Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste

Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste

Liang, M. (TU Delft Materials and Environment)
Gan, Y. (TU Delft Materials and Environment)
Chang, Z. (TU Delft Materials and Environment)
Wan, Z. (TU Delft Materials and Environment)
Schlangen, E. (TU Delft Materials and Environment)
Šavija, B. (TU Delft Materials and Environment)

2022

This study aims to provide an efficient alternative for predicting creep modulus of cement paste based on Deep Convolutional Neural Network (DCNN). First, a microscale lattice model for short-term creep is adopted to build a database that contains 18,920 samples. Then, 3 DCNNs with different consecutive convolutional layers are built to learn from the database. Finally, the performance of DCNNs is tested on unseen testing samples. The results show that the DCNNs can achieve high accuracy in the testing set, with the R² all higher than 0.96. The distribution of creep modulus predicted by the DCNNs coincides with that of the original data. Furthermore, through analyzing the feature maps, it is found that the DCNNs can correctly capture the local importance of different microstructural phases. The DCNN allows therefore prediction of the creep modulus based on microstructural input, which saves computational resources of segmentation procedure and multiple incremental FEM calculations.

Cement paste
Convolutional neural network
Creep
Image processing
Prediction

http://resolver.tudelft.nl/uuid:a52c8c3a-4c61-4873-b484-8ff11a5a76b3

https://doi.org/10.1016/j.cemconres.2021.106681

0008-8846

Cement and Concrete Research, 152, 1-17

Institutional Repository

journal article

© 2022 M. Liang, Y. Gan, Z. Chang, Z. Wan, E. Schlangen, B. Šavija