Optimized Interaction P-M diagram for Rectangular Reinforced Concrete Column based on Artificial Neural Networks

Abstract

This study proposes an artificial neural network for a design of reinforced concrete (RC) columns for structural engineers who are interested in performing reverse designs, exploring influences of structural parameters (e.g., $$\phi {P_n},{\ }\phi {M_n}$$, and $${{\rm{\varepsilon }}_{\rm{s}}}$$) or code requirements on structural performances. The proposed networks enable both forward and reverse designs for an RC column, which is challenging to be achieved using conventional designs. An AI-based surrogate model of RC columns with sufficient training accuracy can comprehensively replace conventional design software, exhibiting excellent productivity for both forward and reverse designs. In addition, useful reverse design models based on neural networks can be established by relocating preferable control parameters, including safety factor (SF = $${\ }\phi {M_n}/{M_u}$$) and an aspect ratio of column sections, into the input region. All associated design parameters, including $$b$$, $$h$$, and $${\rho _s}$$, are computed on an output side. Design charts, such as the P–M diagram, are constructed, demonstrating design moment strength equal to the factored moment demand by specifying SF = 1. The design scenarios can be extended as further as possible to meet the requirements of engineers.

Keywords

• artificial neural networks
• p-m diagrams
• ai-based surrogate model
• reverse design
• rc column