Abstract 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.

中文翻译：

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使用分数随机森林的建筑设备活动识别

摘要 挖掘机等施工设备的监控和跟踪对于提高施工项目的生产力、安全性和可持续性具有重要意义。近年来，利用数字技术开发建筑设备监控系统。这些系统通常用于检测和/或跟踪不同的设备。然而，最近的研究工作表明，当设备监控系统也能够识别/跟踪设备的活动（例如，挖掘、压实等）时，它们的性能会提高。然而，当前设备活动识别的研究方向倾向于使用深度学习方法。虽然很有前途，深度学习方法的性能取决于用于训练模型的数据集的全面性。鉴于建筑设备在尺寸和形状方面的广泛差异，开发综合数据集可能具有挑战性。这项研究假设，通过使用强大的特征增强方法，随机森林等浅层模型可以产生相当的性能，而无需庞大而全面的数据集。因此，本研究提出了一种基于随机森林分类器与基于分数阶微积分的特征增强技术集成的新型机器学习方法，以使用有限的数据集开发准确的活动识别模型。该方法被实施并应用于三个案例研究。在第一个案例研究中，跟踪了两种不同型号挖掘机（一种小型和一种中型）的操作。通过使用一台挖掘机的数据进行训练，另一台挖掘机的数据进行测试，研究了设备尺寸和操作员技能水平对所提出方法性能的影响。在第二个案例研究中，来自实际挖掘机的数据用于预测缩放遥控挖掘机的活动。在最后一个案例研究中，建议的方法应用于滚子（作为非铰接设备的示例）。结果表明，分数特征增强方法可以对本研究中研究的所有机器学习方法（即神经网络和支持向量机）的性能产生积极影响。