System Hazard Analysis of Tower Crane in Different Phases on Construction Site

Jiang, Ling; Zhao, Tingsheng; Zhang, Wei; Hu, Junjie

doi:10.1155/2021/7026789

Cited by 14 publications

(8 citation statements)

References 41 publications

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“…Shin (2015) analyzed the installation and dismantling of tower crane and emphasized that the safety management of these phases are also worthy of attention. Jiang et al. (2021)Jiang et al , 2021 analyzed compared the tower crane hazards of different phases.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Jiang et al. (2021)Jiang et al , 2021 analyzed compared the tower crane hazards of different phases. They found that hazards are transferable between phases.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 3 shows the statistical data of the accident phases and types. Table 1 lists the 35 unsafe factors that may potentially cause tower crane accidents, as obtained from accident reports and previous research (Jiang et al , 2021). These factors only include those related to the construction site; the management factors at the government and enterprise levels are excluded, as they are difficult to obtain from accident reports and construction sites.…”

Section: Tower Crane Accident Datamentioning

confidence: 99%

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Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data

Jiang

Zhao

Feng

et al. 2021

ECAM

Self Cite

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PurposeThis research is aimed at predicting tower crane accident phases with incomplete data.Design/methodology/approachThe tower crane accidents are collected for prediction model training. Random forest (RF) is used to conduct prediction. When there are missing values in the new inputs, they should be filled in advance. Nevertheless, it is difficult to collect complete data on construction site. Thus, the authors use multiple imputation (MI) method to improve RF. Finally the prediction model is applied to a case study.FindingsThe results show that multiple imputation RF (MIRF) can effectively predict tower crane accident when the data are incomplete. This research provides the importance rank of tower crane safety factors. The critical factors should be focused on site, because the missing data affect the prediction results seriously. Also the value of critical factors influences the safety of tower crane.Practical implicationThis research promotes the application of machine learning methods for accident prediction in actual projects. According to the onsite data, the authors can predict the accident phase of tower crane. The results can be used for tower crane accident prevention.Originality/valuePrevious studies have seldom predicted tower crane accidents, especially the phase of accident. This research uses tower crane data collected on site to predict the phase of the tower crane accident. The incomplete data collection is considered in this research according to the actual situation.

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Section: Literature Reviewmentioning

confidence: 99%

“…Jiang et al. (2021)Jiang et al , 2021 analyzed compared the tower crane hazards of different phases. They found that hazards are transferable between phases.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Tower Crane Accident Datamentioning

confidence: 99%

See 1 more Smart Citation

Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data

Jiang

Zhao

Feng

et al. 2021

ECAM

Self Cite

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“…Although this approach improved efficiency and reduced measurement errors, it incurred significant costs. The detection equipment enhanced production efficiency, minimized operational errors, ensured detection repeatability, and generated extensive monitoring data [ 17 , 18 , 19 , 20 ]. However, these measures still failed to prevent levelness deviations during the climbing process, addressing the complexity of repairing such deviations and mitigating subjective errors in worker operations.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models

Pan,

Huang,

Zhang

et al. 2024

Sensors

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High-rise building machines (HBMs) play a critical role in the successful construction of super-high skyscrapers, providing essential support and ensuring safety. The HBM’s climbing system relies on a jacking mechanism consisting of several independent jacking cylinders. A reliable control system is imperative to maintain the smooth posture of the construction steel platform (SP) under the action of the jacking mechanism. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN) are three multivariate time series (MTS) neural network models that are used in this study to predict the posture of HBMs. The models take pressure and stroke measurements from the jacking cylinders as inputs, and their outputs determine the levelness of the SP and the posture of the HBM at various climbing stages. The development and training of these neural networks are based on historical on-site data, with the predictions subjected to thorough comparative analysis. The proposed LSTM and GRU prediction models have similar performances in the prediction process of HBM posture, with medians R2 of 0.903 and 0.871, respectively. However, the median MAE of the GRU prediction model is more petite at 0.4, which exhibits stronger robustness. Additionally, sensitivity analysis showed that the change in the levelness of the position of the SP portion of the HBM exhibited high sensitivity to the stroke and pressure of the jacking cylinder, which clarified the position of the cylinder for adjusting the posture of the HBM. The results show that the MTS neural network-based prediction model can change the HBM posture and improve work stability by adjusting the jacking cylinder pressure value of the HBM.

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“…Several studies have been conducted to examine construction safety risks and monitor equipment and facilities at construction sites [7][8][9][10][11]. Peng et al [12] concluded that in their entire life cycle, buildings are prone to risks during the construction phase, particularly the concrete pouring phase of the reinforced concrete structures.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Monitoring and Early Warning of the High-Rise Building Machine during the Climbing Stage

Pan,

Zuo,

Zhang

et al. 2023

Advances in Civil Engineering

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High-rise building machines (HBMs) are commonly used for the construction of super-high skyscrapers. Monitoring and early warning are critical to ensure the safety of giant HBMs during dynamic climbing. However, no indicators or control methods directly reflect the dynamic safety state of such large structures. In this study, the key factors influencing the climbing altitude were systematically analyzed and a method for the dynamic monitoring and early warning of HBMs during climbing was proposed. This approach is innovative for monitoring 3D attitude in real time using a string of fiber grating-level sensors mounted on the main bearing surface of the HBM. Three-level early warning indicators and control methods that reflect the safety status of the HBM during dynamic climbing have been established. The method was successfully applied to a 356-m tall high-rise project, the Shenzhen Xinghe Yabao Building. Results demonstrate that the proposed method can monitor and control the safety state of the HBM climbing process more accurately than current methods in real time. In addition, it significantly reduces the impact of factors such as preclimbing differential deformation and climbing attitude recognition on the accuracy of the HBM climbing control. This guarantees the safe management of HBMs and the efficient construction of super high-rise buildings. The results of this study can also be widely applied to safety monitoring of the dynamic operation of giant construction machinery.

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System Hazard Analysis of Tower Crane in Different Phases on Construction Site

Cited by 14 publications

References 41 publications

Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data

Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data

Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models

Research on Dynamic Monitoring and Early Warning of the High-Rise Building Machine during the Climbing Stage

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