An optimization of round reinforced concrete columns subject to multiple loads using an artificial neural network (ANN)

ABSTRACT

Design optimizations of round reinforced concrete columns based on artificial neural networks (ANNs) have been investigated in previous studies with only one pair of axial load ($P_u$) and bending moment ($M_u$). In this study, ANNs are generalized to be applicable to multiple load pairs by reshaping weight matrices of ANNs to prevent retraining of ANNs on the large datasets. Generalized ANN-based Lagrange optimizations are proposed for structural designs of round reinforced concrete columns with multiple load combinations. The present study modularizes the weight matrix of ANNs which considers one load pair to completely capture multiple factored loads. An optimal design by ANNs based on the modularized weight matrix and Lagrange optimization techniques using the Karush–Kuhn–Tucker (KKT) conditions was performed and validated with large datasets. Design examples performed by an ANN-based method and structural mechanics demonstrate accuracies of safety factors (SF) as small as 1% − 2%, which confirms the applicability of the proposed ANNs. Based on the present study, ANNs with modularized weight matrices aid engineers in optimizing round reinforced concrete columns subject to multiple loads.

KEYWORDS:

• Artificial neural networks
• modularized weight matrices
• reinforced concrete columns
• multiple load combinations
• Lagrange with Karush-Kuhn-Tucker conditions

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT 2019R1A2C2004965).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Research Foundation of Korea (NRF) [MSIT 2019R1A2C2004965].

Notes on contributors

Won-Kee Hong

Dr. Won-Kee Hong is a Professor of Architectural Engineering at Kyung Hee University. Dr. Hong received his Master’s and Ph.D. degrees from UCLA, and he worked for Englelkirk and Hart, Inc. (USA), Nihhon Sekkei (Japan) and Samsung Engineering and Construction Company (Korea) before joining Kyung Hee University (Korea). He also has professional engineering licenses from both Korea and the USA. Dr. Hong has more than 30 years of professional experience in structural engineering. His research interests include a new approach to construction technologies based on value engineering with hybrid composite structures. He has provided many useful solutions to issues in current structural design and construction technologies as a result of his research that combines structural engineering with construction technologies. He is the author of numerous papers and patents both in Korea and the USA. Currently, Dr. Hong is developing new connections that can be used with various types of frames including hybrid steel–concrete precast composite frames, precast frames and steel frames. These connections would help enable the modular construction of heavy plant structures and buildings. He recently published a book titled as ”Hybrid Composite Precast Systems: Numerical Investigation to Construction” (Elsevier).

Thuc Anh Le

Thuc Anh Le is currently enrolled as a master candidate in the Department of Architectural Engineering at Kyung Hee University, Republic of Korea. Her research interest includes AI and precast structures.

Manh Cuong Nguyen

Manh Cuong Nguyen is currently enrolled as a master candidate in the Department of Architectural Engineering at Kyung Hee University, Republic of Korea. His research interest includes AI and precast structures.