Al-Zuriqat, ThamerThamerAl-ZuriqatBirmole, DhananjayDhananjayBirmoleDragos, KosmasKosmasDragosSmarsly, KayKaySmarsly2026-09-092026-09-092026-072026 EC3: European Conference on Computing in Constructionhttps://hdl.handle.net/11420/64715Sewer systems constitute urban infrastructure critical for public health, environmental protection, and flood prevention. Decision-making in sewer systems maintenance often fails to effectively and efficiently interpret heterogeneous and multimodal inspection data into actionable maintenance strategies. To improve decision-making in sewer systems maintenance, this paper introduces a prescriptive maintenance (RxM) framework, in which a pre-trained large language model (LLM) is adapted to the domain of sewer systems using adapter-based fine-tuning. As will be shown in this paper, the LLM is capable of interpreting heterogeneous, multimodal inspection data and recommending stepwise repair procedures of sewer systems, following the paradigm of RxM.enTechnology::600: TechnologyConference Paper10.35490/EC3.2026.202