| 铁路隧道钻爆法施工智能管理的安全质量进度知识图谱构建方法 |
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| 引用本文： | 朱庆,王所智,丁雨淋,曾浩炜,张利国,郭永欣,李函侃,王万齐,宋树宝,郝蕊,程智博.铁路隧道钻爆法施工智能管理的安全质量进度知识图谱构建方法[J].武汉大学学报(信息科学版),2022,47(8):1155-1164. |
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| 作者姓名： | 朱庆 王所智 丁雨淋 曾浩炜 张利国 郭永欣 李函侃 王万齐 宋树宝 郝蕊 程智博 |
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| 作者单位： | 1.西南交通大学地球科学与环境工程学院，四川 成都，611756 |
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| 基金项目： | 国家自然科学基金41941019 |
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| 摘 要： | 复杂艰险的山区环境和不确定的地理地质条件是影响铁路隧道施工建设安全、质量和进度的关键因素，面向智能化、精准化的施工管理，提出一种铁路隧道钻爆法施工安全质量进度知识图谱构建方法。首先，根据铁路隧道施工建设过程中与安全质量进度关联的人机料法环5类关键要素的概念与语义关系，设计了模式层自上而下和数据层自下而上双向协同的构建方式；然后，抽取实体及关系并进行融合、存储，完成模式-数据关联的知识图谱构建；最后，以某新建铁路隧道出口工区施工事件为例构建实例图谱。结果表明，该方法构建的知识图谱精细刻画了影响安全、质量和进度的关键要素属性、要素间语义关联关系以及互馈作用关系等，为铁路隧道钻爆法施工全过程的安全质量进度整体性、系统性的智能化管理提供了关键支撑，也为铁路隧道工程数字孪生奠定了基础。
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| 关 键 词： | 铁路隧道 施工管理 知识图谱 安全质量进度 人机料法环 |
| 收稿时间： | 2021-10-25 |
A Method of Safety-Quality-Schedule Knowledge Graph for Intelligent Management of Drilling and Blasting Construction of Railway Tunnels |
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| Institution: | 1.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China2.Institute of Computing Technology, China Academy of Railway Sciences Co. Ltd, Beijing 100081, China |
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| Abstract: | Objectives The complex and dangerous mountainous environment, uncertain geographical and geological conditions are the key factors affecting the safety, quality and schedule of railway tunnel construction. Methods Orient to intelligent and precise construction management, this paper proposes a top-down and bottom-up combination of railway tunnel drill safety-quality-schedule knowledge graph construction method, clarifies the conceptual connotation and semantic relationship for the five key elements of man-machine-material-method-environment(4M1E) related to safety-quality-schedule during the construction of railway tunnels, and designs a two-way collaborative construction method of mode layer from top to bottom and data layer from bottom to top, then introduces key technologies such as data acquisition and knowledge extraction. Results Taking the construction event of the Kangding No. 2 railway tunnel exit work area as an example, we construct a case knowledge graph. The results show that the knowledge graph constructed by the method in this paper finely depicts the key element attributes that affect safety-quality-schedule, the semantic relationship between the elements, and the mutual feedback relationship, etc. Conclusions Our proposed method provides key support for the overall systemic intelligent management of safety-quality-schedule in the whole process of railway tunnel drilling and blasting construction, and also lays the foundation for the digital twin of railway tunnel engineering. |
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