Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning
This addresses the time-consuming and laborious task of pipeline layout design for engineers in the construction industry, representing an incremental improvement through automation.
The paper tackles the problem of manual pipeline layout design in construction by proposing a deep reinforcement learning method to automate 3D pipeline generation, demonstrating that it completes the task much faster than traditional algorithms while ensuring high-quality outcomes.
The layout design of pipelines is a critical task in the construction industry. Currently, pipeline layout is designed manually by engineers, which is time-consuming and laborious. Automating and streamlining this process can reduce the burden on engineers and save time. In this paper, we propose a method for generating three-dimensional layout of pipelines based on deep reinforcement learning (DRL). Firstly, we abstract the geometric features of space to establish a training environment and define reward functions based on three constraints: pipeline length, elbow, and installation distance. Next, we collect data through interactions between the agent and the environment and train the DRL model. Finally, we use the well-trained DRL model to automatically design a single pipeline. Our results demonstrate that DRL models can complete the pipeline layout task in space in a much shorter time than traditional algorithms while ensuring high-quality layout outcomes.