###### 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. high-rise building machine; construction site; state recognition; vibration signal analysis; real-time monitoring Article  
Automatic Identification of the Working State of High-Rise Building Building Machine Based on Machine Learning  
2020 September 202310 October 2023  
During the construction of a structurally standard vertical section of a concrete core, the HBMs typically experience four typical working states: climbing, operating, pumping, and idling. In each state, the functional state of the HBM components requires tailored monitoring and onsite management, each with distinct areas of emphasis. For instance, during the climbing state, it is essential to monitor unauthorized personnel on the HBM, potential interactions or collisions between the HBM and the core wall, and the posture of the HBM's primary load-bearing platform. In the operating state, the focus shifts to the HBM's load-bearing capacity and structural integrity. Meanwhile, the pumping state emphasizes monitoring potential collisions between the tower crane and the placing boom. Moreover, by analyzing the duration of the idling periods, the HBM's idle rate can be assessed, enabling the more effective management of the construction progress. Different safety monitoring measures must be adopted for the HBM components depending on their construction state. Automated recognition of the HBM's working state is a prerequisite for implementing the corresponding management and safety warning measures. Therefore, to enhance the intelligence and construction efficiency of super-high rise concrete structures and to ensure safety during the construction process, it is crucial to differentiate and accurately identify the working states of the HBM. Currently, a considerable amount of research has been carried out regarding the recognition and prediction of the status of workers, equipment, and materials at construction sites [8; 9], employing various methods and sensors to facilitate the process [10]. In addition, different machine learning (ML) methods such as Artificial Neural Network (ANN) [11], Support Vector Machine (SVM) [12], Decision Tree (DT) [13], k-Nearest Neighbors (kNN) [14], and Random Forest (RF) [15] have been widely utilized in construction site management. These methods have had a significant impact on improving the efficiency [16], safety [17; 18], and predictive capabilities [19]. For example, [PERSON] et al.