Data-driven ultimate condition prediction of UHPC columns subjected lateral confinement using Artificial Neural Network (ANN) approach

Stein S.Y. Lee, Shack Yee Hiew, Sudharshan N. Raman

Research output: Contribution to journalConference articleResearchpeer-review

Abstract

Despite the superior mechanical properties of Ultra-High Performance Concrete (UHPC) in comparison to conventional concrete, UHPC is still not widely used in the construction industry due to the lack of design standards. This research mainly focuses on using advanced computational tools for the prediction of ultimate stress and its corresponding strain for confined UHPC columns, to facilitate the process of analysis and design, and to support further development of standards. Characteristic parameters of confined UHPC column samples were extracted from available literature as the input, and the corresponding ultimate stress and strain as the output. Artificial Neural Network (ANN) technique was adopted to process the input parameters in order to train and predict the output. In the regression analysis, the trained networks exhibited the coefficients of determination (R2) of 0.995 and 0.964 for the prediction of ultimate stress and corresponding strain respectively. The weights and biases within the best performance ANN from the k-fold cross validation can be extracted to perform mathematical solutions for the prediction in design.

Original languageEnglish
Article number012010
Number of pages10
JournalJournal of Physics: Conference Series
Volume2521
Issue number1
DOIs
Publication statusPublished - 2023
EventInternational Conference on Concrete Engineering and Technology, (CONCET 2022) - Virtual, Online
Duration: 6 Dec 20227 Dec 2022
Conference number: 15th
https://iopscience.iop.org/issue/1742-6596/2521/1

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