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世界地质 2018, 37(4) 1281-1287 DOI: 10.3969/j.issn.1004-5589.2018.04.029 ISSN: 1004-5589 CN: 22-1111/P

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	极限学习机
	深基坑
	季冻区
	地表沉降
	变形预测
	本文作者相关文章
	林楠
	陈永良
	李伟东
	刘鹰
	PubMed
	Article by Lin N
	Article by Chen Y
	Article by Li W
	Article by Liu Y

极限学习机模型在季冻区深基坑地表沉降预测中的应用

林楠^1,2, 陈永良³, 李伟东¹, 刘鹰⁴

1. 吉林建筑大学测绘与勘查工程学院, 长春 130118;
2. 吉林大学地球科学学院, 长春 130026;
3. 吉林大学综合信息矿产预测研究所, 长春 130026;
4. 中铁隧道勘测设计院有限公司, 长春 130026

摘要：针对传统数据驱动模型存在收敛速度慢、过度拟合等问题，提出了基于极限学习机算法的基坑地表沉降预测方法。结合季冻区地铁车站基坑的特点，提取基坑开挖时间、开挖深度、围护桩顶位移、围护桩内力、支撑轴力及地表温度等特征信息，建立极限学习机回归预测模型，选用实例数据进行算例分析，并将其与传统回归预测模型进行对比，实验结果表明，极限学习机模型收敛速度快，泛化能力强，其预测精度优于传统预测模型，且在学习速度方面优势明显，对深基坑安全监控有一定的实用价值。

关键词：极限学习机深基坑季冻区地表沉降变形预测

Application of extreme learning machine model in ground settlement prediction of deep foundation pit in seasonal frozen area

LIN Nan^1,2, CHEN Yong-liang³, LI Wei-dong¹, LIU Ying⁴

1. College of Surveying and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China;
2. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;
3. Mineral Resources Institute of Comprehensive Information Prediction, Jilin University, Changchun 130026, China;
4. China Railway Tunnel Survey & Design Institute Co., Ltd, Changchun 130118, China

Abstract: In order to solve the problems of slow convergence rate and over fitting in driving model using traditional data, a prediction method of ground settlement of foundation pit based on extreme learning machine algorithm was proposed. Combined with the characteristics of subway station foundation pit in seasonal frozen area, the regression prediction model of extreme learning machine was established by extracting the characteristic information of excavation time, excavation depth, top displacement of retaining pile, internal force of retaining pile, supporting axial force and surface temperature, etc. The prediction model was analyzed by instance data, and compared with the traditional regression model. The results showed that the extreme learning machine method converged faster, and had higher generalization ability and learning speed. Moreover, the prediction accuracy was better than the traditional prediction model. Therefore, the model can play a significative role on safety monitoring of deep foundation pit.

Keywords: extreme learning machine deep foundation pit seasonal frozen region ground settlement deformation monitoring

收稿日期 2017-05-29 修回日期 2018-05-07 网络版发布日期

DOI: 10.3969/j.issn.1004-5589.2018.04.029

基金项目:

国家自然科学基金项目（412723609）、住房和城乡建设部基金项目（2016-K5-019）联合资助.

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