地铁盾和文字挖掘的风险管理: Comparison
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Please note this is a comparison between Version 2 by Jessie Wu and Version 1 by Jiaji Zhang.

在地铁工程施工方法中，盾构法施工技术具有施工机械化程度高、施工过程对环境影响小、盾构机对地层适应性强等优点，已成为城市地铁建设中应用较为广泛的施工方法。但是，由于地铁建设中周边建筑物（结构）的复杂性，加上地铁盾构法施工活动的多样性和施工环境的不确定性，在一定程度上决定了地铁建设过程非常复杂。Among the construction methods for subway projects, shield method construction technology has become a more widely used construction method for urban subway construction due to the advantages of a high degree of construction mechanization, low impact of the construction process on the environment, and strong adaptability of the shield machine to the stratum, etc. However, because of the complexity of the surrounding buildings (structures) in the subway construction, coupled with the diversity of the subway shield method construction activities and the uncertainties in the construction environment, to a certain extent, it is determined that the subway construction process is very complicated.

  • metro construction
  • shield method
  • text mining
  • risk management

1. Introduction

Since the 21st century, the construction of rail transportation has been increasing, and urban rail transportation represented by the subway has the characteristics of large passenger capacity, high safety, high speed, low pollution, low energy consumption, etc., which largely relieves the traffic pressure in the city. However, the subway construction process is complex; the shield method is the main technology of subway construction, but safety accidents occur frequently. Subway shield construction safety risk management has defects and deficiencies. Risk management relies on subjective experience, the lack of mining, and the utilization of objective text data; it is difficult to meet the needs of subway shield construction safety risk management. Therefore, this paper carries out subway shield construction safety risk identification based on safety risk text data, extracts coping strategies and measures for key risks, and plays an important role in helping to improve subway shield construction site safety management.

2. Research on Risk Management of Subway Shield

Li等以盾构倒塌事故为研究对象，分析了建筑工人安全能力的形成机制，基于对隐患的感知和判断，建立了建筑工人安全能力模型[1]。 et al. took shield collapse accidents as the research object, analyzed the formation mechanism of construction workers’ safety ability, and built a construction workers’ safety ability model based on the perception and judgment of hidden dangers [1]. Chen等将贝叶斯网络中的三角模糊数和云理论相结合，建立了盾构地下通道段的风险分析模型，并以实际工程为例进行了风险评估[2]。 et al. combined the triangular fuzzy number and cloud theory in the Bayesian network to build a risk analysis model for the underpass section of the shield and conducted risk assessment by taking the actual project as an example [2]. Yin等基于社会网络分析建立了地铁盾构施工安全风险网络结构，以线路中心性为标准识别关键风险，为风险控制提供决策依据[3]。 et al. established the safety risk network structure of subway shield construction based on social network analysis and identified key risks with line centrality as the standard to provide a decision-making basis for risk control [3]. Taking Nanning Metro Line 3 as the background, Liu等以南宁地铁3号线为背景，划分盾构施工剖面，建立盾构施工结构模型，并通过矩阵权重计算识别关键危险因素[4]。基于贝叶斯网络， et al. divided the shield construction section to establish the shield construction structure model and identify key risk factors via matrix weight calculation [4]. Based on Bayesian networks, Chung等建立了盾构隧道掘进机风险分析模型，系统识别盾构筑筑的潜在风险事件，估算事故的对策成本，评估潜在风险事件的风险等级[5]。 et al. established a TBM risk analysis model for shield construction, systematically identified potential risk events of shield construction, estimated the countermeasures cost of accidents, and assessed the risk level of potential risk events [5]. According to the geological risks of subway shield construction, Nezarat等根据地铁盾构施工的地质风险，采用模糊层次分析法对各种风险因素进行分类，以指导现场盾构施工[6]。 et al. used a fuzzy analytic hierarchy process to sort various risk factors so as to guide the shield construction on site [6]. Yazdani等提出了一种基于模糊集理论的风险评估模型，用于评估地铁盾构施工过程中的风险事件，并将其与传统风险评估方法进行了比较[7]。周等人利用复杂网络对地铁施工事故进行分析，最终得到了一个有向无力网络，该网络有 et al. proposed a risk assessment model based on fuzzy set theory to evaluate risk events during subway shield construction and compared it with traditional risk assessment methods [7]. Zhou et al. used complex networks to analyze subway construction accidents and finally obtained a directed powerless network with 26个顶点， vertices and 49条边。通过数据分析，采用免疫策略降低网络效率，指导现场地铁盾构施工安全管理[8]。薛等建立了基于盾构隧道地下通道河的开挖工作面稳定性评价指标体系，采用 edges. Via data analysis, immune strategies were adopted to reduce network efficiency and guide the safety management of subway shield construction on site [8]. Xue et al. set up the evaluation index system of excavation face stability based on the underpass river of shield tunneling, calculated the weight by the AHP-熵权法计算权重，建立了基于思想点法的开挖工作面稳定性评价模型[9]。任等在习地铁entropy weight method, and established the evaluation model of excavation face stability based on the thought point method [9]. Ren et al. set up a construction safety risk evaluation index system for buildings adjacent to shield construction in a certain section of Metro Line 3号线某区段建立了盾构施工相邻建筑物施工安全风险评价指标体系，采用模糊综合评价方法评价该区域盾构施工安全风险等级[10]。 in Xi ‘an and used a fuzzy comprehensive evaluation method to evaluate the safety risk level of shield construction in the area [10]. Chen等结合主观和客观方法识别地铁盾构施工的安全风险因素，建立具有解释结构模型的事故致病模型，并与决策实验室分析了这些因素之间的影响关系[11]。 et al. combined subjective and objective methods to identify the safety risk factors of subway shield construction built an accident causative model with an interpretive structure model and analyzed the influence relationship between the factors with a decision laboratory [11]. Wang et al. took the Wuhang等以武汉地铁项目为例，对影响地铁运营隧道安全系统的因素进行了总体分析，建立了分层结构模型。在综合风险评估的基础上，采用模糊综合判断模型、最大隶属度原则、R=P×C[12]确定隧道盾构施工段风险等级。以天津地铁项目为例，潘等建立了基于模糊熵理论。此外，为了定量分析安全风险系统中各因素之间的耦合度，基于物理学中的耦合度理论建立了耦合度计算模型[13]。曹等人研究了一种在盾构隧道施工中建立风险分析标准的方法：使用代表性条件的三维数值建模。然后根据研究结果推荐风险控制措施[14]。黄老师等将 subway project as an example, conducted an overall analysis of the factors affecting the safety system of subway operation tunnels, and established a hierarchical structure model. On the basis of comprehensive risk evaluation, the risk grade of the tunnel shield construction section is determined by the fuzzy synthesis judgment model, maximum membership principle, and R = P × C [12]. Taking the Tianjin Metro project as an example, Pan et al. established a comprehensive index system of shield tunnel construction safety risk system based on fuzzy entropy theory. In addition, in order to quantitatively analyze the coupling degree between various factors in the safety risk system, a calculation model of coupling degree is established based on the coupling degree theory in physics [13]. Cao et al. studied a method of establishing risk analysis standards in shield tunnel construction: 3D numerical modeling using representative conditions. Risk control measures were then recommended based on the findings [14]. Huang et al. compared the TDCM评价方法与一维云模型（ evaluation method with the one-dimensional cloud model (ODCM）评价方法和模糊综合评价方法（FCEM）进行了比较，并讨论了TDCM评价方法的优点和适用性[15]。) evaluation method and the Fuzzy Comprehensive evaluation method (FCEM) and discussed the advantages and applicability of the TDCM evaluation method [15].

3. 文本挖掘在地铁建设中的应用Application of Text Mining in Subway Construction

文本挖掘是从非结构化文本信息中获取有趣或有用的模式的过程。文本挖掘涵盖了多种技术，包括信息提取、信息检索、自然语言处理和数据挖掘。Text mining is the process of obtaining interesting or useful patterns from unstructured text information. Text mining covers a variety of technologies, including information extraction, information retrieval, natural language processing, and data mining. Liu等将文本挖掘技术应用于隧道工程，借助R语言和捷巴分词建立了隧道工程风险评估指标体系，并在此基础上开发了隧道工程风险评估体系[16]。 et al. applied text mining technology to tunnel engineering, established a tunnel engineering risk assessment index system with the help of R language and Jieba word segmentation, and developed a tunnel engineering risk assessment system on this basis [16]. Liu等收集了 et al. collected the subway construction safety accidents after the 21世纪以后的地铁施工安全事故，建立了施工安全事故数据库，识别了48个因数和13个事故类型，利用关联规则和复杂网络构建了地铁施工安全事故因果网络，并对节点进行了免疫研究[17]st century, established a construction safety accident database, identified 48 due factors and 13 accident types, used association rules and complex networks to build a subway construction safety accident causation network, and conducted immune research on nodes [17]. Xu等以安全事故报告为语料库，基于文本挖掘方法，包括因果关系和耦合关系，识别地铁施工安全风险因素和风险关联关系，建立基于解释结构模型和贝叶斯网络的风险评估模型[18]。 et al. took the safety accident report as a corpus and identified the risk factors and risk correlation relationship of subway construction safety based on text mining method, including causality and coupling relationship, and established a risk assessment model based on interpretive structure model and Bayesian network [18]. Ji等利用网络爬虫采集地铁施工安全事故案例，采用文本挖掘方法识别 et al. used a web crawler to collect subway construction safety accident cases, identified 67个风险关键词，通过复杂网络建立地铁施工安全风险网络，识别关键风险节点。然后，基于贝叶斯网络进行风险概率推理，并通过情景分析进行成本控制[19]。 risk keywords by text mining method, established a subway construction safety risk network via a complex network, and identified key risk nodes. Then, the risk probability reasoning was carried out based on the Bayesian network, and the cost control was carried out via scenario analysis [19]. Son et al. conducted text mining of bidding documents an等对韩国大型EPC项目的招投标文件和合同文件进行了文本挖掘，建立了进度延误估计模型，以评估预测进度风险，从而确定合适的项目工期[20]。d contract documents of large-scale EPC projects in South Korea and established a schedule delay estimation model to assess the forecast schedule risk so as to determine the appropriate project duration [20]. Li等利用关联规则寻找地铁工程风险相关性，得到 et al. used association rules to find the risk correlation of subway engineering, obtained 45个地铁工程施工监控组合，并提出风险对策[21]。 subway engineering construction monitoring combinations, and proposed risk countermeasures [21]. Zhang等研究了数据挖掘技术在地铁自动数据采集系统信息处理中的应用，并提出了数据挖掘系统的框架。本文基于地铁数据采集技术，研究了地铁客流和出行信息的分析方法。利用数据挖掘技术和统计分析，从收集的数据中推导出地铁 et al. studied the application of data mining technology in the information processing of the subway automatic data acquisition system and proposed the framework of the data mining system. Based on subway data acquisition technology, this paper studies the analysis method of subway passenger flow and travel information. By using data mining technology and statistical analysis, the metro OD矩阵和交通率，详细描述乘客的出行时间分布，对地铁系统的调度和管理具有重要意义[22]。 matrix and traffic rate are derived from the collected data, and the travel time distribution of passengers is described in detail, which is of great significance to the scheduling and management of the metro system [22]. Hsu et 等人为台北地铁案例研究示例开发了一种响应式乘客信件系统。在随机抽取具有文本类型的乘客信件后，利用文本信件文件中的文本挖掘技术寻找习惯甚至新的关键字，以提高服务质量，例如客户满意度[23]。al. developed a responsive passenger letter system for the Taipei Metro case study example. After random sampling of passenger letters with text types was obtained, text mining technology in text letter files was used to find customary or even new keywords to improve service quality, such as customer satisfaction [23]. Mo等人提出了一种措施，该措施使用数据挖掘技术为地铁系统设备创建结构化数据集，通过分析历史维护记录来监控其日常状态和可能的故障发展趋势，并尝试在任何设备实际发生故障之前应用预测维度[24]。 et al. propose a measure that uses data mining techniques to create structured data sets for subway system equipment by analyzing historical maintenance records to monitor its daily status and possible fault development trends and attempt to apply predictive dimensions before any equipment actually fails [24]. Juan等人从文件中提取了五年的地铁事故记录。通过文本聚类，得到地铁延误的主要影响因素。采用 et al. extracted five years of subway accident records from the document. Via text clustering, the main influencing factors of subway delay are obtained. The relationship between the influencing factors and subway delay was established by using a logit回归模型[25]建立了影响因素与地铁延误的关系。 regression model [25]. Chang et al. 等人通过深度学习和复杂网络理论探索了数据驱动的安全风险评估和响应模型。基于关键安全风险因素，提出相应的风险对策，以验证数据驱动的安全风险管理模型的有效性和适用性[26]。explored data-driven security risk assessment and response models via deep learning and complex network theory. Based on key security risk factors, corresponding risk countermeasures are proposed to verify the effectiveness and applicability of the data-driven security risk management model [26]. 牟等研究了基于地铁运营日志文本挖掘的地铁运营危险性识别算法，研究结果表明，Mou et al. studied the subway operation hazard identification algorithm based on the text mining of subway operation logs, and the research results showed that the AFP-tree算法能够显著提高计算效率，通过分析挖掘出共25类有效的关键危险源，研究结果可为地铁运营单元实现“事前”事故预防提供重要依据[27] algorithm could significantly improve the computing efficiency and a total of 25 types of effective critical hazard sources were excavated via the analysis, and the research results could provide an important basis for the subway operation unit to achieve “prior” accident prevention [27]. Ye Cheng et al.叶成等提出了一种基于改进 proposed a classification method based on an improved BERT模型的分类方法和一种基于知识图谱的结构化检索方法，实现了地铁建设隐患的文本分类和高效数据检索，为集成系统的开发应用提供了支撑。同时，该研究还可以为基于深度学习和知识图谱技术的建筑领域的文本处理、数据检索和管理提供参考[28]。 model and a structured retrieval method based on a knowledge graph to realize text classification and efficient data retrieval of subway construction hidden dangers and provide support for the development and application of the integrated system. At the same time, this research can also provide references for text processing, data retrieval, and management in the field of architecture based on deep learning and knowledge graph technology [28]. Pan et al. proposed a text framework for automated analysis of hidden danger detection等提出了一种基于文本挖掘和可视化技术的隐患检测自动化分析文本框架，该框架在2016—2018年武汉地铁施工安全隐患检测记录分析中得到了应用和验证。实验结果表明，该框架能够有效挖掘出34类隐患对应的关键点和视觉信息[29]。针对海量非结构化地铁建设隐患文本， based on text mining and visualization technology, which was applied and verified in the analysis of construction safety hidden danger detection records of Wuhan Metro from 2016 to 2018. The experimental results show that the framework can effectively excavate the key points and visual information corresponding to 34 types of hidden dangers [29]. In view of the massive unstructured subway construction hidden danger text, Hei等提出利用文本挖掘技术和可视化技术对地铁建设隐患进行分析，将抽象文本数据转化为可视化信息，辅助未来隐患排查并提供数据支撑，可用于地铁企业编制隐患排查年鉴，利用可视化分析结果。在工人安全培训中具有实际应用价值[30]。 et al. proposed the idea of using text mining technology and visualization technology to analyze subway construction hidden danger so as to transform abstract text data into visual information, assist future hidden danger investigation and provide data support for it, which can be used for subway enterprises to compile hidden danger investigation yearbook and use the visual analysis results. It has practical application value in worker safety training [30]. Li等采用 et al. used R语言和文本挖掘方法，利用词云和网络结构图对事故报告和可视化文本挖掘结果进行分词处理、特征项选择、向量空间模型构建和共现规则识别。共发现地铁施工安全事故的23个关键危险因素和31个一般危险因素[<>]。 language and text mining methods to carry out word segmentation processing, feature item selection, vector space model construction, and co-occurrence rule recognition for accident reports and visualized text mining results by using word cloud and network structure graphs. Six key risk factors and 23 general risk factors of subway construction safety accidents were found [31].

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