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

Abstract

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.

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

“…These features contribute to accelerated construction progress. Figure 1 illustrates the three-dimensional structural details of the HBM [ 8 , 9 , 10 ]. The levelness of the steel platform (SP) represents the HBM’s posture changes [ 11 , 12 , 13 ].…”

“…Zuo et al conducted a study on the determination of concrete strength during the construction of HBMs by proposing a remote real-time monitoring system [ 10 ]. Pan et al collected the vibration frequency of HBMs and utilized a machine learning method to categorize and assess the four primary working states of this machinery [ 9 ]. They also proposed a method for monitoring and issuing warnings during the climbing process of an HBM [ 8 ].…”

BS-YOLOV8: an intelligent detection model for bearing pin support-piece states of high-rise building machine

“…For instance, dynamic early warning monitoring of working platforms, prevention of mold frame dislodgement, and scrutiny of piece states of HBM bearing pin support (BPS). Among these methodologies, As the main supporting part of the lifting process, the BPS is the last line of defense for the stability of the HBM [4][5][6][7][8][9].…”