Compressive strength prediction of Portland cement clinker using machine learning by XRD data

Abstract

Accurately predicting the 28-day compressive strength of Portland cement clinkers is challenging due to their complex multiphase nature, especially when production processes and environmental conditions vary. To address this issue, the TransXRD model was introduced, which is a novel deep learning approach that combines the Transformer architecture with CNN design. 214 clinker samples were collected from 6 cement plants. The diffraction peak sequence of samples ranging from 10° to 55° was used as required input for the model. A combination of one-dimensional convolution is incorporated to extract local features, and self-attention mechanisms are leveraged to perform global feature integration. XRD data were directly utilized, bypassing additional processing to minimize signal loss. Compared to other DNN (deep neural network) models, the TransXRD model demonstrates promising performance, achieving a root mean square error (RMSE) of 1.15, a mean squared error (MSE) of 1.33, a mean absolute error (MAE) of 0.93, and a mean absolute percentage error (MAPE) of 1.76 on the test set. Notably, in the training set, 100% of the data exhibit prediction errors within 2 MPa, while within the test set, 90% of the prediction errors are within 2 MPa. The application potential of XRD data in predicting the compressive strength of cement clinker also offers novel insights into predicting the performance of other materials.

Keywords:

• Portland cement clinker
• TransXRD model
• strength prediction
• X-ray diffraction
• self-attention mechanism

Disclosure statement

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Additional information

Funding

This present work has been funded by the National Natural Science Foundation (No. 52341202) and the R&D Program of CNBM (No. 2021YCJS01).