• 学报（中文） •

### 基于RS-MIV-ELM模型的基坑水平位移影响因素分析和预测

1. 1. 中国科学院武汉岩土力学研究所 岩土力学与工程国家重点实验室， 武汉 430071； 2. 中国科学院大学， 北京 100049
• 通讯作者: 王浩，男，研究员，博士生导师，电话（Tel.）：027-87197320；E-mail: hwang@whrsm.ac.cn.
• 作者简介:钟国强（1990-），男，山东省曲阜市人，博士生，目前主要从事岩土工程自动化监测及变形预测和风险评估研究.
• 基金资助:
国家自然科学基金资助项目（41472288, 41172287, 51579235）

### Analysis and Prediction of Factors Affecting Horizontal Displacement of Foundation Pit Based on RS-MIV-ELM Model

ZHONG Guoqiang,WANG Hao,ZHANG Guohua,QIN Weimin WANG Chengtang,XIONG Junfeng

1. 1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: In order to predict the maximum horizontal displacement and depth of the inclinometer in the foundation pit, an RS-MIV-ELM model based on rough set attribute reduction, mean impact value and extreme learning machine was proposed. The model was based on systematic analysis and quantification of deformation factors, the attribute reduction algorithm of rough set (RS) and the mean impact value based on the extreme learning machine algorithm (ELM-MIV) were used to remove the redundant factors and small correlation factors respectively. Then, the extreme learning machine (ELM) model was trained by the simplified influence factor set, and the model was used to predict the displacement of other measuring points. Experimental results show that the training speed, prediction accuracy and generalization ability of the proposed model are better than those of the all factors ELM model and the BP neural network model based on the simplest set. The root mean square error or average relative error of RS-MIV-ELM model is about 1/2~2/3 of the two contrast models.