| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Frontiers of Materials Award Symposium: Machine Learning and Autonomous Researchers for Materials Discovery and Design
|
| Presentation Title |
Bayesian Methods for Concrete Creep Prediction and Learning Optimized Concrete Microstructure Design |
| Author(s) |
Mija Hubler |
| On-Site Speaker (Planned) |
Mija Hubler |
| Abstract Scope |
In past years machine learning has been used to update prediction models for the viscoelastic behavior of concrete. Short-term laboratory tests can only inform certain parameters in science and mechanics-based models of the time-dependent behavior of concrete. Once these models have been empirically calibrated through optimization, they provide a poor prediction. Only be introducing additional data in the form on long-term structural measurements or field testing through Bayesian methods could prediction models provide useful long-term estimates of concrete behavior. More recently, machine learning is being used to automate petrography to assess and diagnose the deterioration state of concrete from image data. The most recent advances in these efforts aim to develop microstructure descriptors of concrete which directly correlate to the strength, stiffness, and toughness of the material. Successfully establishing these descriptors will enable the design of printed concrete microstructures for desired properties. |
| Proceedings Inclusion? |
Undecided |