Inefficiencies in the management of earthmoving equipment greatly contribute to the productivity gap of infrastructure projects. This paper develops and tests a Deep Neural Network (DNN) model for estimating the productivity of excavators and establishing a productivity measure for their benchmark. After investigating current practices for measuring the productivity of earthwork equipment during 13 interviews with selected industry experts, the DNN model was developed and tested in one of the ‘High Speed rail second phase’ (HS2) sites.

The accuracy of prediction achieved by the DNN model was evaluated using the coefficient of determination (R2) and the Weighted Absolute Percentage Error (WAPE) resulting in 0.87 and 69.64%, respectively. This is an adequate level of accuracy when compared to other similar studies. However, according to the WAPE method, the accuracy is still 10.36% below the threshold (i.e. 80%) expected by the industry experts. An inspection of the prediction results over the testing period (21 days) revealed better precision in days with high excavation volumes compared to days with low excavation volumes. This was attributed to the likely involvement of manual work (i.e. archaeologists in the case of the selected site) alongside some of the excavators, which caused gaps in telematics data. This indicates that the accuracy attained is adequate, but the proposed approach is more accurate in a highly mechanised environment (i.e. excavation work with equipment predominantly and limited manual interventions) compared to a mixed mechanised-manual working environment. A bottom-up benchmark measure (i.e. excavation rate) that can be used to measure and benchmark the excavation performance of an individual or a group of equipment, through a work area, to a whole site was also proposed and discussed.

土方设备管理效率低下，极大地加剧了基础设施项目的生产率差距。本文开发并测试了深度神经网络（DNN）模型，用于估计挖掘机的生产率并建立基准的生产率度量。在与选定的行业专家进行了13次访谈中调查了用于测量土方设备生产率的当前实践之后，DNN模型已在“高铁第二阶段”（HS2）站点之一开发和测试。

使用确定系数（R2）和加权绝对百分比误差（WAPE）分别评估DNN模型获得的预测准确性，得出的准确度分别为0.87和69.64％。与其他类似研究相比，这是足够的准确性。但是，根据WAPE方法，准确度仍比行业专家预期的阈值（即80％）低10.36％。在测试期间（21天）内对预测结果的检查显示，与开挖量小的天数相比，开挖量大的天数的精度更高。这是由于可能与其他挖掘机一起进行了人工作业（例如，选定地点的考古学家），这导致了远程信息处理数据的空白。这表明所达到的精度是足够的，但是与混合机械化手动工作环境相比，该方法在高度机械化的环境（即主要使用设备进行挖掘工作且人工干预较少）下更为准确。还提出并讨论了一种自下而上的基准量度（即开挖率），该量度可用于对一个或一组设备的整个工作区域的挖掘性能进行测量和基准化。