Multi-output displacement health monitoring model for concrete gravity dam in severely cold region based on clustering of measured dam temperature field

Abstract

Constructing an accurate dam displacement health monitoring (DHM) model is crucial to ensure the safety of the dam. However, previous studies on DHM focused on the analysis and prediction of a single measurement point, with little work on multiple measurement points, which leads to low efficiency in evaluating the overall status of dams. Furthermore, the majority of these models are based on hydraulic engineering in moderate climatic areas, which results in low accuracy when applied to severely cold regions. To address these issues, the HT_cT model is proposed based on full consideration of extreme climate and engineering measures in cold regions, which replaces the commonly used harmonic function or air temperature with the features extracted from the measured temperature field after clustering as a temperature factor. The method effectively overcomes multicollinearity while ensuring accuracy. Moreover, multi-output least-square support vector regression (MOLSSVR), a multi-output model that can forecast multiple measurement points simultaneously, is proposed. By combining it with the HT_cT, the efficiency of the model is significantly improved. In addition, grey wolf optimization (GWO) is introduced to search for the optimal hyperparameters of the coupling model. The feasibility, accuracy, robustness, and long-term predictive capability of the proposed model are validated with measured displacements of a concrete gravity dam in a severely cold region. The results show that the proposed model outperforms the four popular machine learning (ML) models, including two support vector regression (SVR)-based models and two neural network-based models, which illustrate that the proposed model has outstanding accuracy and excellent long-term predictive capability. It provides an accurate and efficient novel approach for dam displacement safety monitoring in severely cold regions.

Keywords

Concrete gravity dam severely cold region displacement health monitoring multi-output model dam temperature field

Get full access to this article

View all access options for this article.

References

Wang

, et al. Displacement monitoring model of concrete dams using the shape feature clustering-based temperature principal component factor. Struct Control Health Monit 2020; 27(10): e2603.

Chen

Wen

Performance improvement method of support vector machine-based model monitoring dam safety. Struct Control Health Monit 2016; 23(2): 252–266.

Safety monitoring theory and its application of hydraulic structures. Beijing, China: Higher Education, 2003.

Mata

de Castro

da Costa

JS.

Constructing statistical models for arch dam deformation. Struct Control Health Monit 2014; 21(3): 423–437.

Yuan

Wei

Xie

, et al. Modified dam deformation monitoring model considering periodic component contained in residual sequence. Struct Control Health Monit 2020; 27(12): e2633.

Leger

Leclerc

Hydrostatic, temperature, time-displacement model for concrete dams. J Eng Mech-Asce 2007; 133(3): 267–277.

Yuan

Qin

, et al. Performance-improved TSVR-based DHM model of super high arch dams using measured air temperature. Eng Struct 2022; 250: 113400.

Kang

Liu

Concrete dam behavior prediction using multivariate adaptive regression splines with measured air temperature. Arab J Sci Eng 2019; 44(10): 8661–8673.

Kang

Zhao

, et al. Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation. Eng Struct 2019; 180: 642–653.

10.

Kang

Displacement model for concrete dam safety monitoring via gaussian process regression considering extreme air temperature. J Struct Eng 2020; 146(1): 05019001.

11.

Wang

Yang

Shen

Study on MPGA-BP of gravity dam deformation prediction. Math Prob Eng 2017; 2017: 2586107.

12.

Liu

H-F

Ren

Zheng

Z-T

, et al. Study of a gray genetic BP neural network model in fault monitoring and a diagnosis system for dam safety. ISPRS Int J Geo-Info 2018; 7(1): 4.

13.

Dai

Zhu

, et al. On the use of an improved artificial fish swarm algorithm-backpropagation neural network for predicting dam deformation behavior. Complexity 2020; 2020: 5463893.

14.

Zhang

Cao

Xie

, et al. An improved long short-term memory model for dam displacement prediction. Math Prob Eng 2019; 2019: 6792189.

15.

Liu

Pan

Ren

, et al. Coupling prediction model for long-term displacements of arch dams based on long short-term memory network. Struct Control Health Monit 2020; 27(7): e2548.

16.

Zhu

Cao

, et al. Application of ELM neural network in dam displacement early warning model. Adv Mater Res 2013; 864–867: 2363–2366.

17.

Kang

Liu

, et al. Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Health Monit 2017; 24(10): e1997.

18.

Cheng

Xiong

Application of extreme learning machine combination model for dam displacement prediction. Procedia Comput Sci 2017; 107: 373–378.

19.

Yang

Xiao

Wang

, et al. Using complementary ensemble empirical mode decomposition and gated recurrent unit to predict landslide displacements in dam reservoir. Sensors 2022; 22(4): 1320.

20.

Bao

Gao

, et al. A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques. Struct Health Monit-an Int J 2022; 21(3): 770–787.

21.

Tabari

MMR

Sanayei

HRZ

. Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models. Soft Comput 2019; 23(19): 9629–9645.

22.

Ren

Bai

, et al. A multiple-point monitoring model for concrete dam displacements based on correlated multiple-output support vector regression. Struct Health Monit-an Int J 2022; 21(6): 147592 17211069639.

23.

Rankovic

Grujovic

Divac

, et al. Development of support vector regression identification model for prediction of dam structural behaviour. Struct Safe 2014; 48: 33–39.

24.

Chen

Lin

, et al. Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement. Eng Comput 2021; 37(3): 1943–1959.

25.

Liu

Wang

, et al. Dam safety evaluation based on multiple linear regression and numerical simulation. Rock Mech Rock Eng 2018; 51(8): 2451–2467.

26.

Mata

Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 2011; 33(3): 903–910.

27.

Yin

Zhao

, et al. A nonlinear method for component separation of dam effect quantities using kernel partial least squares and pseudosamples. Adv Civ Eng 2019; 2019: 1958173.

28.

Cheng

Zhou

, et al. External deformation monitoring and improved partial least squares data analysis methods of high core rock-fill dam (HCRFD). Sensors 2020; 20(2): 444.

29.

Zhou

, et al. Dam safety analysis based on stepwise regression model. Adv Mater Res 2011; 204–210: 2158–2161.

30.

G-y

Yue

J-p

Zhou

B-x

, et al. Application of an artificial immune algorithm on a statistical model of dam displacement. Comput Math Appl 2011; 62(10): 3980–3986.

31.

Fang

Chen

Zhang

, et al. Quantile regression analysis on short-term measured data of gravity-arch dam based on POD. Adv Eng Res 2017, 70: 142–149.

32.

Castillo-Boton

Casillas-Perez

Casanova-Mateo

, et al. Analysis and prediction of dammed water level in a hydropower reservoir using machine learning and persistence-based techniques. Water 2020; 12(6): 1528.

33.

Wan

Xie

Peng

Research on the application of improved SVM model in dam displacement and deformation prediction. Earth Environ Sci 2020; 585: 012047.

34.

Pan

Liu

, et al. A deformation prediction model of high arch dams in the initial operation period based on PSR-SVM-IGWO. Math Prob Eng 2021; 2021: 8487997.

35.

Chen

Huang

, et al. Long-tern field test and numerical simulation of foamed polyurethane insulation on concrete dam in severely cold region. Constr Build Mater 2019; 212: 618–634.

36.

Kang

Liu

JJ.

Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines. Struct Health Monit-an Int J 2020; 19(4): 987–1002.

37.

Spatial and temporal clustering model of concrete arch dam deformation data based on panel data analysis method. J Yangtze River Sci Res Inst 2021; 38(2): 39–45. (in Chinese)

38.

Zhang

Deformation monitoring and safety warning of concrete dam. Master Thesis, Zhengzhou University, China. 2021.(in Chinese)

39.

Qiao

, et al. Multi-output least-squares support vector regression machines. Pattern Recognit Lett 2013; 34(9): 1078–1084.

40.

Zhu

Gao

An efficient gradient-based model selection algorithm for multi-output least-squares support vector regression machines. Pattern Recognit Lett 2018; 111: 16–22.

41.

Mirjalili

How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl Intell 2015; 43(1): 150–161.

42.

Mirjalili

Lewis

Grey wolf optimizer. Adv Eng Softw 2014; 69: 46–61.

43.

Huang

Wan

Deformation monitoring analysis of an RCC gravity dam considering frost heaving in an alpine region: a case study. Eur J Environ Civ Eng 2022; 26(4): 1349–1363.

44.

Huang

Wan

Study on viscoelastic deformation monitoring index of an RCC gravity dam in an alpine region using orthogonal test design. Math Prob Eng 2018; 2018: 8743505.