%0 Journal Article
%A ZHU Jing
%T Study on Deformation Law of Foundation Pit by Multifractal Detrended Fluctuation Analysis and Extreme Learning Machine Improved by Particle Swarm Optimization
%D 2019
%R 10.11988/ckyyb.20170946
%J Journal of Yangtze River Scientific Research Institute
%P 53-58
%V 36
%N 3
%X In view of the nonlinearity and complexity of deformation series of foundation pit, we propose to research the deformation law of foundation pit by using multifractional detrended fluctuation analysis (MF-DFA) and extreme learning machine improved by particle swarm optimization (PSO-ELM). First of all, we adopt MF-DFA method to analyze the series of deformation rate  of foundation pit; secondly, we employ PSO-ELM model to process the cumulative deformation series of foundation pit; finally, we can determine the comprehensive deformation trend of foundation pit by comparing the results of both deformation series. Conclusions imply that MF-DFA could effectively reflect the multifractional feature of deformation rate series, and meanwhile PSO-ELM model is of high accuracy in predicting deformation. The analysis results of the two methods are well consistent, which supports each other in accuracy.
%U http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20170946