## 2 Literature Review
### Machine Guidance in Earthworks Operations
A review of the literature identified at least fifteen relevant research studies focusing on intelligent excavators, with many demonstrating high accuracy in classification of activities, often exceeding 90%. This suggests the potential for machine learning to play a significant role in future automation of excavators. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology Type** & **Operational Approach** & **Quality Control Methods** & **Key Enabling Technologies** \\ \hline \multirow{4}{*}{Conventional} & Manual operation & Manual verification of depth and slope & Basic GNSS, traditional surveying tools, and operator \\  & by an on-site & using physical & \\  & operator with & markers, on-site & surveying tools, \\  & assistance from a & surveys, and the & and operator \\  & signal person. & operator’s & expertise. \\  & & judgment. & \\ \hline \multirow{4}{*}{Machine guidance} & Operator-driven excavation & Continuous, sensor-based & Advanced GNSS, IMU, sensor fusion algorithms, and \\  & a augmented by & feedback & \\  & real-time visual and & immediate & \\  & auditory feedback & corrections; in situ & \\  & integrated within & adjustments based & \\  & the cab. & on precise depth & \\  & & and slope data. & \\ \hline \multirow{4}{*}{Remote control} & Operators control & Remote monitoring & High-definition cameras, wireless communication \\  & remotely using & periodic on-site & systems, and \\  & video feeds and & quality checks to & \\  & sensor data, & ensure adherence & \\  & without being & to design & \\  & physically present & specifications. & \\  & on site. & & \\ \hline \multirow{4}{*}{Fully automated} & Autonomous & Automated quality & \\  & evacuation with & control through & Robotics, artificial \\  & minimal or no & comprehensive & intelligence, \\  & direct human & sensor networks & machine learning, \\  & intervention. & and AI-driven & and extensive \\  & intervention. & network detection & sensor networks. \\  & & systems. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Excavation Control Approaches: operational strategies, quality control methods, and key enabling technologies across conventional, machine-guided, remote control, and fully automated systems. Furthermore, recent research has increasingly focused on optimizing earthworking equipment [19], developing sensor-based automated construction [20], implementing automated quality control technologies in the field [21], and enhancing the safety and operability of heavy machinery on construction sites [22; 23; 24]. These studies have primarily aimed at improving working conditions, process consistency, and overall safety rather than directly increasing operational productivity. Although semi-automated operations such as machine control have been successfully applied in certain specialized construction tasks, machine guidance in practice remains the most widely utilized productivity-enhancement technology. Consequently, this study focuses on machine guidance as a means to directly enhance excavator operator actions at the micro-level, providing a quantitative evaluation of its impact on excavation efficiency in real-world construction settings. Collectively, previous studies have indicated that machine guidance systems can provide operators with various types of feedback and guidance, potentially modifying operator activity and improving the overall productivity of excavations. While it is evident that machine guidance can lead to more productive earthwork operations, and productivity gains have been reported in numerous studies, a detailed understanding of how machine guidance specifically affects operator behavior and task performance at the micro-level remains an open research question. To address this gap, this study investigated the differences between conventional and machine-guided excavation through a comparative study conducted in a controlled construction environment.