Structural identification in long-term deformation characteristic of dam foundation using meta-heuristic optimization techniques

Lin, Chaoning; Li, Tongchun; Chen, Siyu; Lin, Chih-Hong; Liu, Xiaoqing; Gao, Lingang; Sheng, Taozhen

doi:10.1016/j.advengsoft.2020.102870

Cited by 34 publications

(12 citation statements)

References 47 publications

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“…e latter is applied in the next sate to evaluate the result of the current network and guides the update of the actor network. e update mode of the q value is shown in equation (10). θ of actor is copied to θ − of critic at a certain interval of time steps.…”

Section: Optimization Capability Of Dqn According Tomentioning

confidence: 99%

“…e former is to map the linear or nonlinear relation between environmental loads and displacement by establishing a regress model [7][8][9], whose target is predicting the status of the engineering and environment nearby in the future. e latter is to check the strength and the stability according to the mechanical parameters of structures or foundations by calculating the data of structural operating state combined with the data of the environmental variation [10].…”

Section: Introductionmentioning

confidence: 99%

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Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm

Liu

et al. 2020

Mathematical Problems in Engineering

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During the long-term operating period, the mechanical parameters of hydraulic structures and foundation deteriorated gradually because of the environmental factors. In order to evaluate the overall safety and durability, these parameters should be calculated by some accurate analysis methods, which are hindered by slow computational efficiency and optimization performance. The improved deep Q-network (DQN) algorithm combined with the deep neural network (DNN) surrogate model was proposed in this paper to ameliorate the above problems. Through the study cases of different zoning in the dam body and the actual engineering foundation, it is shown that the improved DQN algorithm has a good application effect on inversion analysis of material mechanical parameters in this paper.

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Section: Optimization Capability Of Dqn According Tomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm

Liu

et al. 2020

Mathematical Problems in Engineering

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“…This work aims to solve two issues: (1) establish a surrogate model-based inversion framework to identify multiple viscoelastic parameters in an accurate and efficient manner, and (2) predict the long-term deformation characteristic of the dam using a physics-based model with calibrated parameters. The case study is a concrete dam located in Cambodia that has been gradually deformed over its operation period [39]. The Burgers constitutive model is applied to fit the creep data and to describe the viscoelastic properties of the dam foundation.…”

Section: Introductionmentioning

confidence: 99%

Long-term viscoelastic deformation monitoring of a concrete dam: A multi-output surrogate model approach for parameter identification

Lin

Chen

et al. 2022

Engineering Structures

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“…In the secondary stage, deformation is almost uniform and creep rate remains constant. In the tertiary stage, deformation and creep rate increases rapidly until the material ruptures after undergoing a total of strain within a period of time 3 , 4 . Therefore, the modeling research on creep performance has great theoretical significance.…”

Section: Introductionmentioning

confidence: 99%

Creep modeling of composite materials based on improved gene expression programming

Hua

Yan

Zhu

et al. 2022

Sci Rep

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In this article, a new method for creep modeling and performance prediction of composite materials is presented. Since Findley power-law model is usually suitable for studying one-dimensional time-dependent creep of materials under low stress, an intelligent computing method is utilized to derive three temperature-related sub-functions, the creep model as a function of time and temperature is established. In order to accelerate convergence rate and improve solution accuracy, an improved gene expression programming (IGEP) algorithm is proposed by adopting the probability-based population initialization and semi-elite roulette selection strategy. Based on short-term creep data at seven temperatures, a bivariate creep model with certain physical significance is developed. At fixed temperature, the univariate creep model is acquired. R2, RMSE, MAE, RRSE statistical metrics are used to verify the validity of the developed model by comparison with viscoelastic models. Shift factor is solved by Arrhenius equation. The creep master curve is derived from time–temperature superposition model, and evaluated by Burgers, Findley and HKK models. R-square of IGEP model is above 0.98 that is better than classical models. Moreover, the model is utilized to predict creep values at t = 1000 h. Compared with experimental values, the relative errors are within 5.2%. The results show that the improved algorithm can establish effective models that accurately predict the long-term creep performance of composites.

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Structural identification in long-term deformation characteristic of dam foundation using meta-heuristic optimization techniques

Cited by 34 publications

References 47 publications

Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm

Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm

Long-term viscoelastic deformation monitoring of a concrete dam: A multi-output surrogate model approach for parameter identification

Creep modeling of composite materials based on improved gene expression programming

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