Economic span selection of bridge based on deep reinforcement learning

TL;DR

This paper applies deep reinforcement learning, specifically a Deep Q-network, to optimize the economic span of bridges, reducing costs through an intelligent decision-making process.

Contribution

It introduces a novel application of deep reinforcement learning for bridge span selection, including the development of a simulation environment and an effective learning agent.

Findings

The agent successfully learns the optimal span selection policy.

The method reduces engineering costs by optimizing bridge span.

The approach demonstrates the feasibility of AI in infrastructure design.

Abstract

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.