Railway tunnel portals serve as critical junctions connecting underground and above-ground structures in rail infrastructure, requiring integrated multidisciplinary knowledge and seamless data interoperability in their design. While existing research has made notable progress in data integration and intelligent decision support, two critical challenges persist: 1. Existing tunnel portal component libraries are highly dependent on specific design software and lack a standardized IFC-based library, limiting data sharing and interoperability. 2. Current data-driven tunnel portal selection methods primarily rely on statistical matching strategies while neglecting the inference and computation of design component parameters, making it difficult to achieve intelligent application of engineering knowledge. This study proposes an ontology-based intelligent assistant design method for railway tunnel portals. The approach involves deconstructing traditional portal components to create a parametric component library, synthesizing railway tunnel design standards and expert knowledge to construct the Railway Tunnel Portal Domain Ontology (RTPDO), and formulating data-driven inference rules to establish a closed-loop "data-knowledge-decision" intelligent assistant design framework. Case study confirms the efficacy of this approach, demonstrating its applicability in intelligent railway tunnel portal design.
Ontology-Based Intelligent Assistant Design Method for Railway Tunnel Portal Models
2025-05-16
10271470 byte
Conference paper
Electronic Resource
English
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