A Case Study of Automated Simulation for Excavator Cycle Time Estimation

Efficient planning of earthworks is critical for the success of construction projects, accounting for approximately 30% of the project budget. However, traditional approaches, relying on managers' experience, or using standardized estimating systems and equipment manufacturer data, are regarded as inaccurate due to the fact that such methods may are limited in incorporating various site conditions that influence excavator operation, such as digging depth, swing angle, and terrain shape. This study proposes a reinforcement learning-based simulation approach for predicting the cycle time of an excavator given field conditions. For the training of an excavator agent, terrain point clouds of a site are collected and used to build a 3D virtual simulation environment representing a real site where the agent learns how to operate the excavation through trial and error processes. The proposed approach achieves a prediction accuracy of 88.89%, which is higher than that of a traditional method (77.06%). It is thus expected that the proposed approach may allow for improving the accuracy of cycle time estimation for earthmoving planning in practice.