基于SVM的城市地下工程施工安全风险预测研究
摘要：为科学准确预测城市地下工程施工安全风险水平，开发一种基于支持向量机(SVM)模型的预测方法。利用Citespace对近5年相关文献进行了主题和关键词聚类分析，将聚类分析结果作为参考。基于4M1E事故要素理论，结合城市地下工程特点和专家意见，建立了契合地下工程施工实际的安全风险评价指标体系，从而构建了安全风险水平预测SVM模型。以武汉、北京、广州等地区范围内的25个城市地下工程施工项目统计数据对SVM模型进行学习训练和测试验证。结果表明：SVM模型训练集回代检验和测试集预测结果均与实际情况完全一致，可作为预测城市地下工程施工安全风险水平的有效方法。
关键词：地下工程施工安全风险预测支持向量机
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参考文献[1] LIU W,ZHAO T S,ZHOU W,et al.Safety risk factors of metro tunnel construction in China:An integrated study with EFA and SEM[J].Safety science,2018,105∶98-113.
[2] SHAN LU,NAKAMURA HITOSHI,YU SHU.The establishment and application of underground space safety evaluation system in Shanghai[J].Procedia engineering,2016,165∶433-447.
[3] 马岩，邓洪亮.基于模糊综合评价法的公路隧道防排水系统评价体系研究[J].施工技术，2020,49(7):98-103.
[4] 蔡伟涛，马叶情.基于AHP城市地下空间多灾种安全性评价[J].矿山测量，2018,46(4):108-111.
[5] 张晓峰，吕良海，白永强，等.城市地下空间模糊综合评价方法研究[J].地下空间与工程学报，2012,8(1):8-13.
[6] 孙飞祥，张兵，彭正勇，等.厦门地铁3号线盾构法与矿山法海下对接施工风险分析及应对措施[J].施工技术，2020,49(1):67-71.
[7] 张健.城市水下隧道工程施工安全风险识别与评价[J].中国西部科技，2013,12(3):66-68.
[8] ROSHANA TAKIM,MUHAMMAD HANAFI ZULKIFLI,ABDUL HADI NAWAWI.Integration of automated safety rule checking (ASRC) system for safety planning BIM-based projects in Malaysia[J].Procedia-social and behavioral sciences,2016,222∶103-110.
[9] 尹嘉鹏.支持向量机核函数及关键参数选择研究[D].哈尔滨：哈尔滨工业大学，2016.
[10] 邓宇.城市地下工程施工安全风险评价研究[D].武汉：武汉理工大学，2018.
[2] SHAN LU,NAKAMURA HITOSHI,YU SHU.The establishment and application of underground space safety evaluation system in Shanghai[J].Procedia engineering,2016,165∶433-447.
[3] 马岩，邓洪亮.基于模糊综合评价法的公路隧道防排水系统评价体系研究[J].施工技术，2020,49(7):98-103.
[4] 蔡伟涛，马叶情.基于AHP城市地下空间多灾种安全性评价[J].矿山测量，2018,46(4):108-111.
[5] 张晓峰，吕良海，白永强，等.城市地下空间模糊综合评价方法研究[J].地下空间与工程学报，2012,8(1):8-13.
[6] 孙飞祥，张兵，彭正勇，等.厦门地铁3号线盾构法与矿山法海下对接施工风险分析及应对措施[J].施工技术，2020,49(1):67-71.
[7] 张健.城市水下隧道工程施工安全风险识别与评价[J].中国西部科技，2013,12(3):66-68.
[8] ROSHANA TAKIM,MUHAMMAD HANAFI ZULKIFLI,ABDUL HADI NAWAWI.Integration of automated safety rule checking (ASRC) system for safety planning BIM-based projects in Malaysia[J].Procedia-social and behavioral sciences,2016,222∶103-110.
[9] 尹嘉鹏.支持向量机核函数及关键参数选择研究[D].哈尔滨：哈尔滨工业大学，2016.
[10] 邓宇.城市地下工程施工安全风险评价研究[D].武汉：武汉理工大学，2018.
SVM-based Method for Predicting Safety Risk of Urban Underground Engineering Construction
Abstract: In order to scientifically and accurately predict the safety risk level of urban underground engineering construction, a prediction method based on the support vector machine(SVM) model was developed. A cluster analysis of themes and keywords was carried out on related papers in the past 5 years through Citespace, and use the results of cluster analysis as a reference. Based on the 4 M1 E accident element theory, and combined with the characteristics of urban underground engineering and expert opinions, a safety risk evaluation index system that fits the actual construction of underground engineering was established, thus constructed an SVM model for predicting safety risk levels. The SVM model was trained and tested with statistical data of urban underground construction projects in 25 cities in Wuhan, Beijing, Guangzhou and other regions. The results showed that the results of the back test of training set and prediction of test set of the SVM model were completely consistent with the actual situation. Thus, SVM can be used as an effective method to predict the safety risk level of urban underground engineering construction.
Keywords: underground engineering; construction safety; risk prediction; support vector machine(SVM)
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