Abstract Although heavy equipment is an indispensable resource in many construction projects, it is often underutilized. Inefficient usage patterns and frequent idling contribute to increased emissions and project costs. Efforts to improve usage patterns often begin with activity tracking. Recent research into automated activity tracking has leveraged sensing devices and Internet-of-Things (IoT) frameworks to power machine learning models that can predict the behaviors of monitored equipment. However, shallow machine learning models require complex manual feature engineering that could be further automated with more recent deep learning approaches. Deep learning approaches not only increase automation but also promise improved accuracies by avoiding biases introduced by manual feature design. This paper proposes a construction equipment activity recognition framework that uses deep learning architectures to predict the activities of heavy construction equipment monitored via accelerometers and applies this framework to a roller compactor and an excavator performing real work. The performance of a simple baseline convolutional neural network (CNN) is compared to a hybrid network that contains both convolutional and recurrent long short-term memory (LSTM) layers. The hybrid model outperforms the baseline model in all instances studied. In the task of classifying the activities of the roller compactor, the hybrid model achieves a validation accuracy of 77.1% when presented with six activities and a validation accuracy of 96.2% when distinguishing only direction. In the task of classifying seven activities of the excavator, the hybrid model achieves a validation accuracy of 77.6%, with some confusion between isolated activities and a Various category that includes elements of the isolated activities. With the Various category removed, the hybrid model achieves a validation accuracy of 90.7%. This study demonstrates that deep learning frameworks can model the activities of construction equipment with high accuracy. In particular, this work shows that convolutional and LSTM layers can each form effective parts of deep learning models that characterize equipment activities based on accelerometer data, and furthermore that these components can produce more effective models when combined. The findings of this study can be leveraged by researchers and industry professionals to develop reliable automated activity recognition systems for tracking and monitoring equipment performance and for measuring the productivity and the efficiency of the work performed.

中文翻译：

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使用卷积循环网络进行建筑活动识别

摘要 虽然重型设备是许多建设项目中不可或缺的资源，但往往未被充分利用。低效的使用模式和频繁的空转导致排放量和项目成本增加。改善使用模式的努力通常始于活动跟踪。最近对自动活动跟踪的研究利用传感设备和物联网 (IoT) 框架来支持机器学习模型，这些模型可以预测受监控设备的行为。然而，浅层机器学习模型需要复杂的手动特征工程，可以通过最近的深度学习方法进一步自动化。深度学习方法不仅可以提高自动化程度，还可以通过避免手动特征设计引入的偏差来提高准确性。本文提出了一种建筑设备活动识别框架，该框架使用深度学习架构来预测通过加速度计监控的重型建筑设备的活动，并将该框架应用于执行实际工作的压路机和挖掘机。将简单基线卷积神经网络 (CNN) 的性能与包含卷积和循环长短期记忆 (LSTM) 层的混合网络进行比较。在研究的所有实例中，混合模型都优于基线模型。在对碾压机的活动进行分类的任务中，混合模型在呈现六个活动时的验证精度为 77.1%，仅区分方向时的验证精度为 96.2%。在对挖掘机的七项活动进行分类的任务中，混合模型实现了 77.6% 的验证准确率，在孤立活动和包含孤立活动元素的各种类别之间存在一些混淆。移除了各种类别后，混合模型的验证准确率达到了 90.7%。这项研究表明，深度学习框架可以高精度地对建筑设备的活动进行建模。特别是，这项工作表明，卷积层和 LSTM 层都可以形成深度学习模型的有效部分，这些部分可以根据加速度计数据表征设备活动，而且这些组件在组合时可以产生更有效的模型。