Application of Deep Learning in Intelligent Optimization and Design of Water Conservancy Projects Based on BIM

This paper proposes an intelligent optimization and design algorithm for water conservancy projects based on GAN and RL, aiming to improve the diversity and optimization efficiency of design schemes by combining BIM (Building Information Modeling) data. The algorithm uses generative adversarial networks to automatically generate multiple design schemes, and evaluates and optimizes the performance of each scheme through reinforcement learning to achieve the optimal design selection. A data set of an actual hydraulic engineering is used, and the comparison is made between the two methods. The results show that the algorithm improves the design quality by 15.3 %, the computational efficiency by 20.8 %, and shows higher optimization ability in structural stability and cost control. Through simulation, the generated design schemes not only increase the diversity by 18 % compared with the traditional method, but also show obvious advantages in multiobjective optimization. Further experiments verify the applicability of the algorithm in complex water conservancy projects, and prove the great potential of the intelligent design optimization system based on deep learning in improving engineering design accuracy, reducing costs and shortening design cycles.