Title: Response Prediction of Ultra-High-Performance Concrete Beams using Machine Learning
Author(s): Roya Solhmirzaei, Hadi Salehi, and Venkatesh Kodur
Publication: Symposium Paper
Appears on pages(s): 113-122
Keywords: ultra-high-performance concrete (UHPC), machine learning, artificial intelligence, failure mode, data-driven framework
A computational framework employing machine learning (ML) is applied to predict failure mode of ultra-high-performance concrete (UHPC) beams. For this purpose, results from a number of tests on UHPC beams with different geometric and loading configurations and material characteristics are collected and utilized as an input to the ML framework. Results from numerical studies are not included in the data set due to the fact that they are highly dependent upon the adopted material models, meshing practices, as well as other assumptions used in modeling. Artificial neural network is used to predict the failure mode of the UHPC beams. Results indicate that the proposed ML framework is capable of predicting failure mod of UHPC beams with varying reinforcement and configurations, and can be considered for use in design applications. This paper aims to promote the applicability of ML for a practical engineering problem, detecting structural response of UHPC beams.