高速公路建设期安全风险智能管理研究
|
摘要
目前,工程建设领域的事故发生数及其造成的伤亡损失仅次于煤矿业,而高速公路作为高危子行业,在“十一五”期间快速扩张的同时,安全现状也异常严峻,巨额损失严重影响了高速公路建设的健康发展。为此,政府在宏观上发布了一系列通知以加强安全监督,预防事故隐患和提高应急反应能力等,并要求安全管理工作实现五个方面质的转变。本文针对高速公路建设中安全风险管理转变的一些关键问题作了以下几个方面的理论和方法研究：
(1)智能化、网络化、信息化是未来高速公路建设安全管理的研究趋势,本文从解决实际问题的角度出发,着眼于智能方法在施工现场中的应用,切实结合高速公路项目特点及安全现状,选用基于案例推理、规则挖掘、支持向量机和可变模糊集等智能化算法,并详细阐述其理论优越性及应用可行性。
(2)数据预处理在人工智能中的地位举足轻重,占据整个智能化流程的60%,其主要目的一是为算法应用提供数据准备,二是防止数据集偏斜导致结果误差,其中,数据转换、归一化及属性降维是三种比较重要的预处理方式。由于高速公路建设过程充斥着主观、客观、定性、定量等众多类型指标值,不同智能算法对数据有不同的要求,通过实际案例分析数据预处理对结果的影响程度,从而证明其在安全决策分析中的重要性。
(3)基于案例推理是参考相似项目经验的类比推理技术,高速公路项目由于自身特点导致其事故发生率高于一般的建设项目,再加上我国积累的施工资料相当匮乏,面临的问题又多为非结构化形式,使得安全决策更多地依赖于主观经验,然而受制于专家的知识背景,往往会出现决策偏差,导致前期风险预测不足,施工风险监控不力,事故应急启动慢、预案死板等问题。为提高这种非程式化安全决策问题的有效性,理论证明最佳解决方式是保留,基于此背景和理论铺垫,强调基于案例推理技术是解决目前安全决策问题的最佳方式,也是实现安全管理工作方式转变的基础,并结合高速公路项目的数据特点及安全管理工作要求,将基于案例推理中的案例表示和案例检索这两项关键技术落实到实处,建立适合高速公路建设风险预测、预警和事故应急等安全工作的智能管理框架。
(4)根据安全风险管理的目的,将案例推理的数据库设置为五大模块进行风险预测、预警及事故应急等,分别为基于项目特征的风险预测、基于项目风险的事故预测、基于安全事故的风险致因查询、基于风险水平的预控方案优选和基于事故水平的应急预案优选。在实现过程中,利用案例推理技术储存相关数据,建立相应的检索机制,并统计挖掘风险—事故关联规则；利用可变模糊聚类模型对已有项目特征、风险特征及事故特征进行聚类；利用支持向量机算法对新建项目风险水平及事故水平进行分类识别；利用可变模糊质变量变判据模式对施工过程中的动态风险指标进行预警监控；利用可变模糊优选模型对风险预控方案及事故应急预案进行多目标优选。
(5)结合案例库的五大模块及安全决策需求,设计并建立了C/S系统,以VB为界面平台,Access数据库为基础,Matlab为数学编程语言,解决了VB、Access和Matlab的技术互动问题,共同实现相似度检索、支持向量机模式识别和可变模糊集系列算法的功能及计算结果可视化,较为直观地为安全管理提供决策依据。
Currently, the number of accidents, the loss of life and injury in the construction are the second greatest after mining. As a high-risk sub-sector, the safety situation of highway was extremely severe while it rapidly expanded during the "Eleventh Five-Year" period. And the severe losses have badly hurt the healthy of highway construction. Therefore, the Government issued a series of macro security notices in order to strengthen supervision, prevent accidents and improve emergency response capabilities, and the Government also required security management to achieve five changes. So the thesis focuses on some key technologies of highway construction safety risk management, and the following aspects are devoted to the main effects:
(1) Intelligence, networking and informatization are the future research trends of highway construction safety management. This article focuses on the intelligent application in construction site to solve practical problems. Combined with practical features and security status of highway projects, this article details the theory superiority and application feasibility of some intelligent algorithms such as case-based reasoning, rule mining, support vector machine and variable fuzzy sets, etc.
(2) Data preprocessing holds an important position in the artificial intelligence field, accounting for 60% of the entire process of intelligence. One of its main purposes is to provide data preparation for algorithm application, and the other is to prevent data sets unbalanced which might lead to errors. And data conversion, normalization and dimension reduction are the three important pretreatments. In the highway construction process, there are filled with many index types, such as subjective, objective, qualitative and quantitative indicators, on the other hand, different intelligent algorithms ask for different data types, so this article researches through actual cases study to prove the importance of data pretreatment in safety decision analysis.
(3) Case-based reasoning is an analogical reasoning technology reference to similar project experience. Due to its own characteristics, the accident rate of highway project is higher than the general construction projects, plus construction information accumulated of China is quite scarce, and most of the problems are unstructured forms, so that security decisions making relied more on subjective experience. However, subject to the experts' background knowledge, decision-making always has some bias, which leads to insufficient early risk prediction, poor risk control in construction, emergency start slow, and inflexible plans, etc. To improve the effectiveness of security decision-making to these non-programmable problems, the best solution in theory is to retain. So based on this background and the theoretical groundwork, this article emphasizes that case-based reasoning is the best way to solve current security decision-making problems, and it is also the basis to achieve security management change. Combined with the data features and security requirements management of highway project, this article puts case representation and case retrieval the two key technologies of case-based reasoning into practice, and establishes a security intelligent management framework for risk prediction, early warning and emergency response of highway construction.
(4) According to security risk management purposes, the database of case-based reasoning is set into five modules in this paper, in order to work on risk prediction, early warning and emergency response, etc. The whole workflow includes risk prediction based on project characterized, accident prediction based on project risk, risk causes query based on accidents, pre-control plans optimization based on risk levels and emergency response plans optimization based on accident levels. In the implementation process, this article uses case-based reasoning technology to store data, establish the appropriate retrieval mechanisms, and mine risks-accidents association rules in the finally; variable fuzzy weight model of subjective and objective to calculate indicators weight; variable fuzzy clustering model to cluster the existing project features, risk characteristics and accident characteristics; support vector machine algorithm to recognize risk level and accident level of the new project; quantitative and qualitative criterion model to monitor dynamic risk indicators in the construction process; variable fuzzy optimization model for multi-objective optimization of risk prevention plans and emergency response plans.
(5) Combined with the five modules of case-base and security decision-making requirements, this article designs a C/S system, which takes VB as the interface platform, Access database as the foundation and Matlab as the mathematics programming. The system has solved the technical interaction problems among VB, Access and Matlab, and also achieved the algorithms function and results visualization, such as similarity search, pattern recognition based on support vector machines and variable fuzzy sets. Based on that, this system also could intuitively provide decision-making basis for security management.
(1)智能化、网络化、信息化是未来高速公路建设安全管理的研究趋势,本文从解决实际问题的角度出发,着眼于智能方法在施工现场中的应用,切实结合高速公路项目特点及安全现状,选用基于案例推理、规则挖掘、支持向量机和可变模糊集等智能化算法,并详细阐述其理论优越性及应用可行性。
(2)数据预处理在人工智能中的地位举足轻重,占据整个智能化流程的60%,其主要目的一是为算法应用提供数据准备,二是防止数据集偏斜导致结果误差,其中,数据转换、归一化及属性降维是三种比较重要的预处理方式。由于高速公路建设过程充斥着主观、客观、定性、定量等众多类型指标值,不同智能算法对数据有不同的要求,通过实际案例分析数据预处理对结果的影响程度,从而证明其在安全决策分析中的重要性。
(3)基于案例推理是参考相似项目经验的类比推理技术,高速公路项目由于自身特点导致其事故发生率高于一般的建设项目,再加上我国积累的施工资料相当匮乏,面临的问题又多为非结构化形式,使得安全决策更多地依赖于主观经验,然而受制于专家的知识背景,往往会出现决策偏差,导致前期风险预测不足,施工风险监控不力,事故应急启动慢、预案死板等问题。为提高这种非程式化安全决策问题的有效性,理论证明最佳解决方式是保留,基于此背景和理论铺垫,强调基于案例推理技术是解决目前安全决策问题的最佳方式,也是实现安全管理工作方式转变的基础,并结合高速公路项目的数据特点及安全管理工作要求,将基于案例推理中的案例表示和案例检索这两项关键技术落实到实处,建立适合高速公路建设风险预测、预警和事故应急等安全工作的智能管理框架。
(4)根据安全风险管理的目的,将案例推理的数据库设置为五大模块进行风险预测、预警及事故应急等,分别为基于项目特征的风险预测、基于项目风险的事故预测、基于安全事故的风险致因查询、基于风险水平的预控方案优选和基于事故水平的应急预案优选。在实现过程中,利用案例推理技术储存相关数据,建立相应的检索机制,并统计挖掘风险—事故关联规则；利用可变模糊聚类模型对已有项目特征、风险特征及事故特征进行聚类；利用支持向量机算法对新建项目风险水平及事故水平进行分类识别；利用可变模糊质变量变判据模式对施工过程中的动态风险指标进行预警监控；利用可变模糊优选模型对风险预控方案及事故应急预案进行多目标优选。
(5)结合案例库的五大模块及安全决策需求,设计并建立了C/S系统,以VB为界面平台,Access数据库为基础,Matlab为数学编程语言,解决了VB、Access和Matlab的技术互动问题,共同实现相似度检索、支持向量机模式识别和可变模糊集系列算法的功能及计算结果可视化,较为直观地为安全管理提供决策依据。
Currently, the number of accidents, the loss of life and injury in the construction are the second greatest after mining. As a high-risk sub-sector, the safety situation of highway was extremely severe while it rapidly expanded during the "Eleventh Five-Year" period. And the severe losses have badly hurt the healthy of highway construction. Therefore, the Government issued a series of macro security notices in order to strengthen supervision, prevent accidents and improve emergency response capabilities, and the Government also required security management to achieve five changes. So the thesis focuses on some key technologies of highway construction safety risk management, and the following aspects are devoted to the main effects:
(1) Intelligence, networking and informatization are the future research trends of highway construction safety management. This article focuses on the intelligent application in construction site to solve practical problems. Combined with practical features and security status of highway projects, this article details the theory superiority and application feasibility of some intelligent algorithms such as case-based reasoning, rule mining, support vector machine and variable fuzzy sets, etc.
(2) Data preprocessing holds an important position in the artificial intelligence field, accounting for 60% of the entire process of intelligence. One of its main purposes is to provide data preparation for algorithm application, and the other is to prevent data sets unbalanced which might lead to errors. And data conversion, normalization and dimension reduction are the three important pretreatments. In the highway construction process, there are filled with many index types, such as subjective, objective, qualitative and quantitative indicators, on the other hand, different intelligent algorithms ask for different data types, so this article researches through actual cases study to prove the importance of data pretreatment in safety decision analysis.
(3) Case-based reasoning is an analogical reasoning technology reference to similar project experience. Due to its own characteristics, the accident rate of highway project is higher than the general construction projects, plus construction information accumulated of China is quite scarce, and most of the problems are unstructured forms, so that security decisions making relied more on subjective experience. However, subject to the experts' background knowledge, decision-making always has some bias, which leads to insufficient early risk prediction, poor risk control in construction, emergency start slow, and inflexible plans, etc. To improve the effectiveness of security decision-making to these non-programmable problems, the best solution in theory is to retain. So based on this background and the theoretical groundwork, this article emphasizes that case-based reasoning is the best way to solve current security decision-making problems, and it is also the basis to achieve security management change. Combined with the data features and security requirements management of highway project, this article puts case representation and case retrieval the two key technologies of case-based reasoning into practice, and establishes a security intelligent management framework for risk prediction, early warning and emergency response of highway construction.
(4) According to security risk management purposes, the database of case-based reasoning is set into five modules in this paper, in order to work on risk prediction, early warning and emergency response, etc. The whole workflow includes risk prediction based on project characterized, accident prediction based on project risk, risk causes query based on accidents, pre-control plans optimization based on risk levels and emergency response plans optimization based on accident levels. In the implementation process, this article uses case-based reasoning technology to store data, establish the appropriate retrieval mechanisms, and mine risks-accidents association rules in the finally; variable fuzzy weight model of subjective and objective to calculate indicators weight; variable fuzzy clustering model to cluster the existing project features, risk characteristics and accident characteristics; support vector machine algorithm to recognize risk level and accident level of the new project; quantitative and qualitative criterion model to monitor dynamic risk indicators in the construction process; variable fuzzy optimization model for multi-objective optimization of risk prevention plans and emergency response plans.
(5) Combined with the five modules of case-base and security decision-making requirements, this article designs a C/S system, which takes VB as the interface platform, Access database as the foundation and Matlab as the mathematics programming. The system has solved the technical interaction problems among VB, Access and Matlab, and also achieved the algorithms function and results visualization, such as similarity search, pattern recognition based on support vector machines and variable fuzzy sets. Based on that, this system also could intuitively provide decision-making basis for security management.
引文
[1]中华人民共和国交通部.2010年公路水路交通运输行业发展统计公报.2011,4.
[2]中国高速公路网.十一届全国人大四次会议公布“十二五”规划纲要草案—基本建成国家高速公路网.2011,3. http://www.cngaosu.com/a/2011/0307/78365.html.
[3]中国高速公路网.中国驶入“高速”时代：高速公路通车里程逼近世界第一.2011,5.http://www.cngaosu.com/a/2010/1109/71783.html.
[4]马海涛,杨润林,任智刚,张志增.高速公路建设工程安全预评价研究及工程应用.中国安全生产科学技术,2006,2(5)：71-75.
[5]刘占杰,董建强.建筑行业的安全评价.劳动保护.2005,(12)：86-87.
[6]加快推行现代工程管理全面提高公路建设管理水平——冯正霖副部长在全国公路建设座谈会上的讲话.交通标准化,2010,(22)：9-16.
[7]交通运输部工程质量监督局.关于切实抓好公路工程预防坍塌事故隐患治理工作的紧急通知.2011,6.
[8]交通运输部质监总站.关于开展公路桥梁和隧道工程施工安全风险评估试行工作的通知.2011,5.
[9]国务院安委会办公室.交通运输部开展预防桥梁隧道坍塌事故专项整治.2011,5.
[10]工人日报.工人日报：“天价”赔不起生命成本.2010,7. http://cs.xinhuanet.com /xwzx/15/xwxzt/100723/201007/t20100723_2522979.htm.
[11]国家安全生产应急救援指挥中心.国家安全监管总局办公厅关于加快建立安全生产应急预案与应急资源数据库的通知.2009,11.
[12]国务院安委会办公室.国务院安委会办公室关于贯彻落实国务院《通知》精神进一步加强安全生产应急救援体系建设的实施意见.2010.11.
[13]安全监管总局网站.全国安全生产应急管理工作会议提出--加快推进应急救援体系建设全面提高安全生产应急能力.2011.3.
[14]罗云.21世纪安全管理科学展望.中国安全科学学报,2000,10(1)：27-32.
[15]Rafiq M. Choudhry, Dongping Fang, Syed M. Ahmed. Safety Management in Construction: Best Practices in Hong Kong.2008,20(134):112-125.
[16]徐波,姚天玮.中德建筑施工安全之比较与思考.建筑经济,2002,(4)：16-19.
[17]方东平,黄新宇,Jimmie Hinze.工程建设安全管理.中国水利水电出版社,2005.
[18]方东平,黄新宇,黄志伟.建筑安全管理研究的现状与展望.安全与环境学报,2001,1(2)：25-31.
[19]Jesus M. De la Garza, Donn E. Hancher, Lisa Decker. Analysis of Safety Indicators In Construction, Journal of Constructing Engineering and Management,1998(8):312-314.
[20]Barrie D S and Paulson B C. Professional Construction Management:Including CM, Design-Construct and General Contracting. New York:McGraw-Hill Book Co.,1992.
[21]Paulson B C. Safety program for volunteer-based construction projects. In:Coble R, Hinze J and Haupt T C, eds. Construction Safety and Health Management. New Jersey: Prentice Hall,2000,1-22.
[22]Jimmie W Hinze. Construction Safety. New Jersey.Prentice Hall,1997.
[23]Jimmie W Hinze and Figone L A. Subcontractor safety as influenced by general contractors on small and medium sized projects. Source Document 38, Construction Industry Inst, Univ of Washington, Seattle,Wash,1988.
[24]Jimmie W Hinze. Incurring the Cost of Injuries Versus Investing in Safety. Construction and Health Management. New Jersey. Prentice Hall,2000, (12):15.
[25]Hinze J. Turnover New Workers and Safety. Journal of Construction Division,1978, 104(4):409-417.
[26]Hinze J, Pannullo J. Safety:Function of job Control. Journal of Construction Division,1978,104(2):241-249.
[27]Hinze J, Parker HW. Safety Productivity and Job Pressures. Journal of Construction Division,1978,104(1):27-34.
[28]Hinze J, Gordon F. Supervisor-worker Relationship Affects Injury Rate. Journal of Construction Division,1979,105(3):253-261.
[29]Jimmie Hinze, Geneva Giang. Factors associated with construction worker eye injuries. Safety Science,2008,46(4):634-645.
[30]Jochen Teizer, Ben S. Allread, Clare E. Fullerton, Jimmie Hinze. Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system. Automation in Construction,2010,19(5):630-640.
[31]Jimmie W. Hinze, Jochen Teizer. Visibility-related fatalities related to construction equipment. Safety Science,2011,49(5):709-718.
[32]Sawacha Edwin, Naoum Shamil, Fong Daniel. Factors affecting safety performance on
construction sites. International Journal of Project Management,1999,17(5):309-315.
[33]Siu Oi-ling, David R, Leung Tat-wing. Safety climate and safety Performance among construction workers in Hong Kong:The role of Psychological strains as mediators. Accident Analysis and Prevention,2004,36(3):359-366.
[34]Halperln Kopl M, McCann Miehael. An evaluation of scaffold safety at construction sites. Journal of Safety Research,2004,35(2):141-150.
[35]Gregory Carter and Simon D. Smith. Safety Hazard Identification on Construction Projects. Journal of Construction Engineering and Management,2006,132(2):197-205.
[36]http://www.bls.gov/opub/ted/2010/ted_20101208.htm.
[37]Theodore K Courney. Frequent, Costly Disabling Construction Injuries. Proceedings of China International Forum on Work Safety. OIL, SAWS, CMIG USA,2002, (10):500.
[38]David Arditi, Dong-Eun Lee, Gul Polat. Fatal accidents in nighttime vs. daytime highway construction work zones. Journal of Safety Research,2007,38(4):399-405.
[39]张水平,朱建平,朱建华.建筑施工安全管理信息系统的研究和发展趋势.山西建筑,2007,(8)：11-12.
[40]Jochen Teizer, Manu Venugopal, Anupreet Walia. Ultrawideband for Automated Real-Time Three-Dimensional Location Sensing for Workforce, Equipment, and Material Positioning and Tracking. Transportation Research Record:Journal of the Transportation Research Board,2008,2081:56-64.
[41]Reza Azimi, SangHyun Lee,Simaan M. AbouRizk, Amin Alvanchi. A framework for an automated and integrated project monitoring and control system for steel fabrication projects. Automation in Construction,2011,(20):88-97.
[42]Ronie Navon, Rafael Sacks. Assessing research issues in Automated Project Performance Control (APPC). Automation in Construction,2007, (16):474-484.
[43]Shen Xuesong, CHEN Wu, LU Ming. Wireless Sensor Networks for Resources Tracking at Building Construction Sites. Tsinghua science and technology,2008,13(S1):78-83.
[44]D. K. H. Chua, M. ASCE, and Y. M. Goh. Application of Case Based Reasoning in Construction Safety Planning. Computer in civil engineering,2002:298-307.
[45]Y. M. Gohand D. K. H. Chua, M. ASCE. Case-Based Reasoning Approach to Construction Safety Hazard Identification-- Adaptation and Utilization. Journal of construction engineering and management,2010,136(2):170-178.
[46]M. Hirokane, A. Miyamoto, H. Kon is hi, Y. Fujioka. XML based data base system for accident cases in construction of pre-stressed conerete bridges. First International Structural Engineering and Construction Conference, Honolulu, Hawii,2001.
[47]J. P. Zhang, Z. Z. Hu. BIM and 4D-based integrated solution of analysis and management for conflicts and structural safety problems during construction:1. Principles and methodologies. Automation in Construction,2011,20(2):155-166.
[48]B. H. W. Hadikusumo, Steve Rowlinson. Integration of virtually real construction model and design for safety progress data base. Automation in Construction,2002,15(11): 501-509.
[49]黄吉欣,丁传波.香港特区建筑安全的过去和现状.建筑经济,2003,(3)：34-37.
[50]田元福,李慧民.建筑业的过度竞争及其原因分析.建筑经济,2002,(242)：10-12.
[51]田元福,李慧民.我国建筑安全管理的现状及思考.中国安全科学学报,2003,13(12)：13-17.
[52]黄吉欣,丁传波,麦鸿骥,刘家强,方东平.香港特区建筑安全的过去和现状,建筑经济,2003,(245)：34-37.
[53]方东平,黄吉欣,麦鸿骥.香港特区建筑安全管理的探讨与借鉴.中国安全科学学报,2003,13(5)：13-16.
[54]赵挺生,卢学伟,方东平.建筑施工伤害事故诱因调查统计分析.施工技术,2003,32(12)：54-55.
[55]方东平,黄新宇,李晓东,黄志伟.建筑业安全事故经济损失研究.建筑经济,2000,(3)：13-16.
[56]强茂山,方东平,肖红萍,陈洋.建设工程项目的安全投入与绩效研究.土木工程学报,2004, 37(11)：101-107.
[57]邓小林,李鸿尤,赵松基,魏伟彬.建筑工地事故的经济损失与最有效的安全投资(香港经验).中国安全科学学报,1995,5(1)：1-5.
[58]建设部.2007年度全国建筑施工安全生产形势分析报告.2008.3.
[59]建设部.2004-2006年度全国建筑施工安全生产形势分析报告.
[60]许程洁.基于事故理论的建筑施工项目安全管理研究：(博士学位论文).哈尔滨：哈尔滨工业大学,2008.
[61]唐晓军.灰色关联分析在建筑安全中的应用.建筑科学,2011,(2)：37-39.
[62]夏立明,宗恒恒,陈树平.公路施工安全事故的影响因素重要度评价实证研究.中国安全科学学报,2011,21(4)：83-90.
[63]胡树华,张冀新,佘廉.高速公路建设灾害预警管理研究.统计与决策,2007,(22)：144-146.
[64]杨德钦,基于工效学的施工危险源识别模型.经济建筑,2006,287(6)：37-39.
[65]罗云,宫运华,宫宝霖,解增武,孙晓伟.安全风险预警技术研究.安全,2005,(2)：26-29.
[66]江宽,黄永祥,张保敏.建筑安全事故的灰色预测.重庆建筑,2006,(1)：45-46.
[67]卫赵斌,朱桂荣,马楠.基于模糊评价法的建筑安全事故预测模型.中国安全生产科学技术,2005,(6)：55-57.
[68]卢岚,王令动,卞立平,秦嵩.建筑施工现场安全模糊评价方法研究.工业工程,2003,6(6)：49-53.
[69]毛健.企业安全管理信息系统与安全评价：(硕士学位论文).西安：西安交通大学,2001.
[70]唐天国,刘浩吾,雷运波.工程事故影响因素的灰色关联分析.水力发电,2003,(11)：57-59.
[71]张双明,许程洁.建筑企业安全投入与效益关系研究.工程管理学报,2010,24(6)：700-703.
[72]陆福春,张洪尧,张兴才.故障树分析技术在建筑施工安全管理中的应用.沙洲职业工学院学报,2006,9(2)：4-6.
[73]王岗.建设工程施工安全监理研究：(硕士学位论文).武汉：华中科技大学,2006.
[74]贺耀荣,汪莉,李祥,宋存义.基于BP网络的建筑安装施工现场安全综合评价的研究.中国安全科学学报,2004,14(12)：100-103.
[75]杜年春.地铁施工监测信息信息管理及安全预警系统研究.工程建设,2006,38(1)：37-40.
[76]邓宝,宋瑞.基于BP神经网络的安全评价方法研究.安全与环境工程,2005,12(6)：61-64.
[77]孟祥海,李梅,麦强,关志强.高速公路事故多发点鉴别及诱发因素识别.交通运输系统工程与信息,2011,11(1)：114-120.
[78]朱蕊.基于粗糙集与神经网络的建筑安全预测研究：(硕士学位论文).天津：天津财经大学.2008.
[79]马楠,张立宁.基于粗糙集—粒子群神经网络的建设项目安全预测研究.中国安全科学学报,2007,17(4)：36-42.
[80]李书全,窦艳杰.基于RS-SVM模型的建筑安全事故预测模型.经济与决策,2008,19(271)：56-58
[81]裴晓丽.基于多源信息触合的建筑施工安全管理研究：(硕士学位论文).西安：西安建筑科技大学,2010.
[82]Zhenzhong Hu, Jianping Zhang, Ziyin Deng. Construction Process Simulation and Safety Analysis Based on Building Information Model and 4D Technology. Tsinghua Science & Technology,2008,13(1):266-272.
[83]Sunkyu Woo, Seongsu Jeong, Esmond Mok, Linyuan Xia, Changsu Choi, Muwook Pyeon, Joon Heo. Application of WiFi-based indoor positioning system for labor tracking at construction sites:A case study in Guangzhou MTR. Automation in Construction,2011,20(1): 3-13.
[84]Roger C. Schank. Dynamic Memory:A Theory Of Reminding And Learning In Computers And People. Cambridge University Press,1983.
[85]Mount, C., and Liao, T. W.. Prototype of an intelligent failure analysis system. ICCBR 2001, LNAI 2080, D. W. Aha and I. Watson, eds., Springer, Berlin Heidelberg,2001, 716-730.
[86]Smyth B, Keane MT. Remembering To Forget:A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems. International Joint Conference on Artificial Intelligence (IJCAI),1995,377-383.
[87]陈文伟,黄金才.数据仓库与数据挖掘.北京：人民邮电出版社,2009.
[88]王万森.人工智能原理及其应用.北京：电子工业出版社,2000.
[89]Grahaml.面向对象的方法：原理与实践.北京：机械工业出版社,2003.
[90]曹元大,徐漫江.面向对象知识表示在专家系统开发工具中的应用.北京理工大学学报,2000,20(6)：688-692.
[91]钱梅,汪斌,吴耿锋.面向对象模型对复杂结构知识的知识表示方法.计算机工程与科学,2002,24(3)：62-65.
[92]耿焕同,方瑾,蔡庆生.范例推理中面向对象的范例表示研究.计算机科学,2006,33(3)：165-178.
[93]倪志伟,李锋刚,毛雪崛.智能管理技术与方法.北京：科学出版社,2007
[94]Jui-Sheng Chou. Web-based CBR System Applied to Early Cost Budgeting for Pavement Maintenance Project. Expert Systems with Applications,2009,36:2947-2960.
[95]Agrawal R, Imielinski T, Swami A. Mining Association Rules between Sets of Items in large Databased. Proceedings of the ACM SIGMOD Conference on Management of Data,1993: 207-216.
[96]蔡伟杰,张晓辉,朱建秋,朱扬勇.关联规则挖掘综述.计算机工程,2001,27(5)：31-33.
[97]Pawlak Z. Rough set—Theoretical aspects of reasoning about data. London:Kluwer Academic Publishers,1991.
[98]王亚英,邵惠鹤.基于粗集理论的规则知识获取技术.上海交通大学学报,2000,34(5)：688-690.
[99]Vladimir N. Vapnik. Statistical Learning Theory. Publishing House of Electronics Industry, Beijing,2004.
[100]张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1)：32-42.
[101]Vapnik V N. The Nature of Statistical Learning Theory, NY:Springer-Verlag,1995.
[102]Cortes C, Vapnik V. Support-vector networks. Machine Learning,1995,20:273-297.
[103]Klaus-Robert Muller, Sebastian Mika, Gunnar Ratsch, et al. An Introduction to Kernel-Based Learning Algorithms, IEEE Transactions on Neural Networks,2001,12(2): 181-201.
[104]Stefan R. SVM Kernels for Time Series Analysis. http://www-ai.cs.uni-dortmund. de/DOKUMENTE/rueping_2001a. pdf.
[105]Baesens B, Viaene S, Van Gestel, et al. An empirical Assessment of Kernel Type Performance for Least Squares Support Vector Machine Classifiers, Fourth international conference on knowledge-based intelligent engineering systems & allied technologies, Brighton, UK,2000:313-316.
[106]L. A. Zadeh. Fuzzy sets. Information and Control,1965,8(3):338-353.
[107]陈守煜.可变模糊集合理论与可变模型集.数学的实践与认识,2008,38(18)：146-153.
[108]陈守煜,袁晶瑄,李亚伟.基于可变模糊集的模糊聚类迭代模型及其应用.大连理工大学学报,2008,48(6)：882-886
[109]陈守煜,李敏,王淑英.可变模糊聚类迭代模型合理性分析与应用检验.大连理工大学学报,2009,49(6)：932-936.
[110]陈守煜.系统模糊决策理论与应用.大连：大连理工大学出版社,1994.
[111]BEZDEK L C. Physical interpretation of fuzzy ISODATA. IEEE Transactions on Systems, Man and Cybernetics,1976, SMC-6(5):387-389
[112]陈守煜.多目标决策系统模糊优选理论、模型与方法.华北水利水电学院学报,2001,22(3)：136-140.
[113]陈守煜.工程模糊集理论与应用.北京：国防工业出版社,1998.
[114]陈守煜.防洪调度多目标决策理论与模型.中国工程科学,2000,2(2)：47-52.
[115]陈守煜.工程可变模糊集理论与模型—模糊水文水资源学数学基础.大连理工大学学报,2005,45(2)：308-312.
[116]陈守煜.可变模糊集理论哲学基础.大连理工大学学报(社会科学版),2005,26(1)：53-57.
[117]陈守煜.可变模糊集量变与质变判据模式及其应用.系统工程与电子技术,2008,30(10)：1879-1882.
[118]侯利娟,王国胤,聂能,吴渝.粗糙集理论中的离散化问题.计算机科学,2000,27(12)：89-94.
[119]刘凌霞.基于粗糙集理论属性重要性的离散化算法.广西轻工业,2007,(10)：75-76.
[120]张建军,张静波.一种新的基于粗糙集理论的决策表离散化算法.西安电子科技大学学报(自然科学版),2004,31(3)：469-472.
[121]孟祥萍,鞠传香,王贤勇,任纪川.粗糙集理论中基于属性重要性的离散化方法.东北电力学院学报,2005,25(1)：40-43.
[122]谢宏,程浩忠,牛东晓.基于信息熵的粗糙集连续属性离散化算法.计算机学报,2005,28(9)：1570-1574.
[123]李靖华,郭耀煌.主成分分析用于多指标评价的方法研究—主成分评价.管理工程学报,2002,16(1)：39-43.
[124]张文修,仇国芳.粗糙集属性约简的一般理论.中国科学E辑信息科学2005,35(12)：1304-1313.
[125]国家安全生产监督管理总局.国家安全监管总局关于印发2010年度安全生产重大事故防治关键技术重点科技项目的通知.2010,6.
[126]Wang Yingming. On fuzzy multiattribute decision-making models and methods with incomplete preference information. Fuzzy Sets and Systems,2005,151(2):285-301.
[127]Li Dengfeng. Fuzzy multi attribute decision-making models and methods with incomplete preference information. Fuzzy Sets and Systems,1999,106(2):113-119.
[128]仲秋雁,郭素,叶鑫,张英菊.应急辅助决策中案例表示与检索方法研究.大连理工大学学报.2011,51(1)：137-142.
[129]蒋占四,陈立平,罗年猛.最近邻实例检索相似度分析.计算机集成制造系统,2007,13(6)：1165-1168.
[130]中华人民共和国住房和城乡建设部.2010年房屋市政工程生产安全事故情况通报.2011,3.
[131]Patrick X. W. Zou, Guomin Zhang, Jiayuan Wang. Understanding the key risks in construction projects in China.2007,25:601-614
[132]赵登福,张小东,夏经德.基于事例推理短期负荷预测方法的改进.西安交通大学学报,2006,40(8)：960-963.
[133]折学森.软土地基沉降计算.北京：人民交通出版社,1998.
[134]中国高速公路网.公路水路建设安全预警研究填补国内空白.2011,3.
[135]中国建设报.逐步完善建筑业伤亡事故统计指标体系.2006,9.
[136]中华人民共和国住房和城乡建设部工程质量安全监管司.关于印发王树平同志在全国建筑安全生产联络员第十四次会议上的讲话的通知,2011,4.
[137]周高平.基础设施项目投资风险预警阀值研究.公路,2006,2：100-103.
[138]陈守煜,袁晶瑄.可变模糊模型及论可拓方法用于水科学的错误.水资源保护,2007,23(6)：1-6.
[139]Seung H. Han, Du Y. Kim, Hyungkwan Kim, Won-Suk Jang. A web-based integrated system for international project risk management. Automation in Construction.2008,17:342-356.
[140]赫伯特.A.西蒙.管理决策新科学.北京：中国社会科学出版社,1982.
[141]刘成君,吴继敏,丁向东.用人工神经网络评价边坡稳定性.西部探矿工程,2003,81(2)：44-45.
[142]付义祥,刘世凯,刘大鹏.RBF神经网络在边坡岩体稳定性中的预测研究.武汉理工大学学报,
2003,27(2)：170-173.
[143]N. K. Sah, P.R. Sheorey, L. N. Upadhyaya. Maximum likelihood cstimation of slope stability. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts,1994,31 (1):47-54.
[144]谭炎,张凌燕.MATLAB与VB混合编程技术研究.软件时空,2005,247-249.
[145]王跃强,王纪龙,王云才.VB程序中实现调用MATLAB的方法.计算机应用,2001,21(2)：95-96.
[146]安子良,孔令超,肖立中.基于VB、Matlab和Access的石油井架应力测量系统.化工自动化及仪表,2010,38(5)：597-600.
[2]中国高速公路网.十一届全国人大四次会议公布“十二五”规划纲要草案—基本建成国家高速公路网.2011,3. http://www.cngaosu.com/a/2011/0307/78365.html.
[3]中国高速公路网.中国驶入“高速”时代：高速公路通车里程逼近世界第一.2011,5.http://www.cngaosu.com/a/2010/1109/71783.html.
[4]马海涛,杨润林,任智刚,张志增.高速公路建设工程安全预评价研究及工程应用.中国安全生产科学技术,2006,2(5)：71-75.
[5]刘占杰,董建强.建筑行业的安全评价.劳动保护.2005,(12)：86-87.
[6]加快推行现代工程管理全面提高公路建设管理水平——冯正霖副部长在全国公路建设座谈会上的讲话.交通标准化,2010,(22)：9-16.
[7]交通运输部工程质量监督局.关于切实抓好公路工程预防坍塌事故隐患治理工作的紧急通知.2011,6.
[8]交通运输部质监总站.关于开展公路桥梁和隧道工程施工安全风险评估试行工作的通知.2011,5.
[9]国务院安委会办公室.交通运输部开展预防桥梁隧道坍塌事故专项整治.2011,5.
[10]工人日报.工人日报：“天价”赔不起生命成本.2010,7. http://cs.xinhuanet.com /xwzx/15/xwxzt/100723/201007/t20100723_2522979.htm.
[11]国家安全生产应急救援指挥中心.国家安全监管总局办公厅关于加快建立安全生产应急预案与应急资源数据库的通知.2009,11.
[12]国务院安委会办公室.国务院安委会办公室关于贯彻落实国务院《通知》精神进一步加强安全生产应急救援体系建设的实施意见.2010.11.
[13]安全监管总局网站.全国安全生产应急管理工作会议提出--加快推进应急救援体系建设全面提高安全生产应急能力.2011.3.
[14]罗云.21世纪安全管理科学展望.中国安全科学学报,2000,10(1)：27-32.
[15]Rafiq M. Choudhry, Dongping Fang, Syed M. Ahmed. Safety Management in Construction: Best Practices in Hong Kong.2008,20(134):112-125.
[16]徐波,姚天玮.中德建筑施工安全之比较与思考.建筑经济,2002,(4)：16-19.
[17]方东平,黄新宇,Jimmie Hinze.工程建设安全管理.中国水利水电出版社,2005.
[18]方东平,黄新宇,黄志伟.建筑安全管理研究的现状与展望.安全与环境学报,2001,1(2)：25-31.
[19]Jesus M. De la Garza, Donn E. Hancher, Lisa Decker. Analysis of Safety Indicators In Construction, Journal of Constructing Engineering and Management,1998(8):312-314.
[20]Barrie D S and Paulson B C. Professional Construction Management:Including CM, Design-Construct and General Contracting. New York:McGraw-Hill Book Co.,1992.
[21]Paulson B C. Safety program for volunteer-based construction projects. In:Coble R, Hinze J and Haupt T C, eds. Construction Safety and Health Management. New Jersey: Prentice Hall,2000,1-22.
[22]Jimmie W Hinze. Construction Safety. New Jersey.Prentice Hall,1997.
[23]Jimmie W Hinze and Figone L A. Subcontractor safety as influenced by general contractors on small and medium sized projects. Source Document 38, Construction Industry Inst, Univ of Washington, Seattle,Wash,1988.
[24]Jimmie W Hinze. Incurring the Cost of Injuries Versus Investing in Safety. Construction and Health Management. New Jersey. Prentice Hall,2000, (12):15.
[25]Hinze J. Turnover New Workers and Safety. Journal of Construction Division,1978, 104(4):409-417.
[26]Hinze J, Pannullo J. Safety:Function of job Control. Journal of Construction Division,1978,104(2):241-249.
[27]Hinze J, Parker HW. Safety Productivity and Job Pressures. Journal of Construction Division,1978,104(1):27-34.
[28]Hinze J, Gordon F. Supervisor-worker Relationship Affects Injury Rate. Journal of Construction Division,1979,105(3):253-261.
[29]Jimmie Hinze, Geneva Giang. Factors associated with construction worker eye injuries. Safety Science,2008,46(4):634-645.
[30]Jochen Teizer, Ben S. Allread, Clare E. Fullerton, Jimmie Hinze. Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system. Automation in Construction,2010,19(5):630-640.
[31]Jimmie W. Hinze, Jochen Teizer. Visibility-related fatalities related to construction equipment. Safety Science,2011,49(5):709-718.
[32]Sawacha Edwin, Naoum Shamil, Fong Daniel. Factors affecting safety performance on
construction sites. International Journal of Project Management,1999,17(5):309-315.
[33]Siu Oi-ling, David R, Leung Tat-wing. Safety climate and safety Performance among construction workers in Hong Kong:The role of Psychological strains as mediators. Accident Analysis and Prevention,2004,36(3):359-366.
[34]Halperln Kopl M, McCann Miehael. An evaluation of scaffold safety at construction sites. Journal of Safety Research,2004,35(2):141-150.
[35]Gregory Carter and Simon D. Smith. Safety Hazard Identification on Construction Projects. Journal of Construction Engineering and Management,2006,132(2):197-205.
[36]http://www.bls.gov/opub/ted/2010/ted_20101208.htm.
[37]Theodore K Courney. Frequent, Costly Disabling Construction Injuries. Proceedings of China International Forum on Work Safety. OIL, SAWS, CMIG USA,2002, (10):500.
[38]David Arditi, Dong-Eun Lee, Gul Polat. Fatal accidents in nighttime vs. daytime highway construction work zones. Journal of Safety Research,2007,38(4):399-405.
[39]张水平,朱建平,朱建华.建筑施工安全管理信息系统的研究和发展趋势.山西建筑,2007,(8)：11-12.
[40]Jochen Teizer, Manu Venugopal, Anupreet Walia. Ultrawideband for Automated Real-Time Three-Dimensional Location Sensing for Workforce, Equipment, and Material Positioning and Tracking. Transportation Research Record:Journal of the Transportation Research Board,2008,2081:56-64.
[41]Reza Azimi, SangHyun Lee,Simaan M. AbouRizk, Amin Alvanchi. A framework for an automated and integrated project monitoring and control system for steel fabrication projects. Automation in Construction,2011,(20):88-97.
[42]Ronie Navon, Rafael Sacks. Assessing research issues in Automated Project Performance Control (APPC). Automation in Construction,2007, (16):474-484.
[43]Shen Xuesong, CHEN Wu, LU Ming. Wireless Sensor Networks for Resources Tracking at Building Construction Sites. Tsinghua science and technology,2008,13(S1):78-83.
[44]D. K. H. Chua, M. ASCE, and Y. M. Goh. Application of Case Based Reasoning in Construction Safety Planning. Computer in civil engineering,2002:298-307.
[45]Y. M. Gohand D. K. H. Chua, M. ASCE. Case-Based Reasoning Approach to Construction Safety Hazard Identification-- Adaptation and Utilization. Journal of construction engineering and management,2010,136(2):170-178.
[46]M. Hirokane, A. Miyamoto, H. Kon is hi, Y. Fujioka. XML based data base system for accident cases in construction of pre-stressed conerete bridges. First International Structural Engineering and Construction Conference, Honolulu, Hawii,2001.
[47]J. P. Zhang, Z. Z. Hu. BIM and 4D-based integrated solution of analysis and management for conflicts and structural safety problems during construction:1. Principles and methodologies. Automation in Construction,2011,20(2):155-166.
[48]B. H. W. Hadikusumo, Steve Rowlinson. Integration of virtually real construction model and design for safety progress data base. Automation in Construction,2002,15(11): 501-509.
[49]黄吉欣,丁传波.香港特区建筑安全的过去和现状.建筑经济,2003,(3)：34-37.
[50]田元福,李慧民.建筑业的过度竞争及其原因分析.建筑经济,2002,(242)：10-12.
[51]田元福,李慧民.我国建筑安全管理的现状及思考.中国安全科学学报,2003,13(12)：13-17.
[52]黄吉欣,丁传波,麦鸿骥,刘家强,方东平.香港特区建筑安全的过去和现状,建筑经济,2003,(245)：34-37.
[53]方东平,黄吉欣,麦鸿骥.香港特区建筑安全管理的探讨与借鉴.中国安全科学学报,2003,13(5)：13-16.
[54]赵挺生,卢学伟,方东平.建筑施工伤害事故诱因调查统计分析.施工技术,2003,32(12)：54-55.
[55]方东平,黄新宇,李晓东,黄志伟.建筑业安全事故经济损失研究.建筑经济,2000,(3)：13-16.
[56]强茂山,方东平,肖红萍,陈洋.建设工程项目的安全投入与绩效研究.土木工程学报,2004, 37(11)：101-107.
[57]邓小林,李鸿尤,赵松基,魏伟彬.建筑工地事故的经济损失与最有效的安全投资(香港经验).中国安全科学学报,1995,5(1)：1-5.
[58]建设部.2007年度全国建筑施工安全生产形势分析报告.2008.3.
[59]建设部.2004-2006年度全国建筑施工安全生产形势分析报告.
[60]许程洁.基于事故理论的建筑施工项目安全管理研究：(博士学位论文).哈尔滨：哈尔滨工业大学,2008.
[61]唐晓军.灰色关联分析在建筑安全中的应用.建筑科学,2011,(2)：37-39.
[62]夏立明,宗恒恒,陈树平.公路施工安全事故的影响因素重要度评价实证研究.中国安全科学学报,2011,21(4)：83-90.
[63]胡树华,张冀新,佘廉.高速公路建设灾害预警管理研究.统计与决策,2007,(22)：144-146.
[64]杨德钦,基于工效学的施工危险源识别模型.经济建筑,2006,287(6)：37-39.
[65]罗云,宫运华,宫宝霖,解增武,孙晓伟.安全风险预警技术研究.安全,2005,(2)：26-29.
[66]江宽,黄永祥,张保敏.建筑安全事故的灰色预测.重庆建筑,2006,(1)：45-46.
[67]卫赵斌,朱桂荣,马楠.基于模糊评价法的建筑安全事故预测模型.中国安全生产科学技术,2005,(6)：55-57.
[68]卢岚,王令动,卞立平,秦嵩.建筑施工现场安全模糊评价方法研究.工业工程,2003,6(6)：49-53.
[69]毛健.企业安全管理信息系统与安全评价：(硕士学位论文).西安：西安交通大学,2001.
[70]唐天国,刘浩吾,雷运波.工程事故影响因素的灰色关联分析.水力发电,2003,(11)：57-59.
[71]张双明,许程洁.建筑企业安全投入与效益关系研究.工程管理学报,2010,24(6)：700-703.
[72]陆福春,张洪尧,张兴才.故障树分析技术在建筑施工安全管理中的应用.沙洲职业工学院学报,2006,9(2)：4-6.
[73]王岗.建设工程施工安全监理研究：(硕士学位论文).武汉：华中科技大学,2006.
[74]贺耀荣,汪莉,李祥,宋存义.基于BP网络的建筑安装施工现场安全综合评价的研究.中国安全科学学报,2004,14(12)：100-103.
[75]杜年春.地铁施工监测信息信息管理及安全预警系统研究.工程建设,2006,38(1)：37-40.
[76]邓宝,宋瑞.基于BP神经网络的安全评价方法研究.安全与环境工程,2005,12(6)：61-64.
[77]孟祥海,李梅,麦强,关志强.高速公路事故多发点鉴别及诱发因素识别.交通运输系统工程与信息,2011,11(1)：114-120.
[78]朱蕊.基于粗糙集与神经网络的建筑安全预测研究：(硕士学位论文).天津：天津财经大学.2008.
[79]马楠,张立宁.基于粗糙集—粒子群神经网络的建设项目安全预测研究.中国安全科学学报,2007,17(4)：36-42.
[80]李书全,窦艳杰.基于RS-SVM模型的建筑安全事故预测模型.经济与决策,2008,19(271)：56-58
[81]裴晓丽.基于多源信息触合的建筑施工安全管理研究：(硕士学位论文).西安：西安建筑科技大学,2010.
[82]Zhenzhong Hu, Jianping Zhang, Ziyin Deng. Construction Process Simulation and Safety Analysis Based on Building Information Model and 4D Technology. Tsinghua Science & Technology,2008,13(1):266-272.
[83]Sunkyu Woo, Seongsu Jeong, Esmond Mok, Linyuan Xia, Changsu Choi, Muwook Pyeon, Joon Heo. Application of WiFi-based indoor positioning system for labor tracking at construction sites:A case study in Guangzhou MTR. Automation in Construction,2011,20(1): 3-13.
[84]Roger C. Schank. Dynamic Memory:A Theory Of Reminding And Learning In Computers And People. Cambridge University Press,1983.
[85]Mount, C., and Liao, T. W.. Prototype of an intelligent failure analysis system. ICCBR 2001, LNAI 2080, D. W. Aha and I. Watson, eds., Springer, Berlin Heidelberg,2001, 716-730.
[86]Smyth B, Keane MT. Remembering To Forget:A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems. International Joint Conference on Artificial Intelligence (IJCAI),1995,377-383.
[87]陈文伟,黄金才.数据仓库与数据挖掘.北京：人民邮电出版社,2009.
[88]王万森.人工智能原理及其应用.北京：电子工业出版社,2000.
[89]Grahaml.面向对象的方法：原理与实践.北京：机械工业出版社,2003.
[90]曹元大,徐漫江.面向对象知识表示在专家系统开发工具中的应用.北京理工大学学报,2000,20(6)：688-692.
[91]钱梅,汪斌,吴耿锋.面向对象模型对复杂结构知识的知识表示方法.计算机工程与科学,2002,24(3)：62-65.
[92]耿焕同,方瑾,蔡庆生.范例推理中面向对象的范例表示研究.计算机科学,2006,33(3)：165-178.
[93]倪志伟,李锋刚,毛雪崛.智能管理技术与方法.北京：科学出版社,2007
[94]Jui-Sheng Chou. Web-based CBR System Applied to Early Cost Budgeting for Pavement Maintenance Project. Expert Systems with Applications,2009,36:2947-2960.
[95]Agrawal R, Imielinski T, Swami A. Mining Association Rules between Sets of Items in large Databased. Proceedings of the ACM SIGMOD Conference on Management of Data,1993: 207-216.
[96]蔡伟杰,张晓辉,朱建秋,朱扬勇.关联规则挖掘综述.计算机工程,2001,27(5)：31-33.
[97]Pawlak Z. Rough set—Theoretical aspects of reasoning about data. London:Kluwer Academic Publishers,1991.
[98]王亚英,邵惠鹤.基于粗集理论的规则知识获取技术.上海交通大学学报,2000,34(5)：688-690.
[99]Vladimir N. Vapnik. Statistical Learning Theory. Publishing House of Electronics Industry, Beijing,2004.
[100]张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1)：32-42.
[101]Vapnik V N. The Nature of Statistical Learning Theory, NY:Springer-Verlag,1995.
[102]Cortes C, Vapnik V. Support-vector networks. Machine Learning,1995,20:273-297.
[103]Klaus-Robert Muller, Sebastian Mika, Gunnar Ratsch, et al. An Introduction to Kernel-Based Learning Algorithms, IEEE Transactions on Neural Networks,2001,12(2): 181-201.
[104]Stefan R. SVM Kernels for Time Series Analysis. http://www-ai.cs.uni-dortmund. de/DOKUMENTE/rueping_2001a. pdf.
[105]Baesens B, Viaene S, Van Gestel, et al. An empirical Assessment of Kernel Type Performance for Least Squares Support Vector Machine Classifiers, Fourth international conference on knowledge-based intelligent engineering systems & allied technologies, Brighton, UK,2000:313-316.
[106]L. A. Zadeh. Fuzzy sets. Information and Control,1965,8(3):338-353.
[107]陈守煜.可变模糊集合理论与可变模型集.数学的实践与认识,2008,38(18)：146-153.
[108]陈守煜,袁晶瑄,李亚伟.基于可变模糊集的模糊聚类迭代模型及其应用.大连理工大学学报,2008,48(6)：882-886
[109]陈守煜,李敏,王淑英.可变模糊聚类迭代模型合理性分析与应用检验.大连理工大学学报,2009,49(6)：932-936.
[110]陈守煜.系统模糊决策理论与应用.大连：大连理工大学出版社,1994.
[111]BEZDEK L C. Physical interpretation of fuzzy ISODATA. IEEE Transactions on Systems, Man and Cybernetics,1976, SMC-6(5):387-389
[112]陈守煜.多目标决策系统模糊优选理论、模型与方法.华北水利水电学院学报,2001,22(3)：136-140.
[113]陈守煜.工程模糊集理论与应用.北京：国防工业出版社,1998.
[114]陈守煜.防洪调度多目标决策理论与模型.中国工程科学,2000,2(2)：47-52.
[115]陈守煜.工程可变模糊集理论与模型—模糊水文水资源学数学基础.大连理工大学学报,2005,45(2)：308-312.
[116]陈守煜.可变模糊集理论哲学基础.大连理工大学学报(社会科学版),2005,26(1)：53-57.
[117]陈守煜.可变模糊集量变与质变判据模式及其应用.系统工程与电子技术,2008,30(10)：1879-1882.
[118]侯利娟,王国胤,聂能,吴渝.粗糙集理论中的离散化问题.计算机科学,2000,27(12)：89-94.
[119]刘凌霞.基于粗糙集理论属性重要性的离散化算法.广西轻工业,2007,(10)：75-76.
[120]张建军,张静波.一种新的基于粗糙集理论的决策表离散化算法.西安电子科技大学学报(自然科学版),2004,31(3)：469-472.
[121]孟祥萍,鞠传香,王贤勇,任纪川.粗糙集理论中基于属性重要性的离散化方法.东北电力学院学报,2005,25(1)：40-43.
[122]谢宏,程浩忠,牛东晓.基于信息熵的粗糙集连续属性离散化算法.计算机学报,2005,28(9)：1570-1574.
[123]李靖华,郭耀煌.主成分分析用于多指标评价的方法研究—主成分评价.管理工程学报,2002,16(1)：39-43.
[124]张文修,仇国芳.粗糙集属性约简的一般理论.中国科学E辑信息科学2005,35(12)：1304-1313.
[125]国家安全生产监督管理总局.国家安全监管总局关于印发2010年度安全生产重大事故防治关键技术重点科技项目的通知.2010,6.
[126]Wang Yingming. On fuzzy multiattribute decision-making models and methods with incomplete preference information. Fuzzy Sets and Systems,2005,151(2):285-301.
[127]Li Dengfeng. Fuzzy multi attribute decision-making models and methods with incomplete preference information. Fuzzy Sets and Systems,1999,106(2):113-119.
[128]仲秋雁,郭素,叶鑫,张英菊.应急辅助决策中案例表示与检索方法研究.大连理工大学学报.2011,51(1)：137-142.
[129]蒋占四,陈立平,罗年猛.最近邻实例检索相似度分析.计算机集成制造系统,2007,13(6)：1165-1168.
[130]中华人民共和国住房和城乡建设部.2010年房屋市政工程生产安全事故情况通报.2011,3.
[131]Patrick X. W. Zou, Guomin Zhang, Jiayuan Wang. Understanding the key risks in construction projects in China.2007,25:601-614
[132]赵登福,张小东,夏经德.基于事例推理短期负荷预测方法的改进.西安交通大学学报,2006,40(8)：960-963.
[133]折学森.软土地基沉降计算.北京：人民交通出版社,1998.
[134]中国高速公路网.公路水路建设安全预警研究填补国内空白.2011,3.
[135]中国建设报.逐步完善建筑业伤亡事故统计指标体系.2006,9.
[136]中华人民共和国住房和城乡建设部工程质量安全监管司.关于印发王树平同志在全国建筑安全生产联络员第十四次会议上的讲话的通知,2011,4.
[137]周高平.基础设施项目投资风险预警阀值研究.公路,2006,2：100-103.
[138]陈守煜,袁晶瑄.可变模糊模型及论可拓方法用于水科学的错误.水资源保护,2007,23(6)：1-6.
[139]Seung H. Han, Du Y. Kim, Hyungkwan Kim, Won-Suk Jang. A web-based integrated system for international project risk management. Automation in Construction.2008,17:342-356.
[140]赫伯特.A.西蒙.管理决策新科学.北京：中国社会科学出版社,1982.
[141]刘成君,吴继敏,丁向东.用人工神经网络评价边坡稳定性.西部探矿工程,2003,81(2)：44-45.
[142]付义祥,刘世凯,刘大鹏.RBF神经网络在边坡岩体稳定性中的预测研究.武汉理工大学学报,
2003,27(2)：170-173.
[143]N. K. Sah, P.R. Sheorey, L. N. Upadhyaya. Maximum likelihood cstimation of slope stability. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts,1994,31 (1):47-54.
[144]谭炎,张凌燕.MATLAB与VB混合编程技术研究.软件时空,2005,247-249.
[145]王跃强,王纪龙,王云才.VB程序中实现调用MATLAB的方法.计算机应用,2001,21(2)：95-96.
[146]安子良,孔令超,肖立中.基于VB、Matlab和Access的石油井架应力测量系统.化工自动化及仪表,2010,38(5)：597-600.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |