| Principal Investigator: | Ing. Gago Filip, PhD. |
| Researchers: | Ing. Bulko Roman, PhD., Ing. Masarovičová Soňa, PhD., doc. Ing. Nguyen Giang, CSc., doc. Mgr. Sitányiová Dana, PhD., Ing. Vlček Jozef, PhD. |
| Project Duration: | 4/2021 - 3/2024 |
| Artificial intelligence and machine learning are dynamically developing fields. Machine learning (ML) allows computers to learn from existing data. Geotechnical survey generates a lot of data, and various ML methods can also be used in geotechnical engineering. The project aims to explore the possibilities of using ML for geotechnical engineering problems, where uncertainty in rock environment parameters is a common part of predictive models for foundation soil behavior. Rock environment parameters can be determined using laboratory and field tests or estimated using empirical or numerical correlations that are derived from regression analysis on a data set. During the project, we plan to use artificial neural networks (ANNs) for multivariate nonlinear modeling of rock environment and prediction of its parameters. ANN models will be developed and trained using local data sets. The performance of the models will be compared with the results of laboratory and field tests. | |
