Human Skill Quantification for Excavator Operation using Random Forest

Abstract

In the construction field, the improvement of the work efficiency is one of important problems. However, the work efficiency using construction equipment depends on their operation skills. Thus, in order to increase the work efficiency, the operation skill is required to be quantitatively evaluated. In this study, the Random Forest, one of machine learning method, is adopted as the quantitatively evaluation for the operation skill of construction equipment. Evaluated target is the operation on an excavation to load onto a truck for a hydraulic excavator. The Random Forest learns to classify some states by the pilot pressure of skilled worker's operation. States are defined as 'dig', 'lift', 'dump', 'reposition', and 'idle'. The Random Forest with the learning result of skilled worker is applied to other worker's operation. The human skill quantification is verified based on the 'idle' state.

Evaluation of excavation motion sequence for hydraulic excavators based on extraction of excavation style and phase

“…Table 1 shows the previous studies comparison for these requirements. Some previous studies evaluate the operation of the excavators, but the target excavation task of those studies is simple tasks and far from real construction applications (Du et al, 2018; Imaji et al, 2017; Koiwai et al, 2016).…”