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• W4311571047 endingPage "115447" @default.
• W4311571047 startingPage "115447" @default.
• W4311571047 abstract "Recognising that ultra-high-performance concrete (UHPC) is gaining momentum in structural applications, providing an accurate confinement model is essential to developing a reliable design of UHPC structural members. However, very limited number of models are currently available and these models were empirically formulated and calibrated upon limited test data obtained by the originators of the models. The significant cost associated with comprehensive experimental testing motivates the exploration of cheaper and more efficient data-driven based machine learning approach. This study proposes a sequential artificial neural network (ANN) framework to develop such a data-driven confinement model, incorporating a comprehensive database of 228 axially loaded UHPC columns compiled from available literature. Three deep feed-forward neural network models were established to predict the ultimate stress, ultimate strain and stress–strain behaviour of confined UHPC. The results show that the proposed ANN-based ultimate condition models provide a more robust prediction results compared to the existing design-oriented models for confined UHPC. The stress–strain behaviour, predicted using the proposed ANN model, shows high accuracy levels in capturing different types of stress–strain curves as well as reasonably matching results with those experimentally measured responses. The encouraging outcomes in this study suggest that the proposed models are capable of providing rapid prediction tools that will help to facilitate the on-demand design of UHPC structural components and systems." @default.
• W4311571047 created "2022-12-27" @default.
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• W4311571047 date "2023-02-01" @default.
• W4311571047 modified "2023-10-17" @default.
• W4311571047 title "Prediction of ultimate conditions and stress–strain behaviour of steel-confined ultra-high-performance concrete using sequential deep feed-forward neural network modelling strategy" @default.
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• W4311571047 doi "https://doi.org/10.1016/j.engstruct.2022.115447" @default.
• W4311571047 hasPublicationYear "2023" @default.
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