Autonomous construction hoist system based on deep reinforcement learning in high-rise building construction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Dongmin | - |
dc.contributor.author | Kim, Minhoe | - |
dc.date.accessioned | 2022-02-27T02:40:33Z | - |
dc.date.available | 2022-02-27T02:40:33Z | - |
dc.date.created | 2021-12-07 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 0926-5805 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137083 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | TRANSPORTATION | - |
dc.subject | SIMULATION | - |
dc.subject | TIME | - |
dc.subject | ALGORITHM | - |
dc.title | Autonomous construction hoist system based on deep reinforcement learning in high-rise building construction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Minhoe | - |
dc.identifier.doi | 10.1016/j.autcon.2021.103737 | - |
dc.identifier.scopusid | 2-s2.0-85107116174 | - |
dc.identifier.wosid | 000663562500004 | - |
dc.identifier.bibliographicCitation | AUTOMATION IN CONSTRUCTION, v.128 | - |
dc.relation.isPartOf | AUTOMATION IN CONSTRUCTION | - |
dc.citation.title | AUTOMATION IN CONSTRUCTION | - |
dc.citation.volume | 128 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Construction & Building Technology | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Construction & Building Technology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | SIMULATION | - |
dc.subject.keywordPlus | TIME | - |
dc.subject.keywordPlus | TRANSPORTATION | - |
dc.subject.keywordAuthor | Adaptive hoist control | - |
dc.subject.keywordAuthor | Autonomous hoist | - |
dc.subject.keywordAuthor | Construction hoist | - |
dc.subject.keywordAuthor | Deep Q-network (DQN) | - |
dc.subject.keywordAuthor | Deep reinforcement learning | - |
dc.subject.keywordAuthor | Intelligent automation | - |
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