Application of Ground Settlement Prediction Based on EMD-RVM

DOI

10.2991/icmeit-17.2017.35How to use a DOI?

Keywords

AIP Proceedings; Title Here; International Conference; Research Center of Engineering and Science.

Abstract

Ground settlement is a critical issue in underground construction. Ground settlement prediction is important to identify serious damage to adjacent structures caused by settlement exceed the standard. However, conventional methods have some limitations due to nonlinearity and non-stationarity of settlement data. This paper proposes a hybrid model based on empirical model decomposition (EMD) and relevance vector machine(RVM) regression optimized by particle swarm optimization (PSO) designated as EMD-RVM. EMD is used to decompose the ground measured settlement time series into several stationary components called intrinsic mode functions (IMFs). Then, SVR is applied to predict the components independently. At last, the expected prediction values are the sum of all components prediction value at the same time. In order to validate the performance of the proposed method., the signal support vector regression(SVR), signal relevance vector machine(RVM) regression, relevance vector machine based on wavelet transform(WT-RVM) are used to be compared with root mean square error (RMSE) and mean absolute percentage Error(MAPE) are used to evaluate these models. The evaluation results indicate the method proposed is effective and practical.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Pengfei Wang
PY  - 2017/05
DA  - 2017/05
TI  - Application of Ground Settlement Prediction Based on EMD-RVM
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 193
EP  - 198
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.35
DO  - 10.2991/icmeit-17.2017.35
ID  - Wang2017/05
ER  -