Intelligent Control Method of Hoisting Prefabricated Components Based on Internet-of-Things

Zhao, Yuhong; Cao, Cunfa; Liu, Zhansheng; Mu, Enyi

doi:10.3390/s21030980

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…As a mainstream tool for monitoring object motion, the IMU can measure motion without any spatial constraints [18]. Even though [19] combined Internet of Things (IoT) technology and IMU to optimize the real-time prefabricated component lifting activity recognition, the study ignored the lifting activity collision situation. Based on three situations of collision activities, the IMU and KNN and RF algorithms were used for establishing the hoisting activity recognition model in this study, which significantly improved the real-time detection with high accuracy.…”

Section: Discussionmentioning

confidence: 99%

Fabricated Components Hoisting Activity Recognition and Collision Analysis Based on Inertial Measurement Unit IMU

Chen

Kassem

et al. 2022

Buildings

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The prefabricated components are an important aspect of prefabricated constructions. Based on three possible collision situations when the fabricated component is being lifted during the installation phase, the aim of this study is to investigate how to prevent effective collision between the hoist and the barrier, and establish the standardized operation process (SOP) of using the Inertial Measurement Unit (IMU) to detect the hoisting collision of fabricated building components. The (IMU) technology is used in the collection of lifting activities data. The hoisting activity will be divided into four situations: classification and recognition of stationary, ascending, advancing, and descending of components; use of the K-Nearest Neighbor (KNN) algorithm and the Random Forest (RF) algorithm for data processing to recognize the hoisting activities of assembled components; construction of the hoisting activity recognition model; determination of the best recognition position of the IMU; further collision analysis based on the recognition of the hoisting of assembled components. The collision is divided into direct collision, sudden stop, and detour in a specific space from obstacles. Image analysis of the three types of collision activities will be carried out to help perceive lifting activities in advance and reduce loss resulting from collisions caused by components. According to the systematic research and discussion of hoisting activity recognition and collision behavior, it provides a reasonable basis and ideas for solving hoisting collisions in prefabricated buildings and aids in the use of inertial sensors in construction to provide assistance for construction automation.

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Section: Discussionmentioning

confidence: 99%

Fabricated Components Hoisting Activity Recognition and Collision Analysis Based on Inertial Measurement Unit IMU

Chen

Kassem

et al. 2022

Buildings

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“…"Machine learning" is gradually replacing traditional approaches [57][58][59]. "Wearable sensors" is increasingly apparent in addressing human risks [60][61][62], which can monitor the factors that may impact operation [63,64]. "Manual material handling" poses various risks, and it is vital to include it in a hoisting risk assessment.…”

Section: Topic Trendsmentioning

confidence: 99%

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Junjia,

Alias,

Haron

et al. 2023

Buildings

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Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on “ergonomics”, “machine learning”, “computer simulation”, and “wearable sensors”. Secondly, the previous 80 factors contributing to hoisting risks were summarized from a “human–equipment–management–material–environment” perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT’s interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research.

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“…[27] studied the correlation of risk factors in the lifting process using digital twin technology, which improved the efficiency of parallel accident handling. Zhao (2021) [28] managed the prefabricated component (PC) hoisting process through IoT sensing technology and realized an accident warning function after constantly learning the occurrence of accidents. Arashpour (2015) [29] performed multi-objective optimization of processes to avoid crossover between different processes, thus preventing safety accidents in the case of multiple tower cranes.…”

Section: Safety Management In Hoisting Processmentioning

confidence: 99%

Study of Occupational Safety Risks in Prefabricated Building Hoisting Construction Based on HFACS-PH and SEM

Song

Wang

Liu³

2022

IJERPH

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As the concern for environmental pollution and occupational safety caused by the construction industry is gradually increasing worldwide, the prefabricated building model has become a type of construction promoted by sustainable societies. In China, the management codes of prefabricated buildings are not mature enough and safety accidents occur frequently during the construction process. Therefore, how to analyze and determine the main factors that affect the safety of the construction of prefabricated buildings has become a problem to protect the lives and health of construction workers. In this study, we focused our research on the accident-prone component-hoisting construction phase. First, through the questionnaire and accident data, the traditional human factors analysis and classification system (HFACS) was improved into the HFACS–prefabricated building hoisting (PH) risk model. This study also established a comprehensive safety prevention and control system for the component-hoisting process of prefabricated buildings by combining the factor analysis of using structural equation modeling (SEM). The prevention and control measures to avoid the occurrence of prefabricated building component-hoisting accidents were also proposed from four aspects: external environment, organizational factors, prerequisites for triggering accidents, and unsafe leadership behaviors. The results showed the following: (1) For the external environment, occupational safety and health system standards should be established and safety supervision responsibilities should be implemented. (2) For organizational factors, safety management systems should be improved with more capital investment. (3) For unsafe leadership behaviors, safety education and training should be strengthened to ensure workers’ optimal physical and psychological states. (4) For the prerequisite of accidents, it is necessary to create a good hoisting work environment.

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Intelligent Control Method of Hoisting Prefabricated Components Based on Internet-of-Things

Cited by 11 publications

References 35 publications

Fabricated Components Hoisting Activity Recognition and Collision Analysis Based on Inertial Measurement Unit IMU

Fabricated Components Hoisting Activity Recognition and Collision Analysis Based on Inertial Measurement Unit IMU

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Study of Occupational Safety Risks in Prefabricated Building Hoisting Construction Based on HFACS-PH and SEM

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