Microstructure informed micromechanical modelling of hydrated cement paste: Techniques and challenges

Zhang, Hongzhi; Xu, Yading; Gan, Yidong; Chang, Ze; Schlangen, Erik; Šavija, Branko

doi:10.1016/j.conbuildmat.2020.118983

Cited by 46 publications

(9 citation statements)

References 122 publications

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“…Under the hierarchical modelling scheme [3,15], a so-called uncoupled volume averaging upscaling method [37,44,51] can be applied to bridge the micro and meso scales. In this upscaling method, the simulated global mechanical performance of composites at lower scale are directly assigned as the local mechanical properties of the matrix in the upper scale models.…”

Section: Discussionmentioning

confidence: 99%

“…In pursuit of fundamental understanding of the link between the microstructure and its macroscopic mechanical performance, mechanical models using microstructural information as input are generally used. Such models are therefore considered as microstructure-informed [3]. At the meso-scale, HCP is usually considered as a homogeneous and isotropic continuum.…”

Section: Introductionmentioning

confidence: 99%

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Discrete lattice fracture modelling of hydrated cement paste under uniaxial compression at micro-scale

Jiang

Zhang

Chang

et al. 2020

Construction and Building Materials

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrete lattice fracture modelling of hydrated cement paste under uniaxial compression at micro-scale

Jiang

Zhang

Chang

et al. 2020

Construction and Building Materials

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“…This also contributes to the brittle post-peak behavior. Possible solutions would be deriving the local elastoplastic constitutive relations from experiments or simulations at a lower scale (i.e., microscale) as performed in [40,41,51,52,[60][61][62]. Nevertheless, the purpose of this paper is to show the potential of the mesoscale model and not to have an 'exact' match.…”

Section: Calibrationmentioning

confidence: 99%

Effect of printing parameters on interlayer bond strength of 3D printed limestone-calcined clay-based cementitious materials: An experimental and numerical study

Chen

Jansen

Zhang

et al. 2020

Construction and Building Materials

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110

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“…Analytical and numerical models, commonly based on homogenization, are increasingly being used to predict the mechanical properties (compressive/tensile strength, elastic modulus) of concrete [12,13]. In such models, detailed information about the microstructure, spatial distribution, and mechanical properties of individual phases are required [14,15]. In addition, many of these models are computationally expensive, which prohibits their practical applicability.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance

Wan

Šavija

2021

Materials

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Compressive strength is the most significant metric to evaluate the mechanical properties of concrete. Machine learning (ML) methods have shown promising results for predicting compressive strength of concrete. However, at present, no in-depth studies have been devoted to the influence of dimensionality reduction on the performance of different ML models for this application. In this work, four representative ML models, i.e., Linear Regression (LR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are trained and used to predict the compressive strength of concrete based on its mixture composition and curing age. For each ML model, three kinds of features are used as input: the eight original features, six Principal Component Analysis (PCA)-selected features, and six manually selected features. The performance as well as the training speed of those four ML models with three different kinds of features is assessed and compared. Based on the obtained results, it is possible to make a relatively accurate prediction of concrete compressive strength using SVR, XGBoost, and ANN with an R-square of over 0.9. When using different features, the highest R-square of the test set occurs in the XGBoost model with manually selected features as inputs (R-square = 0.9339). The prediction accuracy of the SVR model with manually selected features (R-square = 0.9080) or PCA-selected features (R-square = 0.9134) is better than the model with original features (R-square = 0.9003) without dramatic running time change, indicating that dimensionality reduction has a positive influence on SVR model. For XGBoost, the model with PCA-selected features shows poorer performance (R-square = 0.8787) than XGBoost model with original features or manually selected features. A possible reason for this is that the PCA-selected features are not as distinguishable as the manually selected features in this study. In addition, the running time of XGBoost model with PCA-selected features is longer than XGBoost model with original features or manually selected features. In other words, dimensionality reduction by PCA seems to have an adverse effect both on the performance and the running time of XGBoost model. Dimensionality reduction has an adverse effect on the performance of LR model and ANN model because the R-squares on test set of those two models with manually selected features or PCA-selected features are lower than models with original features. Although the running time of ANN is much longer than the other three ML models (less than 1s) in three scenarios, dimensionality reduction has an obviously positive influence on running time without losing much prediction accuracy for ANN model.

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Microstructure informed micromechanical modelling of hydrated cement paste: Techniques and challenges

Cited by 46 publications

References 122 publications

Discrete lattice fracture modelling of hydrated cement paste under uniaxial compression at micro-scale

Discrete lattice fracture modelling of hydrated cement paste under uniaxial compression at micro-scale

Effect of printing parameters on interlayer bond strength of 3D printed limestone-calcined clay-based cementitious materials: An experimental and numerical study

On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance

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