基于变精度粗糙集的安全生产监督决策方法研究
|
摘要
为克服资源不足与建设项目众多之间的矛盾,提高我国建筑安全监督水平,结合建筑安全事故特征和变精度粗糙集(VPRS)理论的应用优势,提出基于VPRS的建筑施工项目安全预测方法及安全监督决策方法。以深基坑工程为例,构造该类施工项目的安全属性集,借助VPRS模型和以往工程案例资料,得到深基坑工程安全监督的知识域,以此提出安全监督对策。结果表明,基于VPRS的安全监督决策方法适应安全监督的要求,构建更为系统的安全属性集和庞大的案例库是决定该方法应用效果的关键。
In order to overcome the contradiction between resources inadequacy and increasing number of construction projects,and rise the level of safety supervision,features of construction safety accident and advantages of the VPRS theory were analyzed.A new construction accident forecasting method and a decision-making method based on VPRS were worked out which can be applied in construction safety supervision.A case of deep foundation pit was studied and the data on thirty engineering projects were investigated.A safety property set of construction projects was constructed.With the help of the VPRS model,the knowledge domain on safety supervision in deep foundation pit engineering was got.Safety supervision measures were proposed.The results show that the decision-making method based on VPRS meets the requirements of safety supervision,and that to build a more systematic set of safety attributes and a comprehensive case database is the key to ensuring the application effect of this method.
In order to overcome the contradiction between resources inadequacy and increasing number of construction projects,and rise the level of safety supervision,features of construction safety accident and advantages of the VPRS theory were analyzed.A new construction accident forecasting method and a decision-making method based on VPRS were worked out which can be applied in construction safety supervision.A case of deep foundation pit was studied and the data on thirty engineering projects were investigated.A safety property set of construction projects was constructed.With the help of the VPRS model,the knowledge domain on safety supervision in deep foundation pit engineering was got.Safety supervision measures were proposed.The results show that the decision-making method based on VPRS meets the requirements of safety supervision,and that to build a more systematic set of safety attributes and a comprehensive case database is the key to ensuring the application effect of this method.
引文
[1]Matthew R Hallowell.A Formal Model for Construction Safety and Health Risk Management[D].Oregon State Universi-ty,2008.
[2]国家统计数据库:固定资产投资和房地产[EB].[2012-09-30].http://219.235.129.58/.China Statistical Database:Investment in fixed assets and real estate[EB].[2012-09-30].http://219.235.129.58/.
[3]Hinze J,HUANG Xin-yu,Terry L.The nature of struck-by accidents[J].Journal of Construction Engineering andManagement,2005,131(2):262-268.
[4]Carter G,Smith S D.Safety hazard identification on construction projects[J].Journal of Construction Engineering andManagement,2006,132(2):197-205.
[5]张印玉.建筑施工现场重大危险源辨识、评价与控制的探讨[J].施工技术,2007,36(7):468-473.ZHANG Yin-yu.Discussion on identification,judgment and control of hazard sources in construction site[J].ConstructionTechnology,2007,36(7):468-473.
[6]吴伟巍.施工现场安全危险源实时监控与安全风险预测方法研究[D].南京:东南大学,2009.WU Wei-wei.Real-time Tracking Methods of Safety Hazards and Predictive Model of Safety Risks on ConstructionSites[D].Nanjing:Southeast University,2009.
[7]赵小柠,马昌喜.基于范例推理的灾害性地震应急物资需求预测研究[J].中国安全科学学报,2012,22(8):3-9.ZHAO Xiao-ning,MA Chang-xi.Research on predicting emergency material demand after disastrous earthquake based oncase-based reasoning[J].China Safety Science Journal,2012,22(8):3-9.
[8]魏凯,管志川,韦杰宏,等.基于神经网络和Monte-Carlo模拟的钻井工程风险评估方法[J].中国安全科学学报,2013,23(2):123-129.WEI Kai,GUAN Zhi-chuan,WEI Jie-hong.Drilling engineering risk evaluation method based on neural network andMonte-Carlo simulation[J].China Safety Science Journal,2013,23(2):123-129.
[9]王忠勇,王志勇,方千山.基于模糊控制理论和神经网络的建筑火灾报警系统[J].中国安全科学学报,2004,14(4):111-113.WANG Zhong-yong,WANG Zhi-yong,FANG Qian-shan.Building fire alarm system based on fuzzy theory and neuralnetworks[J].China Safety Science Journal,2004,14(4):111-113.
[10]陈伟珂,张铮燕.地铁施工灾害关键警兆监测指标研究[J].中国安全科学学报,2013,23(1):148-154.CHEN Wei-ke,ZHANG Zheng-yan.Research on key monitoring indicators of subway construction disaster warning signsbased on WBS-RBS and association rules[J].China Safety Science Journal,2013,23(1):148-154.
[11]Janicak C A.The Three Factor Accident Prediction in Ventory[D].Chicago:Loyola University Chicago,1993.
[12]Beynon M.Reducts within the variable precision rough sets model:a further investigation[J].European Journal of Oper-ational Research,2001,134(3):592-605.
[13]Loughborough University and UMIST for the Health and Safety Executive.Causal Factors in Construction AccidentsResearch Report.HSE Books[R],2003.
[14]JGJ/T77—2010,施工企业安全生产评价标准[S].JGJ/T77-2010,Standard of the Work Safety Assessment for Construction Company[S].
[15]生产安全事故报告和调查处理条例[OL].[2007-04-19].http://www.gov.cnzwgk2007-04/19/content_588577.htm.Production safety accident reporting and investigation regulations[EB].[2007-04-19].http://www.gov.cnzwgk2007-04/19/content_588577.htm.
[2]国家统计数据库:固定资产投资和房地产[EB].[2012-09-30].http://219.235.129.58/.China Statistical Database:Investment in fixed assets and real estate[EB].[2012-09-30].http://219.235.129.58/.
[3]Hinze J,HUANG Xin-yu,Terry L.The nature of struck-by accidents[J].Journal of Construction Engineering andManagement,2005,131(2):262-268.
[4]Carter G,Smith S D.Safety hazard identification on construction projects[J].Journal of Construction Engineering andManagement,2006,132(2):197-205.
[5]张印玉.建筑施工现场重大危险源辨识、评价与控制的探讨[J].施工技术,2007,36(7):468-473.ZHANG Yin-yu.Discussion on identification,judgment and control of hazard sources in construction site[J].ConstructionTechnology,2007,36(7):468-473.
[6]吴伟巍.施工现场安全危险源实时监控与安全风险预测方法研究[D].南京:东南大学,2009.WU Wei-wei.Real-time Tracking Methods of Safety Hazards and Predictive Model of Safety Risks on ConstructionSites[D].Nanjing:Southeast University,2009.
[7]赵小柠,马昌喜.基于范例推理的灾害性地震应急物资需求预测研究[J].中国安全科学学报,2012,22(8):3-9.ZHAO Xiao-ning,MA Chang-xi.Research on predicting emergency material demand after disastrous earthquake based oncase-based reasoning[J].China Safety Science Journal,2012,22(8):3-9.
[8]魏凯,管志川,韦杰宏,等.基于神经网络和Monte-Carlo模拟的钻井工程风险评估方法[J].中国安全科学学报,2013,23(2):123-129.WEI Kai,GUAN Zhi-chuan,WEI Jie-hong.Drilling engineering risk evaluation method based on neural network andMonte-Carlo simulation[J].China Safety Science Journal,2013,23(2):123-129.
[9]王忠勇,王志勇,方千山.基于模糊控制理论和神经网络的建筑火灾报警系统[J].中国安全科学学报,2004,14(4):111-113.WANG Zhong-yong,WANG Zhi-yong,FANG Qian-shan.Building fire alarm system based on fuzzy theory and neuralnetworks[J].China Safety Science Journal,2004,14(4):111-113.
[10]陈伟珂,张铮燕.地铁施工灾害关键警兆监测指标研究[J].中国安全科学学报,2013,23(1):148-154.CHEN Wei-ke,ZHANG Zheng-yan.Research on key monitoring indicators of subway construction disaster warning signsbased on WBS-RBS and association rules[J].China Safety Science Journal,2013,23(1):148-154.
[11]Janicak C A.The Three Factor Accident Prediction in Ventory[D].Chicago:Loyola University Chicago,1993.
[12]Beynon M.Reducts within the variable precision rough sets model:a further investigation[J].European Journal of Oper-ational Research,2001,134(3):592-605.
[13]Loughborough University and UMIST for the Health and Safety Executive.Causal Factors in Construction AccidentsResearch Report.HSE Books[R],2003.
[14]JGJ/T77—2010,施工企业安全生产评价标准[S].JGJ/T77-2010,Standard of the Work Safety Assessment for Construction Company[S].
[15]生产安全事故报告和调查处理条例[OL].[2007-04-19].http://www.gov.cnzwgk2007-04/19/content_588577.htm.Production safety accident reporting and investigation regulations[EB].[2007-04-19].http://www.gov.cnzwgk2007-04/19/content_588577.htm.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心