{'fraction_non_alphanumeric': 0.013469827586206896, 'fraction_numerical': 0.0021551724137931034, 'mean_word_length': 5.655913978494624, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Seismic performance sensitivity and uncertainty analysis of gravity dams

Uncertainties in structural engineering are often arising from the modeling assumptions and errors, or from variability in input loadings. A practical approach for dealing with them is to perform sensitivity and uncertainty analysis in the framework of stochastic and probabilistic methods. These analyses can be statically and dynamically performed through nonlinear static pushover and IDA techniques, respectively. Of the existing structures, concrete gravity dams are infrastructures which may encounter many uncertainties. In this research, probabilistic analysis of the seismic performance of gravity dams is presented. The main characteristics of the nonlinear tensile behavior of mass concrete, along with the intensity of earthquake excitations are considered as random variables in the probabilistic analysis. Using the tallest non‐overflow monolith of the Pine Flat gravity dam as a case study, its response under static and dynamic situations is reliably examined utilizing different combinations of parameters in the material and the seismic loading. The sensitivity analysis reveals the relative importance of each parameter independently. It will be shown that the undamaged modulus of elasticity and tensile strength of mass concrete have more significant roles on the seismic resistance of the dam than the ultimate inelastic tensile strain. In order to propagate the parametric uncertainty to the actual seismic performance of the dam, probabilistic simulation methods such as Monte Carlo simulation with Latin hypercube sampling, and approximate moment estimation techniques will be used. The final results illustrate the possibility of using a mean‐parameter dam model to estimate the mean seismic performance of the dam. Copyright © 2014 John Wiley & Sons, Ltd.