Multi-point hybrid prediction model for concrete dam deformation based on the rapid identification of structural performance evolution state

Abstract

The deformation prediction model of concrete dams provides a scientific basis for long-term safety evaluations. However, traditional models primarily focus on characterizing the weight differences of deformation explanatory factors at a specific moment while ignoring the variations in causality induced by the evolution of structural performance. Additionally, the complex nonlinear mapping relationships significantly increase the difficulty of rapidly identifying the structural performance evolution states across different regions of dams. To address this issue, a multi-point hybrid prediction model for concrete dam deformation based on the rapid identification of structural performance evolution state is proposed in this paper. During the identification process, standard particle swarm optimization is employed to optimize the objective function, while hierarchical surrogate-model-assisted (HSMA) mechanism is proposed to improve the multi-dimensional adaptability by deeply coupling the iteration. With the identification results, a multi-point hybrid model for deformation prediction based on Bayesian optimized support vector regression is constructed. Taking an aging concrete dam as example, the application results indicate that the HSMA mechanism significantly improves the efficiency and accuracy of multi-dimensional identification of structural performance, while the fitting and prediction results of the proposed model exhibit a favorable agreement with the prototype monitoring data. After certain improvements and extensions, this method can be applied to construct the digital twin platforms for water conservancy engineering, providing technical support for monitoring spatiotemporal deformation.

Keywords

Concrete dam multi-point deformation prediction model multi-dimensional parameter inversion hierarchical surrogate model standard particle swarm optimization algorithm

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References

Shao

, et al. Application of spatiotemporal hybrid model of deformation in safety monitoring of high arch dams: a case study. Int J Environ Res Public Health 2020; 17(1): 319.

Liu

Wei

, et al. Multipoint hybrid model for RCC arch dam displacement health monitoring considering construction interface and its seepage. Appl Math Modell 2022; 110: 674–696.

Yang

Liu

Wang

, et al. Segmented sequence decomposition-Informer model for deformation of arch dams. Struct Health Monit Int J 2024; 23(5): 3007–3025.

Tian

Yang

Cheng

. Deep learning model for the deformation prediction of concrete dams under multistep and multifeature inputs based on an improved autoformer. Eng Appl Artif Intell 2024; 137: 109109.

Cai

Chen

, et al. Improved extreme learning machine-based dam deformation prediction considering the physical and hysteresis characteristics of the deformation sequence. J Civil Struct Health Monit 2022; 12(5): 1173–1190.

Yang

Chen

, et al. Hybrid hydraulic-seasonal-time model for predicting the deformation behaviour of high concrete dams during the operational period. Struct Control Health Monit 2021; 28(3): e2685.

Wei

Yuan

, et al. Modified hybrid forecast model considering chaotic residual errors for dam deformation. Struct Control Health Monit 2018; 25(8): e2188.

Wang

Wei

, et al. Dynamic inversion method for the material parameters of a high arch dam and its foundation. Appl Math Modell 2019; 71: 60–76.

Liu

Wang

, et al. Dynamic material parameter inversion of high arch dam under discharge excitation based on the modal parameters and Bayesian optimised deep learning. Adv Eng Inform 2023; 56: 102016.

10.

Zhang

Zhu

, et al. A mathematical-mechanical hybrid driven approach for determining the deformation monitoring indexes of concrete dam. Eng Struct 2023; 277: 115353.

11.

Lin

Chen

, et al. Structural identification in long-term deformation characteristic of dam foundation using meta-heuristic optimization techniques. Adv Eng Softw 2020; 148: 102870.

12.

Lin

Chen

, et al. Long-term viscoelastic deformation monitoring of a concrete dam: a multi-output surrogate model approach for parameter identification. Eng Struct 2022; 266: 114553.

13.

Hariri-Ardebili

Deng

, et al. A surrogate-assisted stochastic optimization inversion algorithm: parameter identification of dams. Adv Eng Inform 2023; 55: 101853.

14.

Zhang

Shen

, et al. A new approach for mechanical parameter inversion analysis of roller compacted concrete dams using modified PSO and RBFNN. Cluster Comput 2022; 25(6): 4633–4652.

15.

Kang

Liu

, et al. Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model. Eng Struct 2022; 256: 113999.

16.

Sevieri

Andreini

De Falco

, et al. Concrete gravity dams model parameters updating using static measurements. Eng Struct 2019; 196.

17.

Pang

, et al. Jaya-ICSM: a rapid inverse method driven by monitoring data for concrete-faced rockfill dams static displacement simulation. Adv Eng Inform 2023; 57: 102080.

18.

Xiong

Wei

. Identification of arch dam mechanical parameters based on sensitivity analysis and Hooke-Jeeves algorithm optimization. Structures 2022; 46: 88–98.

19.

Huang

Xie

, et al. Evaluation of the mechanical parameters of a reinforced concrete dam based on multi-source data. Structl Concrete 2022; 23(2): 652–668.

20.

Yang

, et al. AC-IBFGS-based inversion method for estimating the quasi-viscoelastic parameters of arch dams. IEEE Access 2022; 10: 68151–68160.

21.

. A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria. Appl Soft Comput 2022; 128: 109476.

22.

Lin

Chen

, et al. On the multi-parameters identification of concrete dams: a novel stochastic inverse approach. Int J Numer Anal Methods Geomech 2024; 48(16): 3792–3810.

23.

Huang

Yin

. Theoretical study on multi-parameter inversion non-uniqueness based on elastic displacements of concrete gravity dams. Appl Math Mech 2020; 41(2): 171–181.

24.

Liu

Kang

Limongelli

. Multi-zone parametric inverse analysis of super high arch dams using deep learning networks based on measured displacements. Adv Eng Inform 2023; 56: 102002.

25.

Wang

Yin

D-Q

Yang

, et al. Global and local surrogate-assisted differential evolution for expensive constrained optimization problems with inequality constraints. IEEE Trans Cybern 2019; 49(5): 1642–1656.

26.

. Safety monitoring theory & its application of hydraulic structure. Hohai University Press, 2003. (in Chinese)

27.

McKay

Beckman

Conover

. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 2000; 42(1): 55–61.

28.

Zambrano-Bigiarini

Clerc

Rojas

, et al. Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: Proceedings of the IEEE congress on evolutionary computation, Cancun, Mexico, 20–23 June 2013.

29.

Vapnik

Golowich

Smola

. Support vector method for function approximation, regression estimation, and signal processing. Adv Neural Inform Process Syst 2008; 9: 281–287.

30.

Bergstra

Bardenet

Bengio

, et al. Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, Granada, Spain, 12–14 December 2011, pp. 2546–2554.