Monitoring Model for Displacement of Arch Dams Considering Viscoelastic Hysteretic Effect

XU Cong; WANG Shao-wei; LIU Yi; SUI Xu-peng

doi:10.11988/ckyyb.20201229

PDF(1520 KB)

Journal of Changjiang River Scientific Research Institute ›› 2022, Vol. 39 ›› Issue (3) : 67-72. DOI: 10.11988/ckyyb.20201229

ENGINEERING SAFETY AND DISASTER PREVENTION

Monitoring Model for Displacement of Arch Dams Considering Viscoelastic Hysteretic Effect

XU Cong¹, WANG Shao-wei^1,2,3, LIU Yi^2,3, SUI Xu-peng¹

Author information +

History +

Abstract

A displacement monitoring model should well interpret and predict the deformation behavior of arch dam. HHST model could explain the viscoelastic hysteretic deformation behavior of Jinping-I arch dam. To further improve the prediction accuracy of the HHST model, the nonlinear relationship between the finite element method (FEM)-calculated viscoelastic hysteretic displacement of arch dam and its causal factors is modeled by the support vector machine (SVM) and is used as a whole variable in the HHST model. In subsequence, a combinatorial monitoring model is established for the displacement of arch dam based on multiple linear regression (MLR). Case study of the Jinping-I arch dam shows that the prediction accuracy of the combinatorial monitoring model, which has a reduced number of input factors, is significantly higher than that of simple models directly established with all the 18 causal factors of the HHST model. SVM has a better prediction accuracy for the hysteretic hydraulic displacement than that of constrained least square method-based linear regression model. The two combined monitoring models, respectively using the SVM and linear regression-based hysteretic hydraulic displacement component, have similar interpretation ability for the measured deformation behavior of arch dams, while the former can effectively improve the prediction accuracy of dam displacement, with the average mean square error(MSE) of multiple monitoring points dropping by 21.67% and the average determination coefficient R² rising by 0.07%.

Key words

displacement of arch dams / viscoelastic hysteretic deformation behaviour / HHST model / support vector machine / combinatorial monitoring model / Jinping-I arch dam

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

XU Cong, WANG Shao-wei, LIU Yi, SUI Xu-peng. Monitoring Model for Displacement of Arch Dams Considering Viscoelastic Hysteretic Effect[J]. Journal of Changjiang River Scientific Research Institute. 2022, 39(3): 67-72 https://doi.org/10.11988/ckyyb.20201229

References

[1] SU Huai-zhi, CHEN Zhe-xin, WEN Zhi-ping. Performance Improvement Method of Support Vector Machine-based Model Monitoring Dam Safety[J]. Structural Control and Health Monitoring, 2016, 23(2): 252-266.
[2] 李明超,任秋兵,孔锐,等. 多维复杂关联因素下的大坝变形动态建模与预测分析[J]. 水利学报,2019,50(6):687-698.
[3] WEI Bo-wen, CHEN Liang-jie, LI Huo-kun, et al. Optimized Prediction Model for Concrete Dam Displacement Based on Signal Residual Amendment[J]. Applied Mathematical Modelling, 2020, 78: 20-36.
[4] WANG Shao-wei, XU Ying-li, GU Chong-shi, et al. Hysteretic Effect Considered Monitoring Model for Interpreting Abnormal Deformation Behavior of Arch Dams: A Case Study[J]. Structural Control and Health Monitoring, 2019, 26(10): 1-20.
[5] 王少伟,徐丛,苏怀智.锦屏一级拱坝黏弹性工作性态反演分析[J].水利水电科技进展,2020,40(5):62-69.
[6] 胡安玉,包腾飞,杨晨蕾,等. 基于LSTM-ARIMA的大坝变形组合预测模型及其应用[J].长江科学院院报,2020,37(10):64-68, 75.
[7] 李涧鸣,包腾飞,高瑾瑾,等. 基于小波EGM-ISFLA-SVR的大坝变形组合预测模型[J]. 水利水电技术,2018,49(5):57-62.
[8] 汪程,杨光,祖安君,等. 混凝土坝变形Wavelet-EGM-PE-ARIMA组合预测模型[J]. 长江科学院院报,2019,36(8):67-72.
[9] 王成,胡添翼,顾艳玲,等. 大坝安全监测的ARIMA-BP组合预测模型[J]. 三峡大学学报: 自然科学版,2018,40(1):20-24.
[10]魏博文,熊威,李火坤,等. 融合混沌残差的大坝位移蛙跳式组合预报模型[J]. 武汉大学学报: 信息科学版,2016,41(9):1272-1278.
[11]顾冲时,汪亚超,彭妍,等. 大坝安全监控模型的病态问题及其处理方法[J]. 中国科学:技术科学,2011,41(12):1574-1579.
[12]刘志,刘泽,杨金辉,等. 基于BBO-SVM的大坝变形预测模型与性能验证[J]. 水利水电技术,2020,51(8):62-68.
[13]钱秋培,崔伟杰,包腾飞,等. 基于SVM的混凝土坝变形监控模型预测能力实例分析[J]. 长江科学院院报,2018,35(8):46-50.
[14]肖阳,唐诗华,王江波,等. 基于果蝇优化算法的LS-SVM的大坝变形应用[J]. 水力发电,2018,44(5):101-104.
[15]WU Shi-yong, CAO Wei, ZHENG Jiang. Analysis of Working Behavior of Jinping-I Arch Dam During Initial Impoundment[J]. Water Science and Engineering,2016,9(3):240-248.
[16]徐丛,王少伟,顾冲时,等. 融合空间关联性的特高拱坝位移概率性预测模型[J]. 武汉大学学报: 信息科学版, doi: 10.13203/j.whugis20200508.