This disclosure relates to an
intelligent design method,
system, and medium for outsourced UHPC eccentrically compressed columns, belonging to the field of
structural engineering technology. The method includes constructing a deep
reinforcement learning network model; obtaining training samples and inputting them into the model; calculating the
bearing capacity, cost, and compliance with
engineering specifications using combinations of predicted design parameters obtained from the training samples; calculating a reward
signal for training using the reward function and updating the network parameters; obtaining the load parameters of the outsourced UHPC eccentrically compressed column to be designed, and obtaining multiple sets of candidate design parameter combinations using the trained deep
reinforcement learning network model; calculating the
bearing capacity and checking against
engineering specifications for the candidate design parameter combinations; and selecting the final design scheme based on the reward
signal, the
bearing capacity calculation results, and the
engineering specification check results. This disclosure realizes
intelligent design of outsourced UHPC eccentrically compressed columns, enabling rapid and efficient acquisition of the safe and economical
optimal design.