The planning process for bridge construction is time-consuming, prone to errors, and cost-intensive, because bridges always have to be individually adapted to specific boundary conditions, for example, the respective topography situation. Despite sharing the same fundamental physical principles, bridge structures can take different forms due to regional variations in engineering expertise. Existing computational design approaches for bridges are predominantly rule-based or parametric. To support early-stage decision-making, successful practices from around the world could be analyzed using image-based, data-driven artificial intelligence (AI) methods. Hence, this work presents an alternative, result-oriented, data-driven method based on generative AI to assist engineers in the conceptual design phase of bridge construction. First, the research background and related works in structural design, with a focus on bridge engineering, are elaborated. Subsequently, we propose a multimodal generative model combining pix2pix and BERT to map valley topographies and textual design specifications to conceptual bridge topology configurations. The approach leverages image-based terrain information and text-based design descriptors to infer structurally plausible configurations from precedent data, rather than relying on explicitly formulated design rules. The results show that the model can reproduce multiple topology patterns consistent with the training data and generate constraint-consistent configurations for topographies with similar geometric characteristics. The findings demonstrate that result-oriented, data-driven generative models can support early-stage bridge topology exploration under controlled conditions, providing a complementary alternative to parametric design workflows. The paper concludes by outlining methodological implications and directions for extending the approach toward broader applicability.