Automatic enhancement of vascular configuration for self-healing concrete through reinforcement learning approach

Zhi Wan, Yading Xu*, Ze Chang, Minfei Liang, Branko Šavija

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Vascular self-healing concrete (SHC) has great potential to mitigate the environmental impact of the construction industry by increasing the durability of structures. Designing concrete with high initial mechanical properties by searching a specific arrangement of vascular structure is of great importance. Herein, an automatic optimization method is proposed to arrange vascular configuration for minimizing the adverse influence of vascular system through a reinforcement learning (RL) approach. A case study is carried out to optimize a concrete beam with 3 pores (representing a vascular network) positioned in the beam midspan within a design space of 40 possibilities. The optimization is performed by the interaction between RL agent and Abaqus 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 (i.e., peak load or fracture energy) are accessed for all of the independent runs. The RL optimization method is capable of identifying the structure with high fracture energy in the new optimization task for 4-pore concrete structure.

Original languageEnglish
Article number134592
Number of pages14
JournalConstruction and Building Materials
Publication statusPublished - 2024


  • Concrete
  • Numerical simulation
  • Optimization
  • Reinforcement learning
  • Self-healing


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