in this paper, using numerical methods and test results, the behavior of reinforced concrete beams strengthened with fiber-reinforced polymers (frp) is investigated. in the numerical analysis, the concrete damaged plasticity model is used for considering the nonlinear behavior and strain softening of concrete. the abaqus finite element program is utilized for the analysis of 12 flexural beams s...

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Causal inference with interference is a rapidly growing area. The literature has begun to relax the "no-interference" assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which...

Due to several uncertainties  which  affect structural responses of Reinforced concrete (RC) frames,  it is sensibly required  to  apply  a vulnerability analysis tool such as fragility curve. To  construct an analytical fragility curve, the incremental dynamic analysis (IDA) method has been extensively used as an applicable seismic analysis too...

One of the issues in Data Envelopment Analysis (DEA) is sensitivity and stability region of the speci c decision making unit (DMU), included ecient and inecient DMUs. In sensitivity analysis of ecient DMUs,the largest region should be found namely stability region thatdata variations are only for ecient DMU under evaluation and the data for the remainingDMUs are assumed xed. Also ecient DMU u...

Transit network conditions change everyday. While those changes should be considered for modifying a transit network, formulating and optimizing the whole transit network may be a costly, difficult task. This article uses a developed transit network design model to examine how optimal transit networks should be developed based on changes in input elements of the transit network. Three major inp...

We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined k-th PSM to define a basis for the input space. We train neur...

The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudo-operational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance. For a single observation, expression...