Articulated haulers are heavy construction machines used for earthmoving tasks. To optimize factors such as weight, cost, CO₂ footprint, and productivity, maintaining the optimal chassis weight is critical. To support this, a digital twin approach is developed. In this study, two digital twin concepts for articulated haulers are evaluated and compared based on their prediction accuracy for component life. Both concepts leverage vehicle simulations, but one introduces segmented simulation data to increase flexibility and improve data utilization. Cross-validation is employed to assess the core idea of the digital twins. Results show that the best-performing concept achieves a mean cross-validation accuracy of 99%, demonstrating strong potential for future design optimization.