Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

D. Tsamatsoulis

Abstract
The dynamic approach of two well-known techniques has been used to predict a cement’s 28-day compressive strength: Multiple linear regression (MLR), and artificial neural networks (ANN). The modeling is based on Portland cement data and utilizes daily physical, chemical analyses, and early strength results at days 1 and 7. Two kinds of models have been built, containing the 1-day strength as an independent variable, or both 1- and 7-day strength. The models are dynamic because they are applied to a movable past period of TD days to calculate the parameters, and then used for a future periodof TF days. The comparison is based on the residual error of the testing period, and TD, TF have been optimized. Eight ANNs of different complexity have been developed, but some of them are suffering from over-fitting. A third model has also been created with the coupling of the initial two. The time parameters as well as the filtering and weighting coefficients of the coupled model have been optimized. The simple ANNs with one node in the hidden layer, sigmoid or hyperbolic functions and bias, show better performance. The combination of the coupled model with these two best ANN techniques provides an improved prediction of 28-day strength compared with the initial model containing the 1-day strength. The sensitivity also of TF parameter is lower providing certain benefit in daily industrial application. The implementation of these methods in cement process control can contribute to quality improvement by maintaining a low variance of typical strength.

This work is licensed under a Creative Commons Attribution 4.0 International License

Keywords
cement, strength, neural networks, prediction, model, error