Wang, QianqingMaximiano Dos Santos, Ketson RobertoBeyer, Katrin2023-03-072023-03-072022-09-05https://infoscience.epfl.ch/handle/20.500.14299/195458The construction of dry-joint brick masonry that is subject to seismic loading can be modelled as a strategy problem wherein bricks must be laid to form a load resisting structure. To learn how to mount bricks while considering relevant engineering criteria, this paper trains an artificial intelligence using a reinforcement learning approach. Here, we created a simulation environment wherein an agent assembles bricks to receive rewards based on the measured force capacity of the dry-joint brick masonry and the geometry of the constructed wall in comparison with the target volume of the wall. The horizontal force capacity was assessed using kinematic analysis based on a rigid block modelling. This work shows that agents can learn construction strategies by finding optimum placements for the larger bricks to improve the earthquake resistance of masonry structures.reinforcement learningdry joint brick masonry wallkinematics analysisoptimizationtext::conference output::conference paper not in proceedings