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arXiv · 2305.10760

Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning

Chen Yang·Zhe Zheng·Jia-Rui Lin
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Abstract

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.

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Chen Yang, Zhe Zheng, Jia-Rui Lin. 2023-05-18. Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning. https://arxiv.org/abs/2305.10760

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