Probabilistic Identification of Seismic Response Mechanism in a Class of Similar Arch Dams

Hariri-Ardebili, Mohammad Amin; Heshmati, M.; Boodagh, Parnaz; Salamon, J. W.

doi:10.3390/infrastructures4030044

Cited by 6 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OBE level seismic forces are known as unusual loads, whereas MDE and MCE seismic forces are regarded as extreme loads. Seismic earthquake loads must be unified with multiple loads for design purposes expected to be available during routine operations [2][3][4][5]. The damage starts from the heel within the gravity dam's body and then extends over the dam's upstream material and progresses to its downstream face [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Improving Resistance of Gravity Dam to Seismic Wave by Using Stepped Base

2021

View full text Add to dashboard Cite

This study analyzes the possible modes of the seismic wave for concrete gravity dams and the damage occurring in the body where two gravity dam sections are taken as a case study. Model (1) is a typical section of a gravity dam, while model (2) is a gravity dam section provided with specified steps at its base. The design of seismic waves included Operational Base Earthquake (OBE), Maximum Design Earthquake (MDE), and Maximum Credible Earthquake (MCE) levels. The dam body-foundation-reservoir water interaction system is analyzed by numerical analysis software (ABAQUS). The main conclusion shows that the gravity dam model's underground motion loadings have a noticeable increase in the displacement distribution within the first gravity dam body model, especially in the higher mode compared to the first model. The first model's Damage Index value was equal to 0.357 at the MCE wave, which does not appear in the second model. The time taken for the damage that occurs within a model (1) is faster than the model (2). Consequently, model (2) significantly reduces seismic waves with different intensity levels on the amount of damage transmission within the gravity dam body.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving Resistance of Gravity Dam to Seismic Wave by Using Stepped Base

2021

View full text Add to dashboard Cite

show abstract

“…The parameters of an elastic-damage interface model are also identified by Corigliano et al [55,56] where the extended Kalman filter method is applied. Further, Ebrahimian et al and Hariri-Ardebili et al [57][58][59][60][61][62] investigated the damage parameter identification in the framework of structural health monitoring by using an extended version of Kalman filter. Damage detection for the purpose of health monitoring is also done by Yan et al [63] by using the Kalman filter and other stochastic approaches by Kourehli et al and Gharehbaghi et al [64][65][66].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Adeli

Rosić

Matthies

et al. 2020

Metals

View full text Add to dashboard Cite

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

show abstract

“…Also Sevieri et al [44][45][46] investigated the parameter identification of structural models in the framework of structural health monitoring and Marsili et al [47][48][49][50][51] investigated the update of a finite element model using functional approximation of the system response. Further, Hariri-Ardebili et al [52][53][54] and Pouraminian et al [55][56][57] studied the finite element updating of mechanical models and reliability analysis for the concrete structures by using the functional approximation. Also Kourehli et al and Gharehbaghi et al [58][59][60] employed stochastic approaches for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error

Adeli

Rosić

Matthies

et al. 2020

Metals

View full text Add to dashboard Cite

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consuming finite element model updating problems among filtering and random walk approaches. The parameters identified using this Bayesian approach are compared with the true parameters in the simulation, and further, the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and to see how much information is indeed needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collapse.

show abstract

Probabilistic Identification of Seismic Response Mechanism in a Class of Similar Arch Dams

Cited by 6 publications

References 24 publications

Improving Resistance of Gravity Dam to Seismic Wave by Using Stepped Base

Improving Resistance of Gravity Dam to Seismic Wave by Using Stepped Base

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error

Contact Info

Product

Resources

About