Holistic design of pre-tensioned concrete beams based on Artificial Intelligence. Issue 3 (4th May 2023)

• Record Type: Journal Article
• Authors: Hong, Won-Kee; Nguyen, Manh Cuong; Pham, Tien Dat; Le, Thuc Anh
• Is Part Of: Journal of Asian architecture and building engineering. Volume 22:Issue 3(2023)
• Page Start: 1714
• Page End: 1745
• Publication Date: 2023-05-04
• Subjects: Artificial intelligence -- pre-tensioned beams -- reverse designs -- design chart -- curvature ductility; Structural engineering -- East Asia -- Periodicals; Architectural design -- East Asia -- Periodicals; Architectural design; Structural engineering; Periodicals; Electronic journals; 720.95
• DOI: 10.1080/13467581.2022.2097909
• Languages: English
• ISSNs: 1346-7581

Abstract

ABSTRACT: This research demonstrates how pre-tensioned concrete beams (PT beams) are designed holistically using artificial neural networks (ANNs). To establish reverse design scenarios, large input and output data are generated using the mechanics-based software AutoPTbeam. ANN-trained reverse-forward networks are proposed to solve reverse designs with 15 input and 18 output parameters for engineers. ANNs for reverse designs pre-tensioned concrete beams are formulated based on 15 input structural parameters to investigate the performances of PT beams with pin-pin boundaries. Useful reverse designs based on neural networks can be established by relocating preferable control parameters on an input-side, such as when four output parameters ( q L / 250, q 0.2 m m, q s t r, μ Δ ) (reverse scenario) are reversely pre-assigned on an input-side, all associated design parameters, including crack width, rebar strains at transfer load stage, rebar strains, and displacement ductility ratio at ultimate load stage are computed on an output-side. Deep neural networks trained by chained training scheme with revised sequence (CRS) for the reverse network of Step 1 show the better design accuracies when compared to those obtained based on ANNs trained by parallel training method (PTM) and based on shallow neural networks trained by CRS when the deflection ductility ratios ( μΔ ) within generated big datasets are reversely pre-assigned on an input-side. GRAPHICAL ABSTRACT: uf0001