Risk Factors Affecting Equipment Management in Construction Firms

Pham, Cuong Phu; Nguyen, Phong Thanh; Phan, Phuong Thanh; Nguyen, Quyen Le Hoang Thuy To; Le, Loan Phuc; Duong, My Tien Ha

doi:10.13106/jafeb.2020.vol7.no11.347

Cited by 4 publications

(2 citation statements)

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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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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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“…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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Predictive Maintenance for Construction Equipment using Artificial Intelligence and Machine Learning

P. Rohith,

Mr. P A. Prabakaran,

Mr. A. Aswin Bharath

et al. 2023

IJARSCT

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Efficient maintenance of construction equipment plays an essential role in ensuring optimal functionality, reducing downtime, and enhancing safety on job sites. Playing Artificial Intelligence (AI) and Machine Learning (ML) techniques in construction equipment maintenance introduces a standard shift in traditional maintenance strategies. This paper explores the application of AI and ML algorithms in predictive maintenance, fault detection, and condition monitoring of heavy machinery used in construction. By harnessing data-driven insights, these technologies enable real-time monitoring of equipment health, facilitating timely identification of potential failures or performance degradation. Furthermore, the integration of AI-driven maintenance systems optimizes equipment utilization, minimizes unplanned downtime, and enhances overall operational efficiency. This research underscores the significance of AI and ML methodologies in developing construction equipment maintenance, paving the way for cost-effective, proactive, and predictive maintenance strategies in the construction industry

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Risk Factors Affecting Equipment Management in Construction Firms

Cited by 4 publications

References 0 publications

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

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

Predictive Maintenance for Construction Equipment using Artificial Intelligence and Machine Learning

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