A mathematical-mechanical hybrid driven approach for determining the deformation monitoring indexes of concrete dam. (15th February 2023)

• Record Type: Journal Article
• Authors: Zhang, Kang; Gu, Chongshi; Zhu, Yantao; Li, Yangtao; Shu, Xiaosong
• Is Part Of: Engineering structures. Volume 277(2023)
• Publication Date: 2023-02-15
• Subjects: Uncertainty quantization -- Probability inversion analysis -- Markov chain Monte Carlo -- Surrogate model -- Concrete dam -- Monitoring indexes; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.115353
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Probabilistic inversion analysis of mechanical parameters of concrete dam is realized under Bayesian framework. The establishment of surrogate model greatly improves the computational efficiency of MCMC sampling process. The deformation monitoring indexes of concrete dam based on uncertainty quantification are established. In the process of determining monitoring indexes, numerical simulation, machine learning and statistical analysis strategies are integrated together. The determined deformation monitoring indexes are more sensitive to outliers. Abstract: This paper aims at quantifying the uncertainty of mechanical parameters of the concrete dam based on prototype measured data, and puts forward a novel method for determining deformation monitoring indexes based on the quantification results of the uncertainty of deformation causes. Firstly, the deformation components are separated by the HST model for deformation of concrete dam. Secondly, on the basis of the separated hydraulic component and numerical simulation, the posterior distribution of main mechanical parameters involved in the finite element model (FEM) is updated by utilizing Markov chain Monte Carlo sampling method under Bayesian framework. Meanwhile, in order to improve the computational efficiency of the parameter calibration procedure, this study adopts a surrogate model based on the multi-layer perceptron algorithm to replace the finite element calculation. The training set of the surrogate model is generated by Latin hypercube sampling in the sample space and the optimum sample size is discussed. Thirdly, based on the probability inversion analysis results of mechanical parameters, the uncertainty quantification of hydraulic component is realized by forward analysis based on FEM. The uncertainty of temperature and aging component is characterized by confidence interval method. Then, the deformation monitoring indexes are established by integrating the uncertainty quantization results of three components. Finally, the feasibility of the proposed method is verified by using the long-term deformation monitoring data of Jinping-I arch dam. The results show that the monitoring indexes determined by this method is more sensitive to the abnormal measurement due to the uncertainty quantification of hydraulic component.