Jia-Rui Lin

Jia-Rui Lin

Associate Professor

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

30th International Conference on Intelligent Computing in Engineering (EG-ICE 2023), 2023

Recommended citation: Yang, C., Zheng, Z., Lin, J.R.* (2023). Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning. 30th International Conference on Intelligent Computing in Engineering (EG-ICE 2023), 42-51. London, UK. https://doi.org/10.48550/arXiv.2305.10760 cited by count

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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