AI-Assisted Bridge Conceptual Design: A Hybrid Knowledge- and Learning-Based Approach to Automated Span Planning

Abstract: Bridge conceptual design is challenged by heterogeneous boundary conditions, strict engineering constraints, and large solution spaces. This paper presents an AI-assisted pipeline for automated bridge conceptual design, focusing on early-stage span layout planning. The proposed approach integrates ontology-based semantic knowledge representation with reinforcement learning to enable automated decision-making while preserving engineering consistency. By embedding engineering constraints into the learning environment, the method supports efficient exploration of feasible span configurations. The results indicate that the proposed pipeline provides a robust and extensible foundation for AI-supported bridge planning in realistic engineering scenarios.