The development of this platform started with a master's degree research of Gabriel Emidio Lage in 2023 at University of São Paulo, Brazil, under the supervision of professors Luís Bitencourt Jr. from University of São Paulo and Thomaz Buttignol from University of Campinas. The proposal was to create data-driven models based on Machine Learning algorithms capable of predicting post-cracking parameters usually obtained in EN 14651 experiments and shear strenght for SFRC beams. The shear strength model takes into account relevant features, such as fiber volume fraction, fiber aspect ratio, concrete compressive strength, longitudinal reinforcement ratio, cross-section dimensions, and more.

Despite of some researches suggest models to predict SFRC behavior, hardly ever those models have public access. In that way, this platform was an initiative to make prediction models available to others. Furthermore, all databases used during model training are also available for download, avoiding the task of gathering experimental data published by different authors from scratch. New models and databases will be added in the future as new researches are carried out. If you have any suggestions or want to contribute, providing results from new tests, please let us know through the contact form.

All information, content, and materials provided on this platform are for informational reference purposes only. The use of this platform and the data within it is the sole and exclusive responsibility of the user, and it does not accept responsibility for any damages that may occur due to improper use. Analyses of the results presented should be carried out by qualified professionals, such as engineers. The links to third-party websites contained on this website, if enabled, are for the user's convenience only.