Cable cranes are the main concrete transportation equipment used in arch dam construction. Productivity analysis of cable crane transportation is crucial for improving scheduling management, reducing operational costs, and controlling dam construction progress. However, the traditional manual recording method for analyzing cable crane productivity is time-consuming and labor-intensive. Moreover, existing monitoring methods, such as sensors and global navigation satellite systems, are susceptible to interference because of the challenging environment and complicated operating space at dam construction sites. Furthermore, they usually entail high installation and maintenance costs. Therefore, this study proposes an intelligent monitoring method based on visual tracking and pattern recognition for cable crane transportation in dam construction.
The proposed method initially tracks the process of cable crane transporting concrete using visual tracking technology to obtain the complete moving trajectory of crane buckets. Subsequently, it establishes a pattern recognition model to automatically identify the working states of cable cranes and calculate their productivity by analyzing the time-series features of the trajectory data. In the visual tracking of cable cranes, the main challenge is to address the similar appearance and occlusion problems of crane buckets. Therefore, we propose a new multiobject tracking framework by introducing a rematching mechanism based on tracklet features (segments of the entire object trajectory), which effectively reduces the occurrences of ID switches and enhances tracking accuracy. Additionally, You Only Look Once (YOLO) model is trained as the object detector of the tracking framework. Subsequently, trajectory data obtained by visual tracking is used as input for the pattern recognition model of cable crane working states, with the output being the pouring productivity. This pattern recognition model employs spline interpolation and Savitzky-Golay filters to solve the problems of missing values and noises in the trajectory data. A first-differential method is applied to statistically analyze the variation patterns of the trajectory data. This model can rapidly and accurately identify the working states and determine the key efficiency indicators of cable cranes.
A testing experiment was conducted at an arch dam construction site to evaluate the monitoring performance using this approach. Experimental results are summarized as follows: 1) The proposed vision-based multiobject tracking method proves effective in detecting and tracking cable buckets in intricate construction scenes, thus achieving effective and complete tracking of moving trajectories of crane buckets; moreover, identity F1 score (IDF1) and multiple object tracking accuracy (MOTA) metrics reach 94.8% and 90.0%, respectively. 2) The proposed pattern recognition model can rapidly and accurately distinguish six working states in the cable crane transportation process, including horizontal transport, descent, unloading, ascent, horizontal return, and waiting for loading. 3) Key productivity indicators, such as duration of a single transporting cycle, number of transporting cycles, duration of each working state, and concrete pouring intensity, are accurately calculated and meet engineering management requirements. This also confirms the practicability, reliability, and accuracy of the proposed monitoring method.
Thus, this study successfully integrates vision-based tracking and pattern recognition technologies to develop an intelligent monitoring method, consequently achieving automatic and accurate calculation of cable crane productivity. Furthermore, it demonstrates a positive application effect at dam construction sites and provides innovative perspectives and technical support for construction management.
