| 基于RS-SVM的建筑施工项目安全预警模型 |
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| 引用本文： | 李万庆,安娟. 基于RS-SVM的建筑施工项目安全预警模型[J]. 河北工程大学学报(自然科学版), 2010, 27(4): 30-35 |
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| 作者姓名： | 李万庆 安娟 |
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| 作者单位： | 河北工程大学,经管学院,河北,邯郸,056038;河北工程大学,经管学院,河北,邯郸,056038 |
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| 摘 要： | 针对目前建筑施工项目安全风险管理的现状,应用粗集理论对建筑施工项目的安全因素进行预处理,将处理后的信息结构作为支持向量机的输入数据进行训练和预测,构建建筑施工项目安全风险预警模型,并在小样本条件下,与BP神经网络进行对比分析。结果表明,RS-SVM预测模型的最小均方根误差为0.011 5,BP神经网络的均方根误差为0.070 7,RS-SVM预警模型的预测精度、泛化能力明显优越于BP神经网络学习方法。
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| 关 键 词： | 建筑施工 粗集 支持向量机 安全预警 |
| 收稿时间： | 2010-09-08 |
Safety early-warning model of construction project based on rough set and support vector machine |
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| LI Wan-qing and AN Juan. Safety early-warning model of construction project based on rough set and support vector machine[J]. Journal of Hebei University of Engineering(Natural Science Edition), 2010, 27(4): 30-35 |
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| Authors: | LI Wan-qing and AN Juan |
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| Affiliation: | Chongqing Research & Design Institute,China Railway Eryuan Engineering Group Co.Ltd,Chongqing 400015,China;Chongqing Research & Design Institute,China Railway Eryuan Engineering Group Co.Ltd,Chongqing 400015,China |
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| Abstract: | In this paper,the finite difference strength reduction criteria was used to determine slope failure analysis.Taking the high rock slope in the head of dam for the example,Flac3D was used to analysis the high slope stability before and after water based on the basis of Survey.The safety coefficient was solved by using the strength reducing method.The results showed that the high slope is stability and finite difference strength reduction can resolve quantitative rock slope stability problems. |
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| Keywords: | construction project rough set support vector machine safety early-warning |
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