The predictions of the model are used as recommendations for onsite testing to accelerate finding mixtures with optimal strength-speed-sustainability trade-offs. We are not replacing canonical testing with the model.
Our work on concrete here differs in that the problem is both
1) an inherently time-varying, and
2) multi-objective.
See our write-up here for details: https://arxiv.org/pdf/2310.18288
We use Gaussian processes trained on vetted test data from academic and industry partners. We use these predictions to recommend mixes for onsite testing to accelerate finding mixtures with optimal strength-speed-sustainability trade-offs. None of the data and predictions go untested. The blog post goes into this in more detail.
Hi, I developed the model. We are not bypassing the regular testing process, and are not using LLMs, but Gaussian processes with vetted test data. The predictions are used as recommendations for onsite testing, to accelerate finding mixtures with optimal strength-speed-sustainability trade-offs.