Deep reinforcement learning for automated design of reinforced concrete structures

Abstract This study proposes a novel concept of reinforcement learning (RL) framework to facilitate automated structural design, with a particular focus on reinforced concrete (RC) beam design as case studies. We trained an RL agent called deep deterministic policy gradient (DDPG) with a convolutional neural network function approximators. The RL agent was successfully trained to design RC beams subject to American Concrete Institute (318) provisions without any hand‐labeled dataset. A python‐based RC beam design environment was developed and used to simulate RC beam designs with customized reward functions that encouraged the agent to minimize material cost by maximizing reward. The DDPG agent self‐learned cost‐effective RC beam design through 100,000 randomly generated design cases during the training procedure. The trained agent was able to design an RC beam in a cost‐effective way while taking both flexural and shear reinforcement arrangements into consideration. The trained agent generated near‐optimal design parameters without the need for unnecessary iterations over various design conditions. The performance of the agent was validated with 100 design cases and comparative studies, showing a great promise for automated RC beam design.