Uncertainties In Derivation Research Articles

Variance‐based global sensitivity analysis for fuzzy random structural systems

AbstractThere have been increasing advances in the sophisticated approaches like fuzzy randomness to handle different uncertainties in civil engineering; however, less attention has been paid to the formulation of a sensitivity analysis for fuzzy random structural systems. In this study, the main objective is to present the formulation of fuzzy Sobol sensitivity indices to quantify the influence of fuzzy random structural parameters. Meanwhile, uncertainty in derivation of limit states and acceptance criteria in collapse analysis is addressed briefly and treated using fuzzy model parameters. To show the application of the established sensitivity test, the collapse behavior of a steel moment frame subjected to sudden column removal is evaluated thoroughly. The proposed fuzzy sensitivity indices are determined for the problem and the overall influence of fuzzy acceptance criteria on the collapse assessment is shown using fragility analysis. The results show that the presented fuzzy sensitivity analysis can give detailed insight into the characteristics of fuzzy random systems, and the epistemic uncertainty in derivation of limit states can have significant effects on the reliability‐based collapse analysis. It is worth mentioning that to alleviate high computational demands in fuzzy probabilistic collapse analysis, a neural network metamodel is applied in conjunction with the genetic algorithm which is also of practical value to engineers and researchers.

Pre-flyby estimates of the precision of the mass determination of asteroid (21) Lutetia from Rosetta radio tracking

The Rosetta spacecraft will fly by its second target asteroid (21) Lutetia on 10 July 2010. Simulations based on the currently known size of Lutetia and assumptions on the bulk density show that tracking of two radio-carrier frequencies at <i>X<i/>-band (8.4 GHz) and <i>S<i/>-band (2.3 GHz) during the flyby will determine the mass at less than 1% accuracy. Derivation of the asteroid volume by camera observation will drive the uncertainty in derivation of the bulk density. Mass and bulk density provide valuable clues that might help resolve the difficulties in determining the taxonomic class of the asteroid.

Sensitivity Analysis of an Optimal Access Road Location in HillyForest Area: A GIS Approach

There are various models for establishing the efficient forest road allocation and evaluating optimum density of forest roads network for the transport of timber commodity. Most of them are based on the calculation of common timber transport costs and costs for forest roads. Today, forest road design from traditional method continues to be transformed by remote sensing technology and advancement of GIS. It is now possible for a forester to analyze many different road location alternatives over a large geographic area in a minimal amount of time. A computer programming using GIS, digital terrain data and sensitivity analysis for locating optimal forest road access in a hill area is presented. The optimal access road location specifies destination (starting point) and target (ending point) of the desired path. These paths were allocated by calculated each individual criteria by given weights placed on each cell. Therefore, the objective of this work is to describe an attempt to compute the optimal allocation of forest road corridor in hilly area of Peninsular Malaysia using GIS approach and sensitivity analysis to satisfy the result. Finally, the model minimizes total cost of construction and forest environmental impacts resulting from a specific access road. Sensitivity analysis should be explored further to understand the effects of uncertainty in derivation of model parameter on model outputs. It is concluded that the location of optimal access path were established in the area can reduce the cost and environmental impact to the forest ecosystem.

Propagating data uncertainty through smoothing spline fits

Smoothing splines have been used extensively in the analysis of gas concentration data from bubbles in ice cores. Since the fit is a linear projection of the data, propagation of data uncertainty through the fitting process is formally straightforward and, as we demonstrate, readily achievable from pre-existing spline-fitting procedures. The uncertainty propagation can be extended to determining both uncertainties in derivatives and uncertainties in quantities that reflect rates of input to the atmosphere. As an example, we apply the technique to 1000 yr of methane data from a Law Dome ice core.

GIUH based Clark and Nash models for runoff estimation for an ungauged basin and their uncertainty analysis

Runoff estimation is an important aspect of river basin planning, development and management. Geomorphological instantaneous unit hydrograph (GIUH) approach holds a great potential for estimation of runoff from ungauged basins because of its direct application to ungauged basins, without going for cumbersome procedure of regionalization. In this study, GIUH is derived from the geomorphological characteristics of a basin and it is related to the parameters of the Clark instantaneous unit hydrograph (IUH) model as well as Nash IUH model for deriving its complete shape. The developed GIUH based Clark and Nash models are applied for simulation of the direct surface runoff (DSRO) hydrographs for ten rainfall‐runoff events of the Ajay basin upto Sarath gauging site of eastern India. The geomorphological characteristics of the Ajay basin are evaluated using the GIS package, Integrated Land and Water Information System (ILWIS). A comparison of the performances of the GIUH based Clark and Nash models in simulating the DSRO hydrographs by employing some of the commonly used error functions reveals that the DSRO hydrographs are computed with reasonable accuracy by both the models, which simulate the DSRO hydrographs of the basin considering it to be ungauged. The relative sensitivity and the degree of uncertainty associated with each of the input parameters of the GIUH based Clark and Nash models have been quantified. The uncertainty analysis reveals that apart from the velocity (V) parameter, the length of the highest order stream (LO) is the only geomorphological parameter which results in higher degree of uncertainty in derivation of unit hydrograph using the GIUH based Clark and Nash models, and hence, it is to be evaluated with greater precision for accurate runoff estimation from ungauged basins.

Application of health information to hazardous air pollutants modeled in EPA's Cumulative Exposure Project.

Relatively little is known about the spectrum of health effects, and the scope and level of ambient air concentrations of those pollutants regulated under the Clean Air Act as "hazardous air pollutants". The U.S. Environmental Protection Agency's (USEPA) Cumulative Exposure Project uses currently available emissions inventories, from a variety of source types, and an atmospheric dispersion model to provide estimates of ambient concentrations for 148 hazardous air pollutants (HAPs) in over 60,000 census tracts for the year 1990. This paper uses currently available hazard information for those pollutants and provides a database of potential regulatory threshold concentrations of concern, or "benchmark concentrations," and a methodology for prioritizing and characterizing the quality of the data. In order to demonstrate application of the database and prioritization scheme to outputs from the Cumulative Exposure Project, comparisons were made with the maximum modeled concentration of each individual hazardous air pollutant across the census tracts. Of the 197 benchmark concentrations for cancer and non-cancer (long- and short-term exposures) effects compiled for the study, approximately one half were exceeded with a predominance of exceedance of cancer benchmarks. While the number of benchmark concentrations available to fully characterize potential health effects of these pollutants was limited (approximately 80 percent of HAPs identified as cancer concerns had benchmark concentrations for cancer and 50 percent of all HAPs had non-cancer benchmark concentrations) and there was greater uncertainty in derivation of maximum modeled air concentrations than other levels, the comparison between the two was a useful approach for providing an indication of public health concern from hazardous air pollutants.

Uncertain Value Functions

Value theory plays an essential role in the formal solution of decision problems, but the experimental determination of value functions is subject to errors of various kinds. Sensitivity analyses are methodologically invalid. An attempt is made to overcome the uncertainties in derivation of value functions, in a manner which is similar to the economist's “Pareto optimality” approach. Results are presented for the 2-dimensional situation, and extended to the more general n-dimensional case. In the case when preferences and indifferences are certain the usual results follow. It is shown how the results can be used to cut down the region of initial feasible solutions to a region within which the decisionmaker makes no initial discrimination, when the boundary regions are convex. A comparison is made with the approaches of Luce and Aumann, whose work is of a similar, but not equivalent, nature.