Activity recognition of construction equipment using fractional random forest

The monitoring and tracking of construction equipment, e.g., excavators, is of great interest to improve the productivity, safety, and sustainability of construction projects. In recent years, digital technologies are leveraged to develop monitoring systems for construction equipment. These systems are commonly used to detect and/or track different pieces of equipment. However, the recent research work has indicated that the performance of the equipment monitoring system improves when they are able to also recognize/track the activities of the equipment (e.g., digging, compacting, etc.). Nevertheless, the current direction of research on equipment activity recognition is gravitating towards the use of deep learning methods. While very promising, the performance of deep learning methods is predicated on the comprehensiveness of the dataset used for training the model. Given the wide variations of construction equipment, in size and shape, the development of a comprehensive dataset can be challenging. This research hypothesizes that through the use of a robust feature augmentation method, shallow models, such as Random Forest, can yield a comparable performance without requiring a large and comprehensive dataset. Therefore, this research proposes a novel machine learning method based on the integration of Random Forest classifier with the fractional calculus-based feature augmentation technique to develop an accurate activity recognition model using a limited dataset. This method is implemented and applied to three case studies. In the first case study, the operations of two different models of excavators (one small-size and one medium-size) were tracked. By using the data from one excavator for the training and the data from the other one for testing, the impact of equipment size and operators' skill level on the performance of the proposed method is investigated. In the second case study, the data from an actual excavator was used to predict the activity of a scaled remotely controlled excavator. In the last case study, the proposed method was applied for rollers (as an example of non-articulating equipment). It is shown that the fractional feature augmentation method can have a positive impact on the performance of all machine learning methods studied in this research (i.e., Neural Network and Support Vector Machine). It is also shown that the proposed Fractional Random Forest method is able to provide comparable results to deep learning methods using considerably smaller training dataset.