Materials & Design (May 2023)

Predicting micromechanical properties of cement paste from backscattered electron (BSE) images by computer vision

  • Minfei Liang,
  • Shan He,
  • Yidong Gan,
  • Hongzhi Zhang,
  • Ze Chang,
  • Erik Schlangen,
  • Branko Šavija

Journal volume & issue
Vol. 229
p. 111905

Abstract

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This paper employs computer vision techniques to predict the micromechanical properties (i.e., elastic modulus and hardness) of cement paste based on an input of Backscattered Electron (BSE) images. A dataset comprising 40,000 nanoindentation tests and 40,000 BSE micrographs was built by express nanoindentation test and Scanning Electron Microscopy (SEM). A Residual Convolutional Neural Network (Res-Net) model, which differs from a typical Convolutional Neural Network (CNN) architecture by a shortcut connection, was employed and compared with a simple table model. The models were trained, tuned, and tested over a training, validation and testing set comprising 70%, 15% and 15% of the 40,000 data pairs, respectively. The following conclusions were drawn: 1) Express nanoindentation tests can provide reliable information for cement paste. Deconvolution based on Gaussian Mixture Model (GMM) can obtain almost invariant statistics for each phase; 2) Based on averaged greyscale values of each BSE image, a table model can predict the elastic modulus and hardness with R2 of 0.80 and 0.83, respectively; 3) Based on the intensity of each pixel as well as their patterns in each BSE image, the Res-Net model can predict the elastic modulus and hardness with a R2 of 0.85 and 0.88, respectively. Deconvolution of the Res-Net prediction obtains similar invariant statistics as derived by the nanoindentation tests, which gives strong evidence of the applicability of the Res-Net model.

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