Formwork System Selection Criteria for Building Construction Projects: A Structural Equation Modelling Approach

Terzioglu, Taylan; Polat, Gül; Turkoglu, Harun

doi:10.3390/buildings12020204

Cited by 15 publications

(8 citation statements)

References 97 publications

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“…In [55], the focus is put on the criteria for selecting a certain formwork system. Following that, a structural equation is developed and presented in [56]. The analysis reveals the quantitative interrelationships among criteria for formwork system selection.…”

Section: Datamentioning

confidence: 99%

Construction Work Efficiency Analysis—Application of Probabilistic Approach and Machine Learning for Formworks Assembly

Juszczyk

2023

Applied Sciences

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Analyses of efficiency are vital for planning and monitoring the duration and costs of construction works, as well as the entire construction project. This paper introduces a combined quantitative (probabilistic) and qualitative (machine learning-based) approach to the problem. The proposed approach covers probabilistic analysis based on fitting a triangular distribution to empirical data, followed by the application of support vector machines (SVM). Following the theoretical assumptions, the paper also presents an application of the proposed approach for formwork assembly as an exemplary construction work. This is based on real-life data, including conditions, characteristics, and features of formwork assembly work recorded on a construction site. As a result of the study, triangular distributions were fitted to data representing efficiencies of formwork assembly for three different types of structural members made of reinforced concrete. The parameters (a—minimum, m—peak and b—maximum values of efficiency measured as square meters of an assembled formwork per hour) of the fitted distributions for the particular real-life data were as follows: for columns a = 0.100, m = 1.450, b = 1.900, for walls a = 0.700, m = 1.995, b = 3.300 and for slabs a = 0.200, m = 2.125, b = 3.200. The obtained distributions allow us to assess the probability of achieving efficiency not less than a certain assumed critical value. The study also developed two SVM models—the first based on so-called C-classification and the second based on ν-classification—capable of recognising with satisfactory accuracy whether the efficiency of formworks assembly works for certain conditions, characteristics, and features of works are above or below median values computed based on previously fitted distributions. The performance of both developed models in terms of proper classification, either for training or testing, was above 80%.

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Section: Datamentioning

confidence: 99%

Construction Work Efficiency Analysis—Application of Probabilistic Approach and Machine Learning for Formworks Assembly

Juszczyk

2023

Applied Sciences

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“…Risks are mainly concentrated in the climbing and operating stages. Most existing studies have focused on the selection of a formwork system [27][28][29], performance analysis [30][31][32], and operational assessment [33,34]. However, research on the dynamic analysis and control of the climbing stage of the HBM and its early warning indices is lacking.…”

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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“…However, concerning HBM research, most existing studies have primarily focused on formwork system selection [31,32], performance analysis [33][34][35], the monitoring of key points or special working conditions [36,37], and operational assessment [38,39]. The absence of the automated recognition of HBM workflows hinders the ability to adjust monitoring strategies in response to dynamic changes in work conditions.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of the Working State of High-Rise Building Machine Based on Machine Learning

Pan,

Zhao,

et al. 2023

Applied Sciences

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High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM’s working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures.

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Formwork System Selection Criteria for Building Construction Projects: A Structural Equation Modelling Approach

Cited by 15 publications

References 97 publications

Construction Work Efficiency Analysis—Application of Probabilistic Approach and Machine Learning for Formworks Assembly

Construction Work Efficiency Analysis—Application of Probabilistic Approach and Machine Learning for Formworks Assembly

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

Automatic Identification of the Working State of High-Rise Building Machine Based on Machine Learning

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