Stochastic Inversion Method for Concrete Dams on the Basis of Bayesian Back Analysis Theory

Gu, Chongshi; Cao, Xin; Xu, Bo

doi:10.1155/2019/5943913

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Luo et al [11] used the measured data, combined with GA-BP neural network, to calculate the mechanical parameters of the surrounding rock by displacement inverse analysis method. Gu et al [12] using the measured displacement data and Bayesian inverse analysis theory, an inversion method of the mechanical parameters of concrete dams was proposed and the stochastic properties of concrete dams were inverted. Wang et al [13] taking the Lanzhou-Haikou tunnel as an engineering example, the inversion analysis method of IA-BP algorithm was used to perform multiparameter creep inversion of the tunnel surrounding rock under the stresspercolation coupling conditions.…”

Section: Introductionmentioning

confidence: 99%

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Wang

Cui

et al. 2023

Advances in Civil Engineering

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Based on similar model tests and finite element simulations, inversion study of the mechanical parameters of the tunnel surrounding rock by combining artificial fish swarm algorithm (AFSA) and digital scattering correlation method is carried out. The inversion of the mechanical parameters of the tunnel surrounding rock is carried out by iterating the optimization algorithm to minimize the objective function value through similar model test with the displacement measurements obtained by the digital scattering correlation method as the known quantity and the nodal displacement simulation values obtained by numerical simulation as the unknown quantity. The results show the following: (1) The new research idea proposed in this paper is effective and feasible and can perform the inversion calculation of the parameters of the soft and weak tunnel surrounding rock. (2) Through the inversion calculation, the parameters related to the surrounding rock show a wave-like change: the parameters increase in the initial and final compaction stages due to the small deformation rate of the tunnel and decrease in the continuous loading stage due to the increasing deformation rate of the tunnel. (3) The displacement field calculated by DSCM in the test is basically the same as the displacement distribution characteristics of the numerical simulation displacement cloud map, and the error of nodal displacement value is within 3%.

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Section: Introductionmentioning

confidence: 99%

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Wang

Cui

et al. 2023

Advances in Civil Engineering

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“…Li et al 17 reconstructed different time‐series decomposition items as the bottom layer of the model based on the addition model under the Bayesian framework; they realized interactive modeling by combining parametric detection and intuitive parameter configuration. Gu et al 18 proposed a stochastic inverse model for solving undetermined mechanical parameters with sequence and spatial randomness by using measured displacement data and Bayesian back analysis theory. The authors 19 proposed a method to determine a displacement monitoring index of concrete dams on the basis of the parameter interval inversion correction hybrid model through the reliability monitoring index of the safety warning of concrete service; moreover, they combined interval analysis theory, rough set theory and neural network theory.…”

Section: Introductionmentioning

confidence: 99%

Hybrid model for concrete dam deformation in consideration of residual correction by frequency division

Wei

Luo

et al. 2022

Structural Contr & Hlth

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The traditional statistical models for concrete dam deformation do not consider the effective contribution to residual sequence and thus results in the low accuracy of deformation prediction of the monitoring model and in the insufficient prediction of dam deformation behavior.This study proposes a combined model for concrete dam deformation, considering residual correction by frequency division. The horizontal displacement of concrete dams is driven by water pressure, temperature, and aging factors. In this study, an accurate value of water pressure deformation is obtained by finite element calculation, and then the hybrid model is constructed. However, the temperature and aging components hardly obtain accurate information values from the traditional model. Therefore, a combined model is constructed to extract additional detailed information that characterizes the dam deformation from the residual sequence. Ensemble empirical mode decomposition is selected to analyze and denoise the residual sequence adaptively, and the consecutive mean square error is used to determine the boundary points of highand low-frequency components. Then, the combined model based on the extreme learning machine method optimized by particle swarm optimization and seasonal autoregressive integrated moving average is constructed for highand low-frequency sequences. Lastly, the prediction results of different subitems are organically integrated. Results of a case study in engineering demonstrate that the accuracy of the combined model for concrete dam deformation is higher than that of the classical numerical model, and the model can effectively overcome noise interference in the monitoring sequence. The proposed combined model for deformation is reliable for analyzing and judging the dam monitoring time series, which can provide new ideas and reference for the data processing of dam deformation monitoring.

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“…and mechanical properties (constitutive model parameters) of rockfill materials show spatial ranges with obvious uncertainty [20,21]. A large number of studies have shown that the uncertainties of dam materials have a nonnegligible impact on dam displacement, slope stability and even safety and reliability [20][21][22][23][24]. In the construction of conventional hybrid models and the determination of monitoring indexes, the mechanical parameters of rockfills are generally considered and determined so that the water pressure component obtained from the structural calculation based on the laboratory geotechnical test parameters ignores the consideration of the spatial variability of rockfill [25], and the effect of spatial variability on the time-dependent component is not considered.…”

Section: Introductionmentioning

confidence: 99%

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

Ran

Yang

Zhang³

et al. 2022

J Civil Struct Health Monit

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To further enhance the prediction accuracy of the monitoring model and improve the rationality of the method for determining the monitoring indexes, this paper considers the impact of the spatial variability of rockfill materials on the displacement of the dam. Based on the stochastic finite element method (SFEM), a new rockfill dam displacement monitoring hybrid model is established, and a method to determine the hybrid index for rockfill dam displacement monitoring is proposed by multiindex amalgamation. First, the SFEM is used to simulate the spatial variability of the rockfill mechanical parameters in the process of calculating the water pressure component and the time-dependent component. On this basis, the expression of each component is fitted to construct a stochastic finite element method hybrid model (SFEMH model) of the rockfill dam. Subsequently, by predicting the displacement of the dam and separating each displacement component, the displacement monitoring hybrid index of the rockfill dam is determined by combining the confidence interval method and the most unfavourable components. Finally, the SFEMH model and hybrid index are applied to analyse the actual monitoring data of a rockfill dam. The results show that the proposed model and the method for determining the displacement monitoring hybrid index are scientific and reasonable. The prediction accuracy and effect of the proposed novel model outperform those of other methods in terms of many evaluation indicators, and the reliability of the monitoring indexes is significantly improved. The proposed hybrid model and hybrid index provide a new method for the efficient operation management and accurate safety performance evaluation of rockfill dams.

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Stochastic Inversion Method for Concrete Dams on the Basis of Bayesian Back Analysis Theory

Cited by 8 publications

References 20 publications

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Inversion Study of Mechanical Parameters of Tunnel Surrounding Rock Based on Similar Test

Hybrid model for concrete dam deformation in consideration of residual correction by frequency division

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

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