Application of Deep Learning in BIM-Based Water Conservancy Building Maintenance Decision Support System

This paper proposes a DRL-based water conservancy building maintenance decision support algorithm. The algorithm combines BIM (Building Information Model) data and intelligently optimizes the maintenance strategy of water conservancy buildings through a deep reinforcement learning model. Specifically, the Q-learning method in reinforcement learning is used to construct a dynamic maintenance environment, automatically learn and select the optimal maintenance strategy, thereby significantly improving decision-making efficiency and accuracy. In the experimental part, the BIM-based water conservancy building maintenance data set is simulated. The experimental results show that compared with the traditional rule-based method, the deep reinforcement learning algorithm improves the accuracy of maintenance decisions by 22.6% and the decision response speed by 30%. In terms of fault prediction and resource allocation, this algorithm also shows a significant advantage, with an accuracy improvement of 18.4%. The experiment verifies the effectiveness and superiority of the algorithm in dealing with complex decision-making environments, indicating that deep reinforcement learning combined with BIM technology can effectively improve the intelligent level of water conservancy building maintenance, and provide new ideas and technical support for future intelligent building management systems.