Seismic Behavior of Pine Flat Concrete Gravity Dam Using Microplane Damage-Plasticity Model

Vorlet, Samuel; Manso, Pedro; Cesare, Giovanni De

doi:10.1007/978-3-030-51085-5_19

Cited by 1 publication

(2 citation statements)

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“…The data analyzed in this paper is derived from the findings presented in the following publications. [86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104] Table 2 provides an overview of key modeling variations observed across each study. These modeling variabilities encompass factors such as mesh type (2D, 3D slice, full 3D), mesh size (which holds significance for DI assessment), time step utilized in the numerical simulation, criteria for convergence, the specific foundation model and its associated boundary conditions for wave propagation and absorption (including considerations for the free-field), choice of fluid element and approach for FSI, the selected concrete constitutive model and its response to dynamic loading-induced damage, the time integration scheme employed for stability control, and finally, the software package employed for analysis.…”

Section: Analysis and Synthesis Of Resultsmentioning

confidence: 99%

“…The following scalar and vector quantities were analyzed: crest and heel displacements, relative crest displacement compared to heel, net hydrodynamic pressure at the dam's heel, ratio of crest‐to‐heel amplitude acceleration spectrum, crack profile, area‐based and length‐based damage index (DI), and failure time (for IAA only). The data analyzed in this paper is derived from the findings presented in the following publications 86–104 . Table 2 provides an overview of key modeling variations observed across each study.…”

Section: Analysis and Synthesis Of Resultsmentioning

confidence: 99%

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Quantifying modeling uncertainties in seismic analysis of dams: Insights from an international benchmark study

Hariri‐Ardebili

2023

Earthq Engng Struct Dyn

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Advances in nonlinear dynamic analysis of dams have not completely resolved concerns over modeling confidence and analysis accuracy. Verification and validation offer accuracy assessment, but uncertainties persist during performance evaluation due to both epistemic (modeling) and aleatory (parametric) sources. Epistemic uncertainties arise from simplifications and modeling techniques. This paper addresses epistemic uncertainties in a gravity dam seismic analysis using data from the International Comnission on Large Dams (ICOLD) benchmark study. While the benchmark formulation included the finite element model of the dam, mechanical material properties, and dynamic loads, participants retained the flexibility to opt for best‐practice modeling assumptions, simplifications, and other specifics. Notable response variability emerged, particularly in crack profiles and damage predictions. This study examines sources of variability, quantifying modeling uncertainty for the benchmark problem. More specifically, the modeling variability is quantified using the logarithmic standard deviation, also known as dispersion. This metric enables its incorporation into other seismic risk assessment and fragility studies. Under relatively low‐intensity motion (peak ground acceleration [PGA] of 0.18 g in this case), modeling dispersion of 0.45, 0.30, 0.32, and 0.30 were calculated for the maximum dynamic crest displacement, maximum hydrodynamic pressure at the heel, heel and crest maximum acceleration, respectively. Additionally, the dispersion of the failure PGA was determined to be 0.7. Findings underscore the need for systematic seismic response modeling in dam engineering to enhance prediction accuracy. A better understanding of the sources and magnitudes of modeling uncertainties can help improve the reliability of dam seismic analysis and contribute to the development of more effective risk assessment and mitigation strategies.

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