Ontology-driven knowledge graph for decision-making in resilience enhancement of underground structures: Framework and application

IF 6.7 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Tunnelling and Underground Space Technology Pub Date : 2025-05-09 DOI:10.1016/j.tust.2025.106739

Bin-Lin Gan , Dong-Mei Zhang , Zhong-Kai Huang , Fei-Yu Zheng , Rui Zhu , Wei Zhang

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引用次数: 0

Abstract

Enhancing the resilience of underground structures for their operation safety amidst complex disasters has become a critical societal issue. However, resilience enhancement decision-making for underground structures mainly depends on practical subjective experience currently, with insufficient integration of ontology knowledge and a clear gap in the availability of efficient and intelligent decision-making models. To address this, this paper presents a novel method for constructing a knowledge graph (KG) based on ontology to enhance the resilience of underground structures. A comprehensive resilience knowledge system considering 10 categories for underground structures is established. This system is built upon resilience quantification analysis, fault tree modeling of resilience insufficiency, and event tree analysis of disaster chain processes. A systematic approach for KG construction, integrating top-down and bottom-up strategies, is then proposed. Additionally, a multi-layered framework of KG for underground structure resilience is developed, comprising application, rule, pattern, and data layers. Resilience-related knowledge is extracted using expert empirical methods, and the data layer is constructed through semantic networks and knowledge fusion. The visualization and field application of the KG are implemented using the Neo4j graph database. Findings of this study substantially advance a methodological foundation for intelligent decision-making in resilience enhancement and safeguarding of underground infrastructures under complex disasters.

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基于本体驱动的地下结构弹性增强决策知识图谱：框架与应用

提高地下结构在复杂灾害条件下的抗灾能力，保障地下结构的安全运行，已成为一个重要的社会问题。然而，目前地下结构的弹性增强决策主要依赖于实际的主观经验，本体知识整合不足，缺乏高效、智能的决策模型。针对这一问题，本文提出了一种基于本体的知识图构建方法，以增强地下结构的弹性。建立了包含10个类别的地下结构综合回弹性知识体系。该系统建立在弹性量化分析、弹性不足故障树建模和灾害链过程事件树分析的基础上。然后，提出了一种自上而下和自下而上相结合的系统方法来构建KG。此外，开发了地下结构弹性KG的多层框架，包括应用层、规则层、模式层和数据层。采用专家经验方法提取弹性相关知识，通过语义网络和知识融合构建数据层。KG的可视化和现场应用使用Neo4j图形数据库实现。本研究结果为复杂灾害下地下基础设施弹性增强和保护的智能决策提供了方法论基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Tunnelling and Underground Space Technology 工程技术-工程：土木

CiteScore

11.90

自引率

18.80%

发文量

454

审稿时长

10.8 months

期刊介绍： Tunnelling and Underground Space Technology is an international journal which publishes authoritative articles encompassing the development of innovative uses of underground space and the results of high quality research into improved, more cost-effective techniques for the planning, geo-investigation, design, construction, operation and maintenance of underground and earth-sheltered structures. The journal provides an effective vehicle for the improved worldwide exchange of information on developments in underground technology - and the experience gained from its use - and is strongly committed to publishing papers on the interdisciplinary aspects of creating, planning, and regulating underground space.