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

Autor: Minfei Liang, Shan He, Yidong Gan, Hongzhi Zhang, Ze Chang, Erik Schlangen, Branko Šavija
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Materials & Design, Vol 229, Iss , Pp 111905- (2023)
Druh dokumentu: article
ISSN: 0264-1275
DOI: 10.1016/j.matdes.2023.111905
Popis: 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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