建设工程项目施工安全精细化评价研究
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摘要
建设工程项目的施工安全问题一直是困扰施工企业的最大难题,对施工安全的评价、预防和控制,是解决施工安全难题的重要手段,而能够对一个建设工程项目进行有效精确的安全评价,是保证施工企业进行安全生产的首要任务和工作重点。
     迄今为止,对一个建设工程项目进行施工安全评价有许多种科学有效的方法,但很少有从评价角度对施工安全危害中的四大伤害类型进行细致的研究。本文通过对安全生产管理制度、资质、机构与人员管理、安全技术管理、设备与设施管理的评价,得到对高处坠落、触电事故、物体打击、机械伤害各自不同的控制水平,能够针对不同危害进行有侧重的安全预防和安全控制。这就是精细化安全评价的目的,而整个评价过程,是通过施工安全精细化评价模型的计算机程序来实现的。
     本文的主要工作有:首先介绍了国内外安全评价的理论和方法,并根据国内建设工程项目的实际情况,提出了施工安全精细化评价的方法;然后建立了施工安全精细化评价的指标,总结出输入指标和输出指标的联系;接着利用故障树分析法计算出输入指标和输出指标的数学关系和权重;然后运用模糊综合评价法将输入指标数据转化为输出指标数据,并把对应输入数据和输出数据配对成样本数据;再然后通过BP人工神经网络构建了施工安全精细化评价模型;最后利用MATLAB语言编制实现评价模型的程序系统,并用收集到的数据通过程序系统进行实证研究。
The issue of construction project safety has been a biggest problem which troubled construction enterprises for a long time. The way of evaluation, prevention and control of construction safety is an important mean of solving the problem of security. So the effective and accurate safety evaluation to a construction project is the priority and focus of ensuring construction enterprises to production safety.
     So far, there are many scientific and effective ways of safety evaluation on a construction project, but few do meticulous research to the four types of injury in the construction from the perspective of the safety evaluation. By the evaluation of safe producing management institution, agencies accreditation and personnel management, security technology management, equipment and facilities management, the article get the control level of height fall, electrocution accident, against objects and mechanical damage, then we can easily prevent and control the different kinds of injury. This is the purpose of the detailed safety evaluation, and the whole evaluation process, through a computer program of construction safety evaluation model to achieve.
     The main work of this article: First, introducing the theory and methods of security evaluation on the internal and external, and propose in accordance with the actual situation. Second, establishing the indicators of the detailed evaluation of construction project safety, and summing up the relationship of input and output indicators. Third, using fault tree analysis to calculate the mathematical relations and weights of the input and output indicators. Fourth, translating the input data into output data by fuzzy comprehensive evaluation, and matching the sample data with the input data and the corresponding output data. Fifth, constructing the detailed evaluation model by BP artificial neural network. Last, using MATLAB language to program a system to achieve the procedure, and doing empirical research with the data collected through the system.
引文
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