Thesis: Ontology-based multi-source heterogeneous O&M data integration framework for tunnel structural health assessment has been published in the journal Structure and Infrastructure Engineering.
As a typical representative of infrastructure, tunnels are indispensable carriers for the normal operation of cities, with their safe and efficient operation directly influencing urban efficiency. However, the various data supporting tunnel operation and maintenance (O&M) exhibit significant diverse sources and structural differences, which pose substantial challenges to tasks such as tunnel structural health assessment. To address these challenges, this paper proposes an ontology-based multi-source heterogeneous O&M data integration framework to support the assessment of tunnel structural health, thereby improving decision-making efficiency in tunnel maintenance. The framework consists of four layers: data layer, ontology layer, mapping layer, and application layer, enabling the unified modeling, integration, and comprehensive application of multi-source heterogeneous tunnel O&M data. Additionally, the proposed framework is applied to a practical engineering project, the Tanglang Mountain Tunnel. Compared with existing methods, the framework demonstrates improvements in data fusion accuracy, data completeness, and operational efficiency.
Note: Structure and Infrastructure Engineering is a journal in the field of engineering technology in the Q2 zone, with an impact factor of 2.6. The first author of the paper is Liu Longxiang, a master's student, and I am the corresponding author. The research results were funded by the National Key Research and Development Program of China .