Amortized inference of Gaussian process hyperparameters for improved concrete strength trajectory prediction

Designing and utilizing alternative concrete formulations which supplant the use of ordinary portland cement with alternative binders have been identified as central goals in reducing the greenhouse gas impact of the concrete industry. Given the variability in availability and quality of alternatives, these goals call for an optimal design of experiment approach to designing formulations, which can be adapted to local needs. The realization of this goal hinges on an ability to predict key properties. Here, we present and benchmark a Gaussian process (GP) model for predicting the trajectory of concrete strength, an essential performance measure. GPs are a desirable model class for the application because of their ability to estimate uncertainty and update predictions given additional data. In this work, rather than manually tuning hyperparameters for different concrete mix models, we propose a new method based on amortized inference leveraging mixture attributes, leading to models which are better fit for use in Bayesian optimization of concrete formulation. We demonstrate the success of the approach using a large, industrial concrete dataset.