Print Email Facebook Twitter Automatic enhancement of vascular configuration for self-healing concrete through reinforcement learning approach Title Automatic enhancement of vascular configuration for self-healing concrete through reinforcement learning approach Author Wan, Z. (TU Delft Materials and Environment) Xu, Y. (TU Delft Materials and Environment) Chang, Z. (TU Delft Materials and Environment; Eindhoven University of Technology) Liang, M. (TU Delft Materials and Environment) Šavija, B. (TU Delft Materials and Environment) Date 2024 Abstract 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. Subject ConcreteNumerical simulationOptimizationReinforcement learningSelf-healing To reference this document use: http://resolver.tudelft.nl/uuid:34f89481-a1ba-46b5-bbf5-09956c0aa540 DOI https://doi.org/10.1016/j.conbuildmat.2023.134592 ISSN 0950-0618 Source Construction and Building Materials, 411 Part of collection Institutional Repository Document type journal article Rights © 2024 Z. Wan, Y. Xu, Z. Chang, M. Liang, B. Šavija Files PDF 1_s2.0_S0950061823043118_main.pdf 9.78 MB Close viewer /islandora/object/uuid:34f89481-a1ba-46b5-bbf5-09956c0aa540/datastream/OBJ/view