Machine learning-based construction site dynamic risk models

Gondia, Ahmed; Moussa, Ahmed; Ezzeldin, Mohamed; El‐Dakhakhni, Wael

doi:10.1016/j.techfore.2023.122347

Cited by 9 publications

(1 citation statement)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Hosoda et al [20] enhanced an ANN model for predicting the maximum thermal crack width in reinforced concrete abutment walls, demonstrating the potential of ML in improving the reliability and safety of concrete construction systems. Awe et al [21] and Gondia et al [22,23] used ML to assess the factors affecting building construction collapses and to predict the severity of building construction injuries. Bugalia et al [24], Cavalcanti et al [25], and Hayat and Morgado-Dias [26] utilized ML for the automated classification of safety reports, accident prevention, and safety helmet detection, respectively.…”

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

View full text Add to dashboard Cite

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.

show abstract