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

Zhi Wan, Yading Xu (Corresponding author), Ze Chang, Minfei Liang, Branko Šavija

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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.

Originele taal-2Engels
Aantal pagina's14
TijdschriftConstruction and Building Materials
StatusGepubliceerd - 12 jan. 2024


Zhi Wan, Ze Chang and Minfei Liang would like to acknowledge the financial support of the China Scholarship Council (CSC) under the grant agreement No. 201906220205 , No. 201806060129 and No. 202007000027 . Yading Xu and Branko Šavija acknowledge the financial support of the European Research Council (ERC) within the framework of the ERC Starting Grant Project “Auxetic Cementitious Composites by 3D printing (ACC-3D)”, Grant Agreement Number 101041342 . Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

European Research Council101041342
China Scholarship Council201806060129, 201906220205, 202007000027


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