  Deep Q-network algorithm is used to select economic span of bridge. Selection
of bridge span has a significant impact on the total cost of bridge, and a
reasonable selection of span can reduce engineering cost. Economic span of
bridge is theoretically analyzed, and the theoretical solution formula of
economic span is deduced. Construction process of bridge simulation environment
is described in detail, including observation space, action space and reward
function of the environment. Agent is constructed, convolutional neural network
is used to approximate Q function,{\epsilon} greedy policy is used for action
selection, and experience replay is used for training. The test verifies that
the agent can successfully learn optimal policy and realize economic span
selection of bridge. This study provides a potential decision-making tool for
bridge design.
