Study on Deformation Law of Foundation Pit by Multifractal Detrended Fluctuation Analysis and Extreme Learning Machine Improved by Particle Swarm Optimization

ZHU Jing

doi:10.11988/ckyyb.20170946

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Journal of Changjiang River Scientific Research Institute ›› 2019, Vol. 36 ›› Issue (3) : 53-58. DOI: 10.11988/ckyyb.20170946

ENGINEERING SAFETY AND DISASTER PREVENTION

Study on Deformation Law of Foundation Pit by Multifractal Detrended Fluctuation Analysis and Extreme Learning Machine Improved by Particle Swarm Optimization

ZHU Jing

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Abstract

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.

Key words

foundation pit / MF-DFA / extreme learning machine / deformation trend judgment / deformation prediction

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ZHU Jing. Study on Deformation Law of Foundation Pit by Multifractal Detrended Fluctuation Analysis and Extreme Learning Machine Improved by Particle Swarm Optimization[J]. Journal of Changjiang River Scientific Research Institute. 2019, 36(3): 53-58 https://doi.org/10.11988/ckyyb.20170946

References

[1] 王永明, 李明峰, 檀丁,等. 南京地区建筑基坑变形预警与安全监控系统[J]. 土木工程学报, 2015,48(增2):143-147.
[2] 李莉, 陈鹏宇, 段新胜. 基坑变形预测的灰色二次优化模型[J] 辽宁工程技术大学学报(自然科学版), 2010,29(5):926-929.
[3] 刘贺, 张弘强, 刘斌. 基于粒子群优化神经网络算法的深基坑变形预测方法[J]. 吉林大学学报(地球科学版), 2014, 44(5):1609-1614.
[4] 张运良, 聂子云, 李凤翔,等. 数值分析在基坑变形预测中的应用[J]. 岩土工程学报, 2012, 34(增1):113-119.
[5] 胡江, 苏怀智, 马福恒,等. MF-DFA在大坝安全监测序列分析和整体性态识别中的应用[J]. 水利水电科技进展, 2014, 34(3):50-55.
[6] 袁晓辉, 齐习文, 田昊,等. 基于改进型MF-DFA的月径流序列多重分形分析[J]. 水力发电, 2011, 37(9):21-24.
[7] 于伟红, 贾康丽, 潘学平,等. 河北省降水和气温的非趋势性波动分析[J]. 干旱区资源与环境, 2015, 29(1):134-139.
[8] 高彩云, 崔希民, 高宁. 熵权遗传算法及极限学习机地铁隧道沉降预测[J]. 测绘科学, 2016, 41(2):71-75.
[9] 高彩云, 崔希民. 滑坡变形预测灰色神经网络耦合模型的构建及适用性分析[J]. 大地测量与地球动力学, 2015, 35(5):835-839.
[10]熊杰, 陈绍宽, 韦伟,等. 基于多重分形去趋势波动分析法的交通流多重分形无标度区间自动识别方法[J]. 物理学报, 2014, 63(20):99-106.
[11]王俊丽, 张辰彦, 何红弟,等. 基于MF-DFA的交通流量多重分形研究[J]. 计算机工程与应用, 2016, 52(6):31-34.
[12]戴波, 何启. 大坝变形监测统计模型与混沌优化ELM组合模型[J]. 水利水运工程学报, 2016,(6):9-15.
[13]王雪妮,韩国锋.地铁车站深基坑的变形预测及稳定性研究[J].长江科学院院报,2018,35(10):77-81,87.
[14]张颖, 李梅. 基于粒子群优化极限学习机的水质评价新模型[J]. 环境科学与技术, 2016,(5):135-139.
[15]刘雪剑. 融合神经元网络与建模分析的基坑变形预测研究[D]. 北京:北京交通大学, 2016.
[16]金路, 姜谙男, 赵文. 基于差异进化支持向量机的基坑变形时间序列预测[J]. 岩土工程学报, 2008,30(增1):216-219.