DOCSLIB.ORG

The Utilization of Load and Resistance Statistics in A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Utilization of Load and Resistance Statistics in A THE UTILIZATION OF LOAD AND RESISTANCE STATISTICS IN A WIND SPEED ASSESSMENT by TIMOTHY PATRICK MARSHALL, B.S., M.S. A THESIS IN CIVIL ENGINEERING Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN CIVIL ENGINEERING Approved / IXrre^nff^rí May, 1983 '7--V.. ACKNOWLEDGMENTS I would like to thank Dr. James McDonald for his support and en- couragement throughout my stay in the Civil Engineering program as well as his constructive comments and guidance in helping me complete my thesis. I would also like to express my appreciation to the rest of my committee, Drs. Joseph Minor, Kishor Mehta and Richard Peterson for their suggestions on my thesis and knowledge gained both in and out of the classroom. It has also been a pleasure to be a part of the Institute for Disaster Research and to be on the forefront of engineering research. My thanks and love also go to my wife, Kay, for her love, patience and encouragement and for believing in me. I dedicate this thesis to the memory of Dr. Jack Villmow: a teacher and friend who devoted his life to meteorology and who inspired me to seek higher education in the sciences. n ACKNOWLEDGMENTS I would like to thank Dr. James McDonald for his support and en- couragement throughout my stay in the Civil Engineering program as well as his constructive comments and guidance in helping me complete my thesis. I would also like to express my appreciation to the rest of my committee, Drs. Joseph Minor, Kishor Mehta and Richard Peterson for their suggestions on my thesis and knowledge gained both in and out of the classroom. It has also been a pleasure to be a part of the Institute for Disaster Research and to be on the forefront of engineering research. My thanks and love also go to my wife, Kay, for her love, patience and encouragement and for believing in me. I dedicate this thesis to the memory of Dr. Jack Villmow: a teacher and friend who devoted his life to meteorology and who inspired me to seek higher education in the sciences. n TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ii LIST OF TABLES vi LIST OF FIGURES vii I. INTRODUCTION 1 1.1 Background 1 1.2 Purpose 2 1.3 Outline 2 II. THE CONCEPT OF VARIABILITY 3 2.1 Factor of Safety 3 2.2 Load and Resistance Equations 5 2.3 Binary Operations 6 2.4 Confidence Levels 11 III. STRUCTURAL RESISTANCE STATISTICS 13 3.1 Steel 13 3.1.1 Steel Sections 13 3.1.2 Bending Resistance 14 3.1.3 Steel Connections 16 3.2 Concrete 18 3.3 Masonry 20 3.3.1 Tensile Bond Strength 20 3.3.2 Strength Properties of Masonry 23 3.4 Wood 24 3.4.1 Nailed Connections 28 3.5 Window Glass 28 m Page 3.5.1 Types of Window Glass 30 3.5.2 Window Glass Strength 30 IV. WIND LOAD STATISTICS 34 4.1 Wind Load Variables 34 4.1.1 The Air Density Term 34 4.1.2 Pressure Coefficients 34 4.1.3 Pressure Coefficient Reduction 36 4.2 Summary of Load and Resistance Statistics 39 4.3 Load and Resistance Wind Speed Compared to F-scale. 42 V. WIND SPEED CALCULATIONS USING LOAD AND RESISTANCE STATISTICS 50 5.1 Altus Tornado Damage Path 50 5.2 Parachute Drying Tower Wind Speed Calculation ... 50 5.2.1 Overall Wind Loading 54 5.3 Dining Hall Wind Speed Calculation 57 5.3.1 Cafeteria Roof 58 5.3.2 Roof Over Offices 64 5.3.3 Interior Roof Section 66 5.4 Recreation Building Wind Speed Calculation .... 67 5.4.1 Windward Roof Corner 67 5.4.2 Windward Roof Section 72 5.5 Communication Building Wind Speed Calculation ... 73 5.5.1 Windward Roof 76 5.5.2 Windward Wall 79 5.6 Summary 80 IV Page VI. CONCLUSIONS 82 6.1 General Conclusions 82 6.2 Future Research 83 LIST OF REFERENCES 84 APPENDIX I 87 LIST OF TABLES Table Page 1 Summary of Binary Operations for Combining Two Normal Probability Distributions 8 2 Factors Which Affect the Strength of Steel 15 3 Summary of Resistance Statistics for Steel 17 4 Factors Which Affect the Strength of Concrete 19 5 Summary of Resistance Statistics for Concrete 21 6 Factors Which Affect the Strength of Masonry Walls . 22 7 Summary of Resistance Statistics for Masonry 25 8 Factors Which Affect the Strength of Wood 26 9 Summary of Resistance Statistics for Wood 27 10 Summary of Resistance Statistics for Nailed Connections . 29 11 Factors Which Affect the Strength of Window Glass ... 31 12 Summary of Resistance Statistics for Window Glass ... 33 13 Summary of Wind Load Variable Statistics 40 14 Credence Levels for Wind Speed Calculations Based on Variability 41 15 Limits in Accuracy in Wind Speed for Various Materials. 43 16 Factors Which can Influence the F-scale Rating .... 45 17 Comparison of F-scale Wind Speed to Load and Resistance Wind Speed 47 18 Comparison of the Performance of Housing Wind Speed to Load and Resistance Wind Speed 49 19 Characteristics of the Altus Tornado 52 20 Summary of Wind Speed Estimates in the Altus Tornado . 81 21 Summary of Strength Data for 8d Nailed Connections ... 90 22 Summary of Strength Data for 16d Nailed Connections. 91 vi LIST OF FIGURES Figures Page 1 A typical load and resistance probability distribu- tion. The shaded region indicates the probability of failure 4 2 Mean GC ^ values for flat roof and wall sections .... 38 3 Tornado damage path through the Altus Air Force Base. Gradations of damage is shown by F-scale 51 4 Plan view of the Parachute Drying Tower and Building 279 . 53 5 Wind loading on the Parachute Drying Tower at Altus Air Force Base 56 6 Plan view of Dining Hall showing windward roof damage (unshaded portion) 59 7 Windward roof-wall detail of the Dining Hall at section A . 60 8 Plan view of the roof sections of the Dining Hall over a) cafeteria and b) offices as divided into ANSI-A58.1 1982 wind regimes 61 9 Plan view of the Recreation Building showing windward roof corner damage (unshaded portion) 68 10 Windward roof-wall detail of the Recreation Building at section B 69 11 Plan view of the failed roof section of the Recreation Building as divided into ANSI-A58.1 1982 wind regimes . 70 12 Plan view of the Communications Building showing windward roof damage (unshaded portion) 74 13 Windward roof-wall detail of the Communications Building . 75 14 Sections of the Communications Building used to determine wind speed calculations a) roof as divided into ANSI-A58.1 1982 wind regimes and b) windward wall 78 15 The types of nailed connections used in the tensile tests a) toenailed and b) straight. Nails were driven into grade two, 2x4, white pine lumber 88 16 View of the Reihle testing machine used to pull apart the nailed connections 89 vn• • CHAPTER I INTRODUCTION 1.1 Background A significant natural disaster in terms of a wind event provides an opportunity for engineers and meteorologists to conduct a damage survey and assess the performance of structures in the path of the storm. The knowledge gained in estimating wind speeds responsible for causing the damage can be used to better design structures to resist the wind. Before 1950, few studies appeared in the literature on wind speed estimates derived from damaged structures. Most notably, the Dallas tornado in 1957 provided the first significant opportunity to study the damage caused to many structures with different types of construc- tion. Segner (1960) presented wind speed estimates from an engineering analysis of the damaged structures. More recently, wind speed estimates have been derived from engineering analyses of damage caused by the Lubbock tornado (Minor et al., 1971) and the Xenia, Ohio tornado (Mehta et al., 1974). In all the above cases, a wind speed was calcu- lated based on average characteristics of the structure. A problem arises in that several variables may be responsible for deviations from the calculated wind speed. Material strength properties, construction practices, orientation of the structure and pressure co- efficients are a few of the variables which are subject to uncertainties. These variables may, in turn, lead to an overestimate or underestimate of the calculated wind speed to cause the damage. In order to quantify the degree of error in a wind speed estimate, statistical properties of the load and resistance behavior of the structure must be known. Within the past several years, a new concept has emerged in the design of engineered structures which enables engineers to account for the variations in the strength and types of loading on structures. The concept is termed Load and Resistance Factor Design (LRFD). LRFD treats the load and resistance properties of structures in terms of continuous probability distributions. In contrast, nominal strengths or loads are discrete values. 1 2 1.2 Purpose The purpose of this study is to utilize recently published load and resistance data in conjunction with statistical methods to place a degree of confidence in a calculated wind speed. 1.3 Outline The theory of variability applicable to load and resistance equa- tions is discussed in Chapter II. Structural resistance statistics are presented in Chapter III along with a brief discussion of the major un- certainties. Chapter IV contains wind load statistics. The methodology for handling highly fluctuating pressure coefficients in a turbulent wind flow is addressed.
Load more

Recommended publications
  • Chapter 6: Random Errors in Chemical Analysis
    Chapter 6: Random Errors in Chemical Analysis Source slideplayer.com/Fundamentals of Analytical Chemistry, F.J. Holler, S.R.Crouch Random errors are present in every measurement no matter how careful the experimenter. Random, or indeterminate, errors can never be totally eliminated and are often the major source of uncertainty in a determination. Random errors are caused by the many uncontrollable variables that accompany every measurement. The accumulated effect of the individual uncertainties causes replicate results to fluctuate randomly around the mean of the set. In this chapter, we consider the sources of random errors, the determination of their magnitude, and their effects on computed results of chemical analyses. We also introduce the significant figure convention and illustrate its use in reporting analytical results. 6A The nature of random errors - random error in the results of analysts 2 and 4 is much larger than that seen in the results of analysts 1 and 3. - The results of analyst 3 show outstanding precision but poor accuracy. The results of analyst 1 show excellent precision and good accuracy. Figure 6-1 A three-dimensional plot showing absolute error in Kjeldahl nitrogen determinations for four different analysts. Random Error Sources - Small undetectable uncertainties produce a detectable random error in the following way. - Imagine a situation in which just four small random errors combine to give an overall error. We will assume that each error has an equal probability of occurring and that each can cause the final result to be high or low by a fixed amount ±U. - Table 6.1 gives all the possible ways in which four errors can combine to give the indicated deviations from the mean value.
    [Show full text]
  • An Approach for Appraising the Accuracy of Suspended-Sediment Data
    An Approach for Appraising the Accuracy of Suspended-sediment Data U.S. GEOLOGICAL SURVEY PROFESSIONAL PAl>£R 1383 An Approach for Appraising the Accuracy of Suspended-sediment Data By D. E. BURKHAM U.S. GEOLOGICAL SURVEY PROFESSIONAL PAPER 1333 UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON, 1985 DEPARTMENT OF THE INTERIOR DONALD PAUL MODEL, Secretary U.S. GEOLOGICAL SURVEY Dallas L. Peck, Director First printing 1985 Second printing 1987 For sale by the Books and Open-File Reports Section, U.S. Geological Survey, Federal Center, Box 25425, Denver, CO 80225 CONTENTS Page Page Abstract ........... 1 Spatial error Continued Introduction ....... 1 Application of method ................................ 11 Problem ......... 1 Basic data ......................................... 11 Purpose and scope 2 Standard spatial error for multivertical procedure .... 11 Sampling error .......................................... 2 Standard spatial error for single-vertical procedure ... 13 Discussion of error .................................... 2 Temporal error ......................................... 13 Approach to solution .................................. 3 Discussion of error ................................... 13 Application of method ................................. 4 Approach to solution ................................. 14 Basic data .......................................... 4 Application of method ................................ 14 Standard sampling error ............................. 4 Basic data ........................................
    [Show full text]
  • Approximating the Distribution of the Product of Two Normally Distributed Random Variables
    S S symmetry Article Approximating the Distribution of the Product of Two Normally Distributed Random Variables Antonio Seijas-Macías 1,2 , Amílcar Oliveira 2,3 , Teresa A. Oliveira 2,3 and Víctor Leiva 4,* 1 Departamento de Economía, Universidade da Coruña, 15071 A Coruña, Spain; [email protected] 2 CEAUL, Faculdade de Ciências, Universidade de Lisboa, 1649-014 Lisboa, Portugal; [email protected] (A.O.); [email protected] (T.A.O.) 3 Departamento de Ciências e Tecnologia, Universidade Aberta, 1269-001 Lisboa, Portugal 4 Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile * Correspondence: [email protected] or [email protected] Received: 21 June 2020; Accepted: 18 July 2020; Published: 22 July 2020 Abstract: The distribution of the product of two normally distributed random variables has been an open problem from the early years in the XXth century. First approaches tried to determinate the mathematical and statistical properties of the distribution of such a product using different types of functions. Recently, an improvement in computational techniques has performed new approaches for calculating related integrals by using numerical integration. Another approach is to adopt any other distribution to approximate the probability density function of this product. The skew-normal distribution is a generalization of the normal distribution which considers skewness making it flexible. In this work, we approximate the distribution of the product of two normally distributed random variables using a type of skew-normal distribution. The influence of the parameters of the two normal distributions on the approximation is explored. When one of the normally distributed variables has an inverse coefficient of variation greater than one, our approximation performs better than when both normally distributed variables have inverse coefficients of variation less than one.
    [Show full text]
  • Analysis of the Coefficient of Variation in Shear and Tensile Bond Strength Tests
    J Appl Oral Sci 2005; 13(3): 243-6 www.fob.usp.br/revista or www.scielo.br/jaos ANALYSIS OF THE COEFFICIENT OF VARIATION IN SHEAR AND TENSILE BOND STRENGTH TESTS ANÁLISE DO COEFICIENTE DE VARIAÇÃO EM TESTES DE RESISTÊNCIA DA UNIÃO AO CISALHAMENTO E TRAÇÃO Fábio Lourenço ROMANO1, Gláucia Maria Bovi AMBROSANO1, Maria Beatriz Borges de Araújo MAGNANI1, Darcy Flávio NOUER1 1- MSc, Assistant Professor, Department of Orthodontics, Alfenas Pharmacy and Dental School – Efoa/Ceufe, Minas Gerais, Brazil. 2- DDS, MSc, Associate Professor of Biostatistics, Department of Social Dentistry, Piracicaba Dental School – UNICAMP, São Paulo, Brazil; CNPq researcher. 3- DDS, MSc, Assistant Professor of Orthodontics, Department of Child Dentistry, Piracicaba Dental School – UNICAMP, São Paulo, Brazil. 4- DDS, MSc, Full Professor of Orthodontics, Department of Child Dentistry, Piracicada Dental School – UNICAMP, São Paulo, Brazil. Corresponding address: Fábio Lourenço Romano - Avenida do Café, 131 Bloco E, Apartamento 16 - Vila Amélia - Ribeirão Preto - SP Cep.: 14050-230 - Phone: (16) 636 6648 - e-mail: [email protected] Received: July 29, 2004 - Modification: September 09, 2004 - Accepted: June 07, 2005 ABSTRACT The coefficient of variation is a dispersion measurement that does not depend on the unit scales, thus allowing the comparison of experimental results involving different variables. Its calculation is crucial for the adhesive experiments performed in laboratories because both precision and reliability can be verified. The aim of this study was to evaluate and to suggest a classification of the coefficient variation (CV) for in vitro experiments on shear and tensile strengths. The experiments were performed in laboratory by fifty international and national studies on adhesion materials.
    [Show full text]
  • "The Distribution of Mckay's Approximation for the Coefficient Of
    ARTICLE IN PRESS Statistics & Probability Letters 78 (2008) 10–14 www.elsevier.com/locate/stapro The distribution of McKay’s approximation for the coefficient of variation Johannes Forkmana,Ã, Steve Verrillb aDepartment of Biometry and Engineering, Swedish University of Agricultural Sciences, P.O. Box 7032, SE-750 07 Uppsala, Sweden bU.S.D.A., Forest Products Laboratory, 1 Gifford Pinchot Drive, Madison, WI 53726, USA Received 12 July 2006; received in revised form 21 February 2007; accepted 10 April 2007 Available online 7 May 2007 Abstract McKay’s chi-square approximation for the coefficient of variation is type II noncentral beta distributed and asymptotically normal with mean n À 1 and variance smaller than 2ðn À 1Þ. r 2007 Elsevier B.V. All rights reserved. Keywords: Coefficient of variation; McKay’s approximation; Noncentral beta distribution 1. Introduction The coefficient of variation is defined as the standard deviation divided by the mean. This measure, which is commonly expressed as a percentage, is widely used since it is often necessary to relate the size of the variation to the level of the observations. McKay (1932) introduced a w2 approximation for the coefficient of variation calculated on normally distributed observations. It can be defined in the following way. Definition 1. Let yj, j ¼ 1; ...; n; be n independent observations from a normal distribution with expected value m and variance s2. Let g denote the population coefficient of variation, i.e. g ¼ s=m, and let c denote the sample coefficient of variation, i.e. vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 1 u 1 Xn 1 Xn c ¼ t ðy À mÞ2; m ¼ y .
    [Show full text]
  • Robust Statistics in BD Facsdiva™ Software
    BD Biosciences Tech Note June 2012 Robust Statistics in BD FACSDiva™ Software Robust Statistics in BD FACSDiva™ Software Robust statistics defined Robust statistics provide an alternative approach to classical statistical estimators such as mean, standard deviation (SD), and percent coefficient of variation (%CV). These alternative procedures are more resistant to the statistical influences of outlying events in a sample population—hence the term “robust.” Real data sets often contain gross outliers, and it is impractical to systematically attempt to remove all outliers by gating procedures or other rule sets. The robust equivalent of the mean statistic is the median. The robust SD is designated rSD and the percent robust CV is designated %rCV. For perfectly normal distributions, classical and robust statistics give the same results. How robust statistics are calculated in BD FACSDiva™ software Median The mean, or average, is the sum of all the values divided by the number of values. For example, the mean of the values of [13, 10, 11, 12, 114] is 160 ÷ 5 = 32. If the outlier value of 114 is excluded, the mean is 11.5. The median is defined as the midpoint, or the 50th percentile of the values. It is the statistical center of the population. Half the values should be above the median and half below. In the previous example, the median is 12 (13 and 114 are above; 10 and 11 are below). Note that this is close to the mean with the outlier excluded. Robust standard deviation (rSD) The classical SD is a function of the deviation of individual data points to the mean of the population.
    [Show full text]
  • Correct Use of Percent Coefficient of Variation (%CV) Formula for Log-Transformed Data
    MOJ Proteomics & Bioinformatics Short Communication Open Access Correct use of percent coefficient of variation (%CV) formula for log-transformed data Abstract Volume 6 Issue 4 - 2017 The coefficient of variation (CV) is a unit less measure typically used to evaluate the Jesse A Canchola, Shaowu Tang, Pari Hemyari, variability of a population relative to its standard deviation and is normally presented as a percentage.1 When considering the percent coefficient of variation (%CV) for Ellen Paxinos, Ed Marins Roche Molecular Systems, Inc., USA log-transformed data, we have discovered the incorrect application of the standard %CV form in obtaining the %CV for log-transformed data. Upon review of various Correspondence: Jesse A Canchola, Roche Molecular Systems, journals, we have noted the formula for the %CV for log-transformed data was not Inc., 4300 Hacienda Drive, Pleasanton, CA 94588, USA, being applied correctly. This communication provides a framework from which the Email [email protected] correct mathematical formula for the %CV can be applied to log-transformed data. Received: October 30, 2017 | Published: November 16, 2017 Keywords: coefficient of variation, log-transformation, variances, statistical technique Abbreviations: CV, coefficient of variation; %CV, CV x 100% and variance of the transformation. If the untransformed %CV is used on log-normal data, the resulting Introduction %CV will be too small and give an overly optimistic, but incorrect, i. The percent coefficient of variation, %CV, is a unit less measure of view of the performance of the measured device. variation and can be considered as a “relative standard deviation” For example, Hatzakis et al.,1 Table 1, showed an assessment of since it is defined as the standard deviation divided by the mean inter-instrument, inter-operator, inter-day, inter-run, intra-run and multiplied by 100 percent: total variability of the Aptima HIV-1 Quant Dx in various HIV-1 RNA σ concentrations.
    [Show full text]
  • A Class of Mean Field Interaction Models for Computer and Communication Systems
    Technical Report LCA-REPORT-2008-010, May 2009 Version with bug fixes of full text published in Performance Evaluation, Vol. 65, Nr. 11-12, pp. 823-838, 2008. A Class Of Mean Field Interaction Models for Computer and Communication Systems Michel Bena¨ım and Jean-Yves Le Boudec Abstract We consider models of N interacting objects, where the interaction is via a common re- source and the distribution of states of all objects. We introduce the key scaling concept of intensity; informally, the expected number of transitions per object per time slot is of the order of the intensity. We consider the case of vanishing intensity, i.e. the expected number of object transitions per time slot is o(N). We show that, under mild assumptions and for large N, the occupancy measure converges, in mean square (and thus in probability) over any finite horizon, to a deterministic dynamical system. The mild assumption is essentially that the coefficient of variation of the number of object transitions per time slot remains bounded with N. No independence assumption is needed anywhere. The convergence re- sults allow us to derive properties valid in the stationary regime. We discuss when one can assure that a stationary point of the ODE is the large N limit of the stationary probability distribution of the state of one object for the system with N objects. We use this to develop a critique of the fixed point method sometimes used in conjunction with the decoupling assumption. Key words: Mean Field Interaction Model, Markov Chain, Dynamical System, MAC protocol, Reputation Systems, Game Theory, Decoupling Assumption, Fixed Point, Bianchi’s Formula 1 Introduction We consider models of objects interacting in discrete time, where objects are ob- servable only through their state.
    [Show full text]
  • On the Coefficient of Variation As a Criterion for Decision Under Risk
    1 On the Coefficient of Variation as a Criterion for Decision under Risk James C. Cox and Vjollca Sadiraj Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University December 2009 Forthcoming in Journal of Mathematical Psychology 1 On the Coefficient of Variation as a Criterion for Decision under Risk* 2 3 Weber, Shafir, and Blais (2004) report a meta-analysis of data for animal and human 4 decision making that uses the coefficient of variation (CV) as a measure of “risk sensitivity.” 5 They argue that CV, defined as the ratio of standard deviation (SD) to expected value (EV), is a 6 good predictor of animal and human risk sensitivity. Weber et al. (2004, pgs. 432 – 434) review 7 results from the analysis of animal data in Shafir (2000) in which the proportion of animals 8 choosing certain (sure thing) rewards rather than risky rewards is regressed on the CV of risky 9 rewards. For comparison, Weber, et al. (pg. 434) report results from another regression in which 10 the proportion of sure thing choices is regressed on the EV of risky rewards. They conclude that: 11 “…neither SD nor EV predicts risk sensitivity in isolation. Their ratio, however, in the form of 12 the CV does so very well.” 13 Weber et al. (2004) report that a large advantage of CV over SD in fitting data occurred 14 with data from environments in which decision makers had to learn about the outcome 15 distribution of different options by trial and error sampling and personal experience. Arguably, 16 such environments might better be described as involving uncertainty rather than risk (Knight, 17 1921).
    [Show full text]
  • Coefficient of Variation
    In Neil Salkind (Ed.), Encyclopedia of Research Design. Thousand Oaks, CA: Sage. 2010 Coe±cient of Variation Herv¶eAbdi 1 Overview The coe±cient of variation measures the variability of a series of numbers independently of the unit of measurement used for these numbers. In order to do so, the coe±cient od variation eliminates the unit of measurement of the standard deviation of a series of numbers by dividing it by the mean of these numbers. The coe±- cient of variation can be used to compare distributions obtained with di®erent units, such as, for example, the variability of the weights of newborns (measured in grams) with the size of adults (measured in centimeters). The coe±cient of variation is meaningful only for mea- surements with a real zero (i.e., \ratio scales") because the mean is meaningful (i.e., unique) only for these scales. So, for example, it will be meaningless to compute the coe±cient of variation of the temperature measured in degrees Fahrenheit, because changing the measurement to degrees Celsius will not change the temperature but will change the value of the coe±cient of variation (because the value of zero for Celsius is thirty-two for Fahrenheit and therefore the mean Herv¶eAbdi The University of Texas at Dallas Address correspondence to: Herv¶eAbdi Program in Cognition and Neurosciences, MS: Gr.4.1, The University of Texas at Dallas, Richardson, TX 75083{0688, USA E-mail: [email protected] http://www.utd.edu/»herve 2 Coe±cient of Variation of the temperature will change from one scale to the other, whereas the standard deviation will not change).
    [Show full text]
  • An Analysis of Drag Forces Based on L-Moments
    Advances in Safety and Reliability – Kołowrocki (ed.) © 2005 Taylor & Francis Group, London, ISBN 0 415 38340 4 An analysis of drag forces based on L-moments P.H.A.J.M. van Gelder Delft University of Technology, Delft, The Netherlands CeSOS, NTNU, Trondheim, Norway M.D. Pandey University of Waterloo, Ontario, Canada ABSTRACT: Since L-moment estimators are linear functions of the ordered data values, they are virtually unbiased and have relatively small sampling variance, especially in comparison to the classical coefficients of skewness and kurtosis. Moreover, estimators of L-moments are relatively insensitive to outliers. Liaw and Zheng (2004) calculated drag forces on cylinders by polynomial approximations in which the coefficients are estimated by least-squares and moment methods. In this paper, the coefficients will be estimated with L-Moment methods. Its advantages will shown to be that: (i) an L-Moments approach leads to a linear solution of the polynomialisation of drag forces and (ii) other distribution types for the turbulent current and wave heights acting on the cylinder can be analysed fairly simple. 1 INTRODUCTION of quantile estimators (Hosking and Wallis, 1987; Rosbjerg et al., 1992). As compared with for exam- Since Hosking (1990), introduced L-moments, they ple the classical method of moments, the robustness have become popular tools for solving various sta- vis-à-vis sample outliers is clearly a characteristic tistical problems related to parameter estimation, of L-moment estimators. However, estimators can be distribution identification, and regionalization. The “too robust” in the sense that large (or small) sam- L-moments are linear functions of probability ple values reflecting important information on the weighted moments (PWM’s) and hence for certain tail of the parent distribution are given too little applications, such as the estimation of distribution weight in the estimation.
    [Show full text]
  • Orthogonal Nanoparticle Size, Polydispersity, and Stability
    Article pubs.acs.org/journal/apchd5 Orthogonal Nanoparticle Size, Polydispersity, and Stability Characterization with Near-Field Optical Trapping and Light Scattering Perry Schein,†,§ Dakota O’Dell,‡,§ and David Erickson*,† † ‡ Sibley School of Mechanical and Aerospace Engineering and School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, United States *S Supporting Information ABSTRACT: Here we present and demonstrate a new technique for simultaneously characterizing the size, polydispersity, and colloidal stability of nanoparticle suspensions. This method relies on tracking each nanoparticle’s motion in three spatial dimensions as it interacts with the evanescent field of an optical waveguide. The motion along the optical propagation axis of the waveguide provides insight into the polydispersity of a nanoparticle suspension. Horizontal motion perpendicular to the propagation axis gives the diffusion coefficient and particle size. In the direction normal to the surface, statistical analysis of the scattered light intensity distribution gives a map of the interaction energy landscape and insight into the suspension stability. These three orthogonal measurements are made simultaneously on each particle, building up population level insights from a single-particle rather than ensemble-averaged basis. We experimentally demonstrate the technique using polystyrene spheres obtaining results consistent with the manufacturer’s specifications for these suspensions. For NIST-traceable polystyrene size standard spheres,
    [Show full text]