments, and Machine Learning

Prediction of underwater explosion response of coated composite cylinders using machine learning (ML) requires a large, consistent, accurate, and representative dataset. However, such reliable large experimental dataset is not readily available. Besides, the ML algorithms need to abide by the fundamental laws of physics to avoid non-physical predictions. To address these challenges, this paper synergistically integrates ML with high-throughput multiscale finite element (FE) simulations to predict the response of coated composite cylinders subjected to nearfield underwater explosion. The simulated responses from the multiscale approach correlate very well with the experimental observations. After validation of the multiscale approach, a representative and consistent dataset containing more than 3800 combinations is developed using high-throughput multiscale simulation by varying the fiber/matrix/coating material properties, coating thickness as well as experimental variables such as explosive energy and stand-off distance. The dataset is leveraged to predict the response of coated composite cylinders subjected to nearfield underwater explosion using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent predictions. Overall, the synergistic approach powered by physics-based simulations presented here can potentially enable materials scientists and engineers to make intelligent, informed decisions in the purview of innovative design strategies for underwater explosion mitigation in composite structures

Finite Element-Based Numerical Simulations to Evaluate the Influence of Wollastonite Microfibers on the Dynamic Compressive Behavior of Cementitious Composites

This paper investigates the dynamic compressive behavior of wollastonite fiber-reinforced cementitious mortars using multiscale numerical simulations. The rate dependent behavior of the multiphase heterogeneous systems is captured in a multiscale framework that implements continuum damage towards effective property prediction. The influence of wollastonite fiber content (% by mass) as cement replacement on the dynamic compressive strength and energy absorption capacity is thereafter elucidated. An average compressive strength gain of 40% is obtained for mortars with 10% wollastonite fiber content as cement replacement, as compared to the control mortar at a strain rate of 200/s. The rate dependent constitutive responses enable the computation of energy absorption, which serves as a comparative measure for elucidating the material resistance to impact loads. Approximately a 45% increase in the dynamic energy absorption capacity is observed for the mixture containing 10% wollastonite fibers, as compared to the control case. Overall, the study establishes wollastonite fibers as a sustainable and dynamic performance-enhanced alternative for partial cement replacement. Moreover, the multiscale numerical simulation approach for performance prediction can provide an efficient means for the materials designers and engineers to optimize the size and dosage of wollastonite fibers for desired mechanical performance under dynamic loading conditions

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

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