| 工程先验知识辨识下的滑坡非平稳变形支持向量机预测 |
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| 引用本文： | 董辉,陈家博,杨果岳,傅鹤林,侯俊敏.工程先验知识辨识下的滑坡非平稳变形支持向量机预测[J].岩土力学,2012,33(8):2366-2372. |
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| 作者姓名： | 董辉 陈家博 杨果岳 傅鹤林 侯俊敏 |
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| 作者单位： | 1. 湘潭大学 土木工程与力学学院，湖南 湘潭 411105；2. 中南大学 土木建筑学院，长沙 410075 |
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| 基金项目： | 国家自然科学基金(No.51108397);湖南省科技厅计划项目(No.2009SK3146);湖南省教育厅计划项目(No.10C1285) |
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| 摘 要： | 针对缺乏非平稳变形（阶段性、反复性及突变性）特征数据导致的滑坡预测与评估不完全符合工程实际的情况，通过分析变形时序的物理意义与类型，在定义了非平稳变形趋势变化外延模式的基础上，提出了综合考虑滑坡当前变形阶段、观测变形数据特征以及待预测时段外界诱发因素的时序外延模式辨识方法，并进一步在工程先验知识指导下建立了支持向量机（SVM）预测模型。通过2个工程实例对方法进行应用验证。结果表明，工程先验知识能够有效补充非平稳变形的观测数据信息，对预测建模具有指导性作用；融入外延模式的SVM模型与一般性SVM的外推预测，其平均相对误差可降低2～3倍，预测的可靠性与准确性得到了显著增强。
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| 关 键 词： | 滑坡 先验知识 非平稳变形 SVM预测 外延模式 |
| 收稿时间： | 2011-03-09 |
SVM prediction of nonstationary deformation of landslide based on identification with prior knowledge |
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| DONG Hui
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CHEN Jia-bo
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YANG Guo-yue
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FU He-lin
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HOU Jun-min.SVM prediction of nonstationary deformation of landslide based on identification with prior knowledge[J].Rock and Soil Mechanics,2012,33(8):2366-2372. |
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| Authors: | DONG Hui CHEN Jia-bo YANG Guo-yue FU He-lin HOU Jun-min |
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| Institution: | 1. College of Civil Engineering and Mechanics, Xiangtan University, Xiangtan, Hunan 411105, China; 2. School of Civil Engineering and Architecture, Central South University, Changsha 410075, China |
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| Abstract: | Defining extensive patterns of nonstationary deformation by analyzing the physical significance and types of monitor time series,aiming at the lower reliability of landslide early-warning due to the key information data,reflected in stages,repeatability and mutability of landslide process,are limited or uncompleted observed.A method,added or imposed extensive pattern is identified according to current status of landslide,such as deformation stage analyzed use factors superposition technique and the types of time series,and the potential causative factors of prediction time into model of support vector machine(SVM) algorithm is presented.The efficiencies of new idea are tested by predicting two groups of displacement time series.All experimental results show that the project prior knowledge can be availably used to increase data message of nonstationary deformation and guide forecast modeling.In contrast to common SVM,the model based on extensive pattern is more reliable and accurate.Particularly,the average relative error is markedly cut down about 2 to 3 times. |
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| Keywords: | landslide prior knowledge instability deformation support vector machine prediction extensive pattern |
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