A Deformation Prediction Model for Concrete Dam Based on Extreme Learning Machine Optimized by Variable Selection

CAO En-hua; BAO Teng-fei; HU Shao-pei; YUAN Rong-yao; YAN Tao

doi:10.11988/ckyyb.20210276

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Journal of Changjiang River Scientific Research Institute ›› 2022, Vol. 39 ›› Issue (7) : 59-65. DOI: 10.11988/ckyyb.20210276

ENGINEERING SAFETY AND DISASTER PREVENTION

A Deformation Prediction Model for Concrete Dam Based on Extreme Learning Machine Optimized by Variable Selection

CAO En-hua^1,2,3, BAO Teng-fei^1,2,3,4, HU Shao-pei³, YUAN Rong-yao³, YAN Tao^1,2,3

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Abstract

Traditional statistical models are of weak generalization capability and are prone to introduce high-dimensional variables,which will negatively affect the output of neural network-based prediction models and increase the risk of overfitting.It is necessary to build a data-driven model with appropriate dimensionality to accomplish accurate monitoring of dam deformation.In this paper,extreme learning machine(ELM)is selected as the base prediction model,and a variable selection method based on mean impact value(MIV)-ELM model is proposed to eliminate redundant information in the initial variable set,thus reducing the model's complexity and improving the prediction accuracy.Analysis results demonstrate that compared with traditional prediction models,HST-MIV-ELM not only has the highest prediction accuracy and robustness,but also has strong scalability.The study provides a reliable theoretical basis for the construction of dam safety monitoring system.

Key words

deformation prediction for concrete dam / variable selection / extreme learning machine / mean impact value / reverse variable-by-variable elimination method

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CAO En-hua, BAO Teng-fei, HU Shao-pei, YUAN Rong-yao, YAN Tao. A Deformation Prediction Model for Concrete Dam Based on Extreme Learning Machine Optimized by Variable Selection[J]. Journal of Changjiang River Scientific Research Institute. 2022, 39(7): 59-65 https://doi.org/10.11988/ckyyb.20210276

References

[1] 吴中如.水工建筑物安全监控理论及其应用[M].北京:高等教育出版社,2003.
[2] CHEN B,HU T,HUANG Z,et al.A Spatio-temporal Clustering and Diagnosis Method for Concrete Arch Dams Using Deformation Monitoring Data[J].Structural Health Monitoring,2019,18:1355-1371.
[3] 顾冲时,吴中如.大坝与坝基安全监控理论和方法及其应用[M].南京:河海大学出版社,2006.
[4] CAO E,BAO T,GU C,et al.A Novel Hybrid Decomposition-Ensemble Prediction Model for Dam Deformation[J].Applied Sciences,2020,10(16):5700,doi:10.3390/app10165700.
[5] YANG J,QU X,CHANG M.An Intelligent Singular Value Diagnostic Method for Concrete Dam Deformation Monitoring[J].Water Science and Engineering,2019,12(3):205-212.
[6] 赵二峰,李波,朱延涛.基于PPA-POT的RCCD变形监测控制值拟定方法[J].人民黄河,2021,43(3):135-139.
[7] 曹恩华,包腾飞,刘永涛,等.基于EMD-RVM-Arima的大坝变形预测模型及其应用[J].水利水电技术,2018,49(12):59-64.
[8] 康传利,陈洋,张临炜,等.小波和混沌神经网络在大坝变形预测中的应用[J].人民黄河,2020,42(3):101-104,116.
[9] 袁冬阳.基于多元时空信息挖掘的混凝土重力坝变形体征监控方法[D].南昌:南昌大学,2019:62-64.
[10] BELMOKRE A,MIHOUBI K,SANTILLÁN D.Analysis of Dam Behavior by Statistical Models:Application of the Random Forest Approach[J].KSCE Journal of Civil Engineering,2019,23:4800-4811.
[11] LI D,ZHOU Y,GAN X.Research on Multiple Points Deterministic Displacement Monitoring Model of Concrete Arch Dam[J].Journal of Hydraulic Engineering,2011,42:981-985.
[12] DAI B,GU C,ZHAO E,et al.Statistical Model Optimized Random Forest Regression Model for Concrete Dam Deformation Monitoring[J].Structural Control & Health Monitoring,2018,25(6):e2170.1-e2170.15.
[13] 胡雨菡,包腾飞,朱征,等.基于IABC-FCM-RVM算法的拱坝变形预测模型[J].武汉大学学报(工学版),2020,53(12):1055-1064.
[14] MATA J.Interpretation of Concrete Dam Behaviour with Artificial Neural Network and Multiple Linear Regression Models[J].Engineering Structures,2011,33:903-910.
[15] SU H,LI X,YANG Z,et al.Wavelet Support Vector Machine-based Prediction Model of Dam Deformation[J].Mechanical Systems and Signal Processing,2018,110:412-427.
[16] 鄢涛,陈波,曹恩华,等.基于EEMD-ELM的大坝变形预测模型[J].长江科学院院报,2020,37(11):70-73,80.
[17] 牛景太.基于奇异谱分析与PSO优化SVM的混凝土坝变形监控模型[J].水利水电科技进展,2020,40(6):60-65,77.
[18] 谢国权,戚蓝,曾新华.基于小波和神经网络拱坝变形预测的组合模型研究[J].武汉大学学报(工学版),2006,39(2):16-19.
[19] HUANG G,ZHU Q,SIEW C.Extreme Learning Machine:Theory and Applications[J].Neurocomputing,2006,70:489-501.
[20] DOMBI G W,NANDI P,SAXE J M,et al.Prediction of Rib Fracture Injury Outcome by an Artificial Neural Network[J].Journal of Trauma,1995,39:915-921.