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

TL;DR

This paper introduces a deep reinforcement learning approach to automate the 3D pipeline layout design process, significantly reducing design time while maintaining high-quality outcomes.

Contribution

It presents a novel DRL-based method for automatic pipeline layout generation, integrating geometric abstraction and reward functions for efficient design.

Findings

DRL models outperform traditional algorithms in speed.

The method ensures high-quality pipeline layouts.

Automation reduces manual effort in pipeline design.

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.