Reinforcement learning to optimize vascular structure of self-healing concrete

Vascular self-healing concrete (SHC) has great potential to mitigate the environmental impact of the construction industry by increasing the durability of structures. With the help of reinforcement learning (RL), we managed to optimize the vascular structure configuration of self-healing concrete. The optimization is performed by the interaction between RL agent and FEM simulation environment with the change of target properties as a reward signal. The results illustrates that the RL approach is able to automatically enhance the vascular arrangement of SHC given the fact that the 3-pore structures that have the maximum target mechanical property.Read the paper for more details.