关键词: 基坑, 水平位移预测, 极限学习机, 属性约简, 影响因素筛选

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.

Key words: foundation pit, horizontal displacement prediction, extreme learning machine (ELM), attri-bute reduction, screening influencing factors