基于粗糙集和RBF神经网络的地铁施工安全风险评估

  本文关键词：基于粗糙集和RBF神经网络的地铁施工安全风险评估，由笔耕文化传播整理发布。

基于粗糙集和RBF神经网络的地铁施工安全风险评估

作者：1”的相关文章'>陈帆1  2”的相关文章'>谢洪涛2 

单位：1. 湖南科技大学土木工程学院,湖南湘潭 411201;
2. 昆明理工大学建筑工程学院,昆明 650093

关键词：安全管理工程 地铁施工 安全风险 RBF神经网络 粗糙集 

分类号：X948

出版年·卷·期（页码）：2013·13·第4期（232-235）

摘要：

为了解决当前我国地铁施工过程中所面临的安全风险评估问题,提出了一种基于粗糙集和RBF神经网络的地铁施工安全风险评估模型。在分析地铁施工安全风险评估指标的基础上,从地质条件、施工技术、施工管理等方面选择了33个变量指标。针对传统神经网络收敛速度慢、容错性差、结果并不唯一的缺点,利用粗糙集理论的属性约简方法使RBF神经网络的输入数据减少且不相关,并利用长沙、武汉、杭州、昆明、北京、上海、广州、重庆的28个地铁工程项目的问卷调查数据实现模型的训练及检测。结果表明,采用粗糙集方法约简属性能使RBF网络的输入数据从33个减少至15个,用经过粗糙集约简后的样本集作为神经网络的训练样本集可以有效地简化神经网络的结构,减少训练步数与训练时间,并提高网络的学习速度和判断准确率。同时,经过粗糙集约简后的神经网络的收敛速度和计算精度能够满足地铁施工安全风险评估的需要。通过粗糙集与RBF神经网络相结合所构建的耦合模型可以识别地铁施工过程的安全状态,进而有针对性地完善地铁施工的相关安全技术。

The present article is inclined to bring forward an urban subway construction risk assessment based on the rough set and RBF neural network. As is known, it has become urgent to promote the safer and more securable subway construction environment with the current surge of subway construction tide in China. In this article, we would like to propose a subway construction risk assessment model based on the analysis of subway construction safety risk assessment model including indicators, which we have chosen of 33 variables in the light of geological conditions, the construction technology, management and so on. In order to overcome the drawbacks of the traditional artificial neural network, namely, slow convergence, poor fault tolerance and inconsistent results, we prefer to choose the rough set method, intending to reduce the table attribute of the sampling decisions and decrease the amount of irrelevant index inputs of the neural network data. The rough set method also allows us to do training and testing on how the given RBF neural network model can be used together with the rough set method by means of the actual subway construction project data. Practically speaking, we have already gained the above said survey data from the 28 subway construction practice from the field project experience in eight cities, that is, from Wuhan, Changsha, Hangzhou, Kunming, Beijing, Shanghai, Guangzhou and Chongqing. The results of our analysis of the data quoted show that the data input with the RBF neural network can actually be reduced from 33 to 15 by using the rough set method. After reducing the sampling data through the rough set method, the sampling sets can be used as the training sets of the RBF neural network. The RBF neural network structure can be effectively simplified with the training steps and training time decreased, and the network training and learning speed and efficiency can be greatly improved. And, as a result, the convergence speed and the calculation accuracy can be easily made to meet the requirements of the subway construction safety risk assessment through the attribute reduction. Therefore, it can be seen that this present coupling of the rough set method and RBF neural network system can be surely taken as the safety state guaranteeing means along with the relevant safety technology for the urban subway construction.

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  本文关键词：基于粗糙集和RBF神经网络的地铁施工安全风险评估，，由笔耕文化传播整理发布。