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Sensitivity analysis study of the source parameter uncertainty factors for predicting near-field strong ground motion

Li Z., Gao M., Jiang H., Chen X., Li T., Zhao X.

Acta Geophysica

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2018

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Vol. 66, no. 4

523--540

EN

Uncertainty factors have substantial influences on the numerical simulations of earthquakes. However, most simulation methods are deterministic and do not sufficiently consider those uncertainty factors. A good approach for predicting future destructive earthquakes that is also applied to probabilistic hazard analysis is studying those uncertainty factors, which is very significant for improving the reliability and accuracy of ground-motion predictions. In this paper, we investigated several uncertainty factors, namely the initial rupture point, stress drop, and number of sub-faults, all of which display substantial influences on ground-motion predictions, via sensitivity analysis. The associated uncertainties are derived by considering the uncertainties in the parameter values, as those uncertainties are associated with the ground motion itself. A sensitivity analysis confirms which uncertainty factors have large influences on ground motion predictions, based upon which we can allocate appropriate weights to those uncertainty factors during the prediction process. We employ the empirical Green function method as a numerical simulation tool. The effectiveness of this method has been previously validated, especially in areas with sufficient earthquake record data such as Japan, Southwest China, and Taiwan, China. Accordingly, we analyse the sensitivities of the uncertainty factors during a prediction of strong ground motion using the empirical Green function method. We consequently draw the following conclusions. (1) The stress drop has the largest influence on ground-motion predictions. The discrepancy between the maximum and minimum PGA among three different stations is very large. In addition, the PGV and PGD also change drastically. The Arias intensity increases exponentially with an increase in the stress drop ratio of two earthquakes. (2) The number of sub-faults also has a large influence on various ground-motion parameters but a small influence on the Fourier spectrum and response spectrum. (3) The initial rupture point largely influences the PGA and Arias intensity. We will accordingly pay additional attention to these uncertainty factors when we conduct ground-motion predictions in the future.

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An extended risk based inspection methodology

Selvik J. T., Scarf P., Aven T.

Journal of Polish Safety and Reliability Association

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2010

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Vol. 1, No. 1

221--230

EN

Risk based inspection (RBI) is a methodology commonly used in planning of inspections for static mechanical equipment, in particular piping networks. The inspections are prioritized based on risk, expressed as expected values, integrating the likelihood and consequences of failures. In this paper we suggest an extension of the RBI methodology which reflects risk and uncertainties beyond expected values. We argue that such an extension is essential for adequately supporting the inspection planning. A pipeline example from the Norwegian oil and gas industry is presented to illustrate and discuss the suggested approach.

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A new methodology of accounting for uncertainty factors in multiple criteria decision making problems

Kalika V. I.

Journal of Telecommunications and Information Technology

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2004

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nr 3

44-56

EN

A new approach is proposed to select a predetermined number of "reasonable" (the best in a certain sense) alternatives from the considerable (maybe a vast) set of initial alternatives according to an arbitrary number of optimization criteria and accounting for uncertainty factors. The approach is based on using a special intuitive methodology, developed to account for uncertainty factors when solving such multiple criteria decision making (MCDM) problems. This methodology is based on performing multi-variant computations (MVC) and finding their "stable-optimal" solutions, and it's realized as a multi-level hierarchical system of MVC series. It's possible to use this methodology for solving various real problems.