This research investigated how machine operator expertise, strategies, and decision-making can be integrated into operator models that simulate authentic human behavior in construction machine operations. Physical prototype tests of construction machines require significant time and cost. However, computer-based simulation is often limited by the fidelity in which human operators are modeled. A greater understanding of how highly skilled operators obtain high machine performance and productivity can inform machine development and advance construction automation technology. Operator interviews were conducted to build a framework of tasks, strategies, and cues commonly used while controlling an excavator through repeating work cycles. A closed loop simulation demonstrated that an operator model could simulate the trenching work cycle with multiple operator strategies, and adapt to different vehicle and work site settings. A Virtual Operator Model that captures human expert behaviors can be used to assess vehicle characteristics and efficiency, and inform the design of automation systems.