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Autonomous construction hoist system based on deep reinforcement learning in high-rise building construction

Authors
Lee, DongminKim, Minhoe
Issue Date
8월-2021
Publisher
ELSEVIER
Keywords
Adaptive hoist control; Autonomous hoist; Construction hoist; Deep Q-network (DQN); Deep reinforcement learning; Intelligent automation
Citation
AUTOMATION IN CONSTRUCTION, v.128
Indexed
SCIE
SCOPUS
Journal Title
AUTOMATION IN CONSTRUCTION
Volume
128
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137083
DOI
10.1016/j.autcon.2021.103737
ISSN
0926-5805
Abstract
Construction hoists at most building construction sites are manually controlled by human operators using their intuitions; as a result, unnecessary trips are often made when multiple hoists are operating simultaneously and/ or when complicated hoist calls are requested. These trips increase a passenger's waiting time and lifting time, reducing the lifting performance of the hoists. To address this issue, the authors develop an autonomous hoist supported by a deep Q-network (DQN), a deep reinforcement learning method. The results show that the DQN algorithm can provide better control policy in complicated real-world hoist control situations than previous control algorithms, reducing the waiting time and lifting time of passengers by up to 86.7%. Such an automated hoist control system helps shorten the project schedule by increasing the lifting performance of multiple hoists at high-rise building construction sites.
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