Structural evaluation and strengthening of tall buildings: expected value and coefficient of variation of limit state capacity

2012 ◽  
Vol 21 ◽  
pp. 31-47 ◽  
Author(s):  
Nicolino G. Delli Quadri ◽  
Colin Kumabe ◽  
Ifa Kashefi ◽  
Larry Brugger ◽  
Lauren D. Carpenter ◽  
...  
Keyword(s):  
Coefficient Of Variation ◽  
State Capacity ◽  
Limit State ◽  
Tall Buildings ◽  
Expected Value ◽  
Structural Evaluation

scholarly journals Evidence-based health security: The nexus of governance and pandemic preparedness

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
R Greenhill ◽  
J Johnson ◽  
P Malone ◽  
A Westrum
Keyword(s):  
Mixed Methods ◽  
Expert Panel ◽  
State Capacity ◽  
Limit State ◽  
Sub Saharan Africa ◽  
Pandemic Preparedness ◽  
African Region ◽  
Health Security ◽  
Preparedness Planning ◽  
Sub Saharan

Abstract Background Pandemic preparedness continues to be an important focus of the global health security agenda. Many nations in the sub-Saharan African region remain at high risk for a major pandemic due to limited capacity and endemic co-morbid conditions in their populations. While the literature does suggest that state capacity influences health, no studies to date indicate an association between state capacity and pandemic disease distribution, particularly in the presence of other endemic diseases. Methods This mixed methods study will contribute to existing research by examining how economic and sociopolitical attributes of state capacity influence pandemic-prone disease distribution in sub-Saharan Africa. A convergent mixed methods design was used to collect and analyze quantitative state capacity attributes and prevention, and control using correlation in six sub-Saharan countries. Results of the quantitative study were triangulated through the use of an expert panel and results integrated for an overall interpretation and conclusion. Results Variables in the study showed statistically significant relationships between proxies of state capacity and the follow areas: control of pandemics and prevention of pandemics. The Expert Panel interviews illustrated convergence between the correlated results. Conclusions This study brought forward associations with expert confirmation suggestive of areas for national governments in sub-Saharan Africa to further review and improve. While many internal factors limit state capacity in these nations (e.g. human and fiscal resources), external funders may consider adding information from this study and other metrics to test progress. Key messages Evidence is valuable for pandemic preparedness planning. Nation state capacity is a factor in pandemic preparedness.

scholarly journals Min And Max Normal Extreme Interval Values And Statistics

2011 ◽  
Vol 6 (8) ◽  
Author(s):  
Marsha Jance ◽  
Nick Thomopoulos
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Expected Value ◽  
Font Size ◽  
Interval Value

<p class="MsoNormal" style="text-align: justify; line-height: normal; margin: 0in 0.5in 0pt;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt;">The method for determining the min and max normal extreme interval values and statistics: expected value, standard deviation, median, mode, and coefficient of variation is discussed.<span style="mso-spacerun: yes;">&nbsp; </span>An extreme interval value </span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast;"><span style="mso-spacerun: yes;">&nbsp;</span></span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt;">is defined as a numerical bound, where a specified percentage &alpha; of the data is less than or equal to ga</span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast;">.</span></p>

scholarly journals Min And Max Uniform Extreme Interval Values And Statistics

2011 ◽  
Vol 7 (1) ◽  
Author(s):  
Marsha Jance ◽  
Nick Thomopoulos
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Expected Value ◽  
Font Size ◽  
Numerical Example ◽  
Relative Line ◽  
Parameter Values ◽  
Uniform Parameter ◽  
Interval Value

<p class="MsoNormal" style="text-align: justify; line-height: normal; margin: 0in 0.5in 0pt;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt;">The min and max uniform extreme interval values and statistics; ie expected value, standard deviation, mode, median, and coefficient of variation, are discussed.<span style="mso-spacerun: yes;">&nbsp;&nbsp; </span>An extreme interval value </span><span style="position: relative; line-height: 115%; font-family: &quot;Calibri&quot;,&quot;sans-serif&quot;; font-size: 11pt; top: 4pt; mso-fareast-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-text-raise: -4.0pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"></span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt;">is defined as a numerical bound where a specified percentage &alpha; of the data is less than </span><span style="position: relative; line-height: 115%; font-family: &quot;Calibri&quot;,&quot;sans-serif&quot;; font-size: 11pt; top: 4pt; mso-fareast-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-text-raise: -4.0pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"></span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast;">. A numerical example and an analysis of the min and max extreme interval values and statistics are provided.<span style="mso-spacerun: yes;">&nbsp; </span>In addition, a procedure for finding the min and max extreme interval values for different uniform parameter values, and an application of this research are presented.</span></p>

Estimating Long Term Extreme Slamming From Breaking Waves

2015 ◽  
Author(s):  
Gunnar Lian ◽  
Sverre K. Haver
Keyword(s):  
Coefficient Of Variation ◽  
Limit State ◽  
Breaking Waves ◽  
Contour Method ◽  
Short Term ◽  
Sea State ◽  
Response Process ◽  
Term Response ◽  
Short Term Variability

Characteristic loads for design are defined in terms of their annual exceedance probability, q. For ultimate limit state (ULS) q = 10−2, while q = 10−4 for accidental limit state (ALS). In principle a full long term analysis is required in order to obtain consistent estimates. This is straight forward for linear response problems, while it is a challenge for non-linear problems in particular if they additionally are of an on-off nature. The latter will typically be the case for loads due to breaking wave impacts. The Contour line approach is an alternative convenient method to estimate the long term extreme response, based on short term statistics from an appropriate sea state. The consequence of very large short term variability (large coefficient of variation for 3-hour extreme value) on the application of the contour method will be discussed. The long term integral is carried out over all sea state combinations. The lowest sea states will of course not affect the extremes. However, for the impact problem the short term variability is much larger than for most response cases. The coefficient of variation of the 3-hour maximum impact pressure is often between 0.5 and 1, while for a typical response process it is between 0.1 and 0.2. Due to the large variability, lower sea states than normal will contribute to the long term response. In this paper the irregularity of the response surface, and the uncertainties related to the number of seeds used in each sea state is looked into. The focus is on slamming loads from breaking waves, and some results from a model test are presented. The uncertainties in long term response from slamming loads are compared to a more common response process. The effect on the long term response when integrating over a reduced area of sea states in the scatter diagram is discussed.

Ultimate Limit State Capacity Analyses of Two Gulf of Mexico Platforms

1997 ◽  
Author(s):  
James Stear ◽  
Robert Bea
Keyword(s):  
Gulf Of Mexico ◽  
State Capacity ◽  
Limit State ◽  
Ultimate Limit State ◽  
Ultimate Limit

scholarly journals Modernization and structural evaluation of the improved Overhead Cable System

2021 ◽  
Author(s):  
M. Trim ◽  
Matthew Murray ◽  
C. Crane
Keyword(s):  
Failure Mode ◽  
Compression Test ◽  
Load Capacity ◽  
Limit State ◽  
System Level ◽  
Component Level ◽  
Cable System ◽  
Structural Evaluation ◽  
Bridge Crossing ◽  
System Prototype

A modernized Overhead Cable System prototype for a 689 ft (210 m) Improved Ribbon Bridge crossing was designed, assembled, and structurally tested. Two independent structural tests were executed, i.e., a component-level compression test of the BSS tower was performed to determine its load capacity and failure mode; and a system-level ‘dry’ test of the improved OCS prototype was conducted to determine the limit state and failure mode of the entire OCS. In the component-level compression test of the BSS tower, the compressive capacity was determined to be 102 kips, and the failure mode was localized buckling in the legs of the tower section. During system-level testing, the prototype performed well up to 40.5 kips of simulated drag load, which corresponds to a uniformly distributed current velocity of 10.7 ft/s. If a more realistic, less conservative parabolic velocity distribution is assumed instead, the drag load for an 11 ft/s current is 21.1 kips. Under this assumption, the improved OCS prototype has a factor of safety of 1.9, based on a 689-ft crossing and 11-ft/s current. The OCS failed when one of the tower guy wires pulled out of the ground, causing the tower to overturn.

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