Strain sensing ability of metallic particulate reinforced cementitious composites: Experiments and microstructure-guided finite element modeling

Yang, Pu; Chowdhury, Swaptik; Neithalath, Narayanan

doi:10.1016/j.cemconcomp.2018.04.004

Cited by 23 publications

(15 citation statements)

References 45 publications

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“…The thickness of the conductive coating is 10 microns [15]. The material properties of sand, conductive coating and ITZ are reported in Table 1 [15,51,56,59,[63][64][65][66][67]. The conductivity of the matrix is reduced by 20% owing to the presence of 25% GGBFS by weight [76,77] whereas the values of Young's modulus and Poisson's ratio of the binder matrix are considered same as that of HCP [78].…”

Section: Comparison Of Simulation Results With Experimental Observationsmentioning

confidence: 99%

“…Similar 2D unit cells are successfully adopted for cementitious materials in [33,56]. Table 1, are adopted from [15,51,56,59,[63][64][65][66][67]. In order to evaluate the influence of debonding on the FCR, a dimensionless scalar parameter, fractional interface debonding is introduced which can be defined as the fraction of the total perimeter of inclusions that has de-bonded completely ( =1).…”

Section: Influence Of Conductive Coating On the Strain Sensing And Damentioning

confidence: 99%

“…The simulation yields the electric field and current density distribution in the unit cell which when volumetrically averaged by a post-processing module yields the average electrical conductivity as per Equation 25 [51,52]. representation, the average electrical conductivities ( ) are expressed in terms of the fractional change in resistance (FCR) which is the ratio of the change in resistance (∆ ) and the bulk resistance of mechanically undeformed microstructure ( 0 ) as shown in Equation 26[51]. is the conductivity response of the mechanically undeformed microstructure.…”

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confidence: 99%

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A microstructure-guided numerical approach to evaluate strain sensing and damage detection ability of random heterogeneous self-sensing structural materials

Nayak

Das

2019

Computational Materials Science

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Heterogeneous self-sensing materials that respond electrically to mechanical strains enable real time health monitoring of structures. To facilitate design and applicability of such smart materials with piezo-resistivity, a finite element-based numerical framework is being proposed in this paper for evaluation of electro-mechanical response and strain-sensing ability. Intrinsic heterogeneous nature of such composites warrants the need for microstructure-based study to have an insight into the effect of microstructural configuration on the macro-scale response. The microstructure-guided simulation framework, presented in this paper, implements interfacial debonding at the matrix-inclusion interface using a coupled interface damage-cohesive zone model and incorporates an isotropic damage model in the matrix under applied strain in the post-peak regime to obtain the deformed/damaged microstructure which is subjected to an electrical potential to simulate change in resistance due to applied strain. The applicability of the simulation framework is confirmed through its successful implementation on a smart structural material containing nano-engineered conductive coating at the inclusion-matrix interfaces. The predicted electro-mechanical responses correspond very well with the experimental observations and thus, the model has the potential to help develop design strategies to tailor the microstructure in these self-sensing materials for efficient performance.

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Section: Comparison Of Simulation Results With Experimental Observationsmentioning

confidence: 99%

Section: Influence Of Conductive Coating On the Strain Sensing And Damentioning

confidence: 99%

mentioning

confidence: 99%

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A microstructure-guided numerical approach to evaluate strain sensing and damage detection ability of random heterogeneous self-sensing structural materials

Nayak

Das

2019

Computational Materials Science

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“…A constant Poisson's ratio of 0.2 is considered for all the materials except the iron particles since a range of 0.17-0.22 for the same yields insignificant changes in the results [59,60]. A Poisson's ratio of 0.3 is adopted for iron particulates [39,61]. Owing to lack of data.…”

Section: Blocks: Materials and Damage Definitionmentioning

confidence: 99%

“…the matrix properties are assigned to the iron particulate-HCP interface elements as well. Similar properties have been successfully adopted in [59,61]. The bulk and shear moduli are thus computed from the Young's modulus and the Poisson's ratio for each phase of the micro-scale unit cell.…”

Section: Blocks: Materials and Damage Definitionmentioning

confidence: 99%

A Peridynamics-Based Micromechanical Modeling Approach for Random Heterogeneous Structural Materials

et al. 2020

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This paper presents a peridynamics-based micromechanical analysis framework that can efficiently handle material failure for random heterogeneous structural materials. In contrast to conventional continuum-based approaches, this method can handle discontinuities such as fracture without requiring supplemental mathematical relations. The framework presented here generates representative unit cells based on microstructural information on the material and assigns distinct material behavior to the constituent phases in the random heterogenous microstructures. The framework incorporates spontaneous failure initiation/propagation based on the critical stretch criterion in peridynamics and predicts effective constitutive response of the material. The current framework is applied to a metallic particulate-reinforced cementitious composite. The simulated mechanical responses show excellent match with experimental observations signifying efficacy of the peridynamics-based micromechanical framework for heterogenous composites. Thus, the multiscale peridynamics-based framework can efficiently facilitate microstructure guided material design for a large class of inclusion-modified random heterogenous materials.

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Fracture response of metallic particulate-reinforced cementitious composites: Insights from experiments and multiscale numerical simulations

Nayak¹,

Krishnan²,

Das³

2019

Cement and Concrete Composites

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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 powdermodified 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.

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Strain sensing ability of metallic particulate reinforced cementitious composites: Experiments and microstructure-guided finite element modeling

Cited by 23 publications

References 45 publications

A microstructure-guided numerical approach to evaluate strain sensing and damage detection ability of random heterogeneous self-sensing structural materials

A microstructure-guided numerical approach to evaluate strain sensing and damage detection ability of random heterogeneous self-sensing structural materials

A Peridynamics-Based Micromechanical Modeling Approach for Random Heterogeneous Structural Materials

Fracture response of metallic particulate-reinforced cementitious composites: Insights from experiments and multiscale numerical simulations

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