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作者:

Zhao Yuhong (Zhao Yuhong.) | Cao Cunfa (Cao Cunfa.) | Liu Zhansheng (Liu Zhansheng.) | Mu Enyi (Mu Enyi.)

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EI SCIE PubMed

摘要:

Prefabricated buildings are widely used because of their green environmental protection and high degree of industrialization. However, in construction process, there are some defects such as small wireless network coverage, high-energy consumption, inaccurate control, and backward blind hoisting methods in the hoisting process of prefabricated components (PC). Internet-of-Things (IoT) technology can be used to collect and transmit data to strengthen the management of construction sites. The purpose of this study was to establish an intelligent control method in the construction and hoisting process of PC by using IoT technology. Long Range Radio (LoRa) technology was used to conduct data terminal acquisition and wireless transmission in the construction site. The Inertial Measurement Unit (IMU), Global Positioning System (GPS), and other multi-sensor fusion was used to collect information during the hoisting process of PC, and multi-sensor information was fused by fusion location algorithm for location control. Finally, the feasibility of this method was verified by a project as a case. The results showed that the IoT technology can strengthen the management ability of PC in the hoisting process, and improve the visualization level of the hoisting process of PC. Analysis of the existing outdated PC hoisting management methods, LoRa, IMU, GPS and other sensors were used for data acquisition and transmission, the PC hoisting multi-level management and intelligent control.

关键词:

hoisting IMU IoT LoRa PC

作者机构:

  • [ 1 ] [Zhao Yuhong]College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Cao Cunfa]College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Liu Zhansheng]College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Mu Enyi]Department of Land Economy, University of Cambridge, Cambridge CB2 1TN, UK

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来源 :

Sensors

ISSN: 1424-8220

年份: 2021

期: 3

卷: 21

3 . 9 0 0

JCR@2022

ESI学科: CHEMISTRY;

ESI高被引阀值:7

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WoS核心集被引频次: 0

SCOPUS被引频次: 11

ESI高被引论文在榜: 0 展开所有

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