Optimizing Linear Construction Processes: A Simulation-Based Approach Towards AI-Supported Decision Making

Abstract: Large-scale linear construction projects involve complex interdependencies, extended sites, and numerous execution variants, while existing approaches lack personalized recommendation systems for trenching applications. This paper presents a simulation-based framework integrating AI-driven optimization methods to support data-informed planning under limited historical data availability. A bottom-up simulation model captures process-specific dynamics and enables synthetic data generation, forming the basis for proposing focused parameter ranges with promising execution variants. Parametric validation across 3,200 simulation runs confirms realistic system behavior. The results indicate that AI-integrated simulation frameworks represent a viable and transferable approach for optimization in data-scarce linear construction environments.