Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review,        editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name
E-mail
Phone
Title
Content
Verification Code
Turn off MathJax
Article Contents

XU Cong, WANG Shaowei, GU Chongshi, SU Huaizhi. A Probabilistic Prediction Model for Displacement of Super High Arch Dams Considering the Deformation Spatial Association[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200508
Citation: XU Cong, WANG Shaowei, GU Chongshi, SU Huaizhi. A Probabilistic Prediction Model for Displacement of Super High Arch Dams Considering the Deformation Spatial Association[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200508

A Probabilistic Prediction Model for Displacement of Super High Arch Dams Considering the Deformation Spatial Association

doi: 10.13203/j.whugis20200508
Funds:

The National Natural Science Foundation of China (51709021), the Project funded by China Postdoctoral Science Foundation (2020M670387), the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2019nkzd03), the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) (IWHR-SKL-KF202002), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_2560).

  • Received Date: 2020-09-24
    Available Online: 2021-05-07
  • Machine learning language has become an ideal modeling tool in the field of dam health monitoring with its powerful nonlinear data mining ability. However, the minimum fitting mean square error (MSE) is determined as the only optimization objective in the traditional modeling process, which is likely to cause over-fitting problems. To overcome this problem, based on the relevance vector machine (RVM), a probabilistic prediction model is established under the constraint of double optimization objectives, which integrates the deformation spatial association and MSE. The deformation spatial association is quantified by the shape similarity index (SSI) at first. The double objective is then established with the combination of the MSE and SSI, and is achieved by making the MSE as small as possible, while the SSI is as large as possible. Engineering example of the Jinping-I arch dam shows that the average decrease proportion of the root mean square error (RMSE) and maximum absolute error (ME) of the proposed double objective RVM model is 31.2% and 24.8%, respectively, and the prediction performance can be further improved by using the multi-kernel function. The prediction confidence bandwidth of the RVM model is significantly smaller than that of the traditional multiple linear regression model, with an average decrease proportion of 75.1%. Therefore, the multi-kernel double objective RVM model established for the displacement of super high arch dams can effectively improve the prediction performance and reduce the uncertainty.
  • [1] Salazar F,Morán R,Toledo M A,et al. Data-Based Models for The Prediction of Dam Behavior:A Review and Some Methodological Consideration[J]. Archives Computational Methods in Engineering,2017,24(1):1-21.
    [2] Su Huaizhi,Chen Zhexin,Wen Zhiping. Performance Improvement Method of Support Vector Machine-Based Model Monitoring Dam Safety[J]. Structural Control and Health Monitoring,2016,23(2):252-266.
    [3] Wei Bowen,Chen Liangjie,Li Huokun,et al. Optimized Prediction Model for Concrete Dam Displacement Based on Signal Residual Amendment[J]. Applied Mathematical Modelling,2020,78:20-36.
    [4] Hu Jiang,Wu Suhua. Statistical Modeling for Deformation Analysis of Concrete Arch Dams with Influential Horizontal Cracks[J]. Structural Health Monitoring,2019,18(2):546-562.
    [5] Wang Shaowei,Xu Yingli,Gu Chongshi,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.
    [6] Mata J. Interpretation of Concrete Dam Behaviour with Artificial Neural Network and Multiple Linear Regression Models[J]. Engineering Structures,2011,33(3):903-910.
    [7] Ranković V,Grujović N,Divac D,et al. Development of Support Vector Regression Identification Model for Prediction of Dam Structural Behaviour[J]. Structural Safety,2014,48:33-39.
    [8] Salazar F,Toledo MÁ,Oñate E,et al. Interpretation of Dam Deformation and Leakage with Boosted Regression Trees[J]. Engineering Structures,2016,119:230-251.
    [9] Liu Wenju,Pan Jianwen,Ren Yisha,et al. Coupling Prediction Model for Long-Term Displacements of Arch Dams Based on Long Short-Term Memory Network[J]. Structural Control and Health Monitoring,2020,27(3):e2548.
    [10] Salazar F,Toledo MA,Oñate E,et al. An Empirical Comparison of Machine Learning Techniques for Dam Behaviour Modelling[J]. Structural Safety,2015,56:9-17.
    [11] Wang Shaowei,Xu Yingli,Gu Chongshi,et al. Two Spatial Association-Considered Mathematical Models for Diagnosing The Long-Term Balanced Relationship and Short-Term Fluctuation of The Deformation Behaviour of High Concrete Arch Dams[J]. Structural Health Monitoring,2020,19(5):1421-1439.
    [12] Shao Chenfei,Gu Chongshi,Yang Meng,et al. A novel model of dam displacement based on panel data[J]. Structural Control and Health Monitoring,2018,25(1):e2037.
    [13] Chen Siyu,Gu Chongshi,Lin Chaoning,et al. Multi-Kernel Optimized Relevance Vector Machine for Probabilistic Prediction of Concrete Dam Displacement[J]. Engineering with Computers. 2020(online first).
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(419) PDF downloads(9) Cited by()

Related
Proportional views

A Probabilistic Prediction Model for Displacement of Super High Arch Dams Considering the Deformation Spatial Association

doi: 10.13203/j.whugis20200508
Funds:

The National Natural Science Foundation of China (51709021), the Project funded by China Postdoctoral Science Foundation (2020M670387), the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2019nkzd03), the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) (IWHR-SKL-KF202002), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_2560).

Abstract: Machine learning language has become an ideal modeling tool in the field of dam health monitoring with its powerful nonlinear data mining ability. However, the minimum fitting mean square error (MSE) is determined as the only optimization objective in the traditional modeling process, which is likely to cause over-fitting problems. To overcome this problem, based on the relevance vector machine (RVM), a probabilistic prediction model is established under the constraint of double optimization objectives, which integrates the deformation spatial association and MSE. The deformation spatial association is quantified by the shape similarity index (SSI) at first. The double objective is then established with the combination of the MSE and SSI, and is achieved by making the MSE as small as possible, while the SSI is as large as possible. Engineering example of the Jinping-I arch dam shows that the average decrease proportion of the root mean square error (RMSE) and maximum absolute error (ME) of the proposed double objective RVM model is 31.2% and 24.8%, respectively, and the prediction performance can be further improved by using the multi-kernel function. The prediction confidence bandwidth of the RVM model is significantly smaller than that of the traditional multiple linear regression model, with an average decrease proportion of 75.1%. Therefore, the multi-kernel double objective RVM model established for the displacement of super high arch dams can effectively improve the prediction performance and reduce the uncertainty.

XU Cong, WANG Shaowei, GU Chongshi, SU Huaizhi. A Probabilistic Prediction Model for Displacement of Super High Arch Dams Considering the Deformation Spatial Association[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200508
Citation: XU Cong, WANG Shaowei, GU Chongshi, SU Huaizhi. A Probabilistic Prediction Model for Displacement of Super High Arch Dams Considering the Deformation Spatial Association[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200508
Reference (13)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return