Polynomial chaos expansion for uncertainty quantification of dam engineering problems

Hariri-Ardebili, Mohammad Amin; Sudret, Bruno

doi:10.1016/j.engstruct.2019.109631

Cited by 64 publications

(23 citation statements)

References 41 publications

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“…The finite element (FE) model response related to water level variation was approximated via PCE method, and used for calibration. Hariri-Ardebili and Sudret [14] investigated the application of PCE meta-model in uncertainty quantification (UQ) of different implicit and explicit dam engineering problems. They found that PCE can develop a surrogate model with a very limited number of initial simulations and reduce the computational time considerably.…”

Section: Literature Reviewmentioning

confidence: 99%

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An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

Hariri-Ardebili

Mahdavi

Abdollahi

et al. 2021

Water

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Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue problem. The situation becomes even more complicated if the material distribution is assumed to be heterogeneous within the dam body (as opposed to conventional isotropic homogeneous relationship). This paper proposes a hybrid Random Field (RF)–Polynomial Chaos Expansion (PCE) surrogate model for uncertainty quantification and sensitivity assessment of dams. For different vibration modes, the most sensitive spatial locations within dam body are identified using both Sobol’s indices and correlation rank methods. Results of the proposed hybrid model is further validated using the classical random forest regression method. The outcome of this study can improve the results of system identification and dynamic analysis by properly determining the vibration characteristics.

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Section: Literature Reviewmentioning

confidence: 99%

“…The main objective in PCE framework is to determine the expansion coefficients [14]. In this paper, a non-intrusive approach is adopted which relies on post-processing the outputs resulted from simulation-based methods [27].…”

Section: Polynomial Chaos Expansionmentioning

confidence: 99%

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

Hariri-Ardebili

Mahdavi

Abdollahi

et al. 2021

Water

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“…Table 2 presents the experimental data. Applying the uncertainty expansion theory [1] , [2] , [3] , [4] and using the certificate of calibration [5] was possible to calculate the uncertainty associated to the standard mass. Moreover, Table 2 shows, in highlight, the indicated mass by the analytical scale, the apparent mass and its uncertainty.…”

Section: Data Descriptionmentioning

confidence: 99%

Evaluation of the metrological reliability of a graduated cylinder from experimental data from an in-situ calibration

Vásquez¹,

Yepes²,

Rodriguez-Salas³

et al. 2020

Data in Brief

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This work presents the data experimentally collected in a chemical laboratory for the calibration of a graduated cylinder. There are several factors that can influence the volume measurement using this type of instrument and, consequently, its metrological reliability, for example: the internal geometry, the environmental conditions (ambient temperature, atmospheric pressure and relative humidity), the acceleration of gravity, the density of the air, among others. For the data collection it was necessary to use a glass liquid thermometer (Range: 0–10 °C), a digital thermohygrometer (Range: 0–100 °C and 0–99%RH) and a digital barometer (Range: 0–9999 mbar). Additionally, an analytical scale (Range: 0–220 g) was used for mass measurement. From the measurements obtained, it was possible to determine the in-situ air density and the buoyancy factor that influences the mass measurement. The data, rigorously obtained, present a potential use to determine the metrological reliability of a graduated cylinder for laboratory use and, additionally, contribute to perform a metrological validation of alternative methods for the calibration of graduated cylinder.

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“…Clearly the S-N curve method can accurately predict the fatigue life based on the above research results [ 20 ]. However, deterministic models fail to account for uncertainty inherent in the monitoring data and interpretation of the model error [ 21 ]. Therefore, probabilistic models based on S-N curve are required.…”

Section: Introductionmentioning

confidence: 99%

Failure Evaluation of Bridge Deck Based on Parallel Connection Bayesian Network: Analytical Model

et al. 2021

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Failure is a major element that causes deterioration, which in turn affects the serviceability of long span bridges. Currently, the Bayesian network, which relates to probability statistics, is widely used for evaluating fatigue failure reliability. In particular, Bayesian network can not only calculate the fatigue failure at the system level, but also deduce the fatigue failure at the weld level. In this study, a system-level fatigue reliability evaluation model of a bridge deck (BD), which is seen as a parallel system, is proposed based on the Bayesian network. A fatigue probability reliability model of the BD was derived using the master S-N curve. In addition, the Monte Carlo (MC) method was applied to solve the multi-dimensional and complex analytical expressions in the Bayesian network. The applicability of the proposed model was demonstrated by three numerical case studies.

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Polynomial chaos expansion for uncertainty quantification of dam engineering problems

Cited by 64 publications

References 41 publications

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

Evaluation of the metrological reliability of a graduated cylinder from experimental data from an in-situ calibration

Failure Evaluation of Bridge Deck Based on Parallel Connection Bayesian Network: Analytical Model

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