Discussion of ‘failure probability evaluation for normally distributed load-strength model with unknown parameters’ by K. Yang, vol. 51 (1996) 115–118

1997 ◽  
Vol 56 (1) ◽  
pp. 95
Author(s):  
Benjamin Reiser
Keyword(s):  
Failure Probability ◽  
Strength Model ◽  
Unknown Parameters ◽  
Distributed Load ◽  
Probability Evaluation ◽  
Normally Distributed

Failure Probability of Short High-Strength Concrete Tied Columns

2003 ◽  
Vol 19 (2) ◽  
pp. 299-309
Author(s):  
Wen-Yao Lu ◽  
Ing-Juang Lin
Keyword(s):  
Monte Carlo ◽  
Cross Section ◽  
Failure Probability ◽  
High Strength Concrete ◽  
High Strength ◽  
Concrete Columns ◽  
Monte Carlo Technique ◽  
Strength Model ◽  
Strength Concrete ◽  
High Strength Concrete Columns

ABSTRACTThis paper aims to investigate the failure probability of short high-strength concrete tied columns using the Monte Carlo technique. The random variables considered in this study are the strength of concrete, the strength of steels, the cross-section dimensions, the location of the steel reinforcement, the variability of strength model and the loads. The results show that the failure probabilities of high-strength concrete columns designed according to the ACI Code are relatively high. The current ACI Code may not be conservative for design of short high-strength concrete tied columns.

scholarly journals The concepts of inverse probability and fiducial probability referring to unknown parameters

1933 ◽  
Vol 139 (838) ◽  
pp. 343-348 ◽  
Keyword(s):  
Standard Deviation ◽  
Practical Application ◽  
Unknown Parameters ◽  
Inverse Probability ◽  
Fiducial Probability ◽  
The Mean ◽  
Normally Distributed

In a paper published in these 'Proceedings' Jeffreys puts forward a form of reasoning purporting to resolve in a particular case the primitive difficulty which besets all attempts to derive valid results of practical application from the theory of Inverse Probability. For a normally distributed variate, x , the frequency element may be written df = h /√π e - h 2 ( x - μ ) 2 dx , where μ is the mean of the distribution, and h the precision constant. For the convenience of the majority of statisticians who prefer to use the standard deviation, σ, of the distribution, in place of the precision constant, we may note that h 2 = 1/2σ 2 , and that this substitution may be made at any stage of the argument.

Failure probability evaluation of passive system using fuzzy Monte Carlo simulation

2011 ◽  
Vol 241 (5) ◽  
pp. 1864-1872 ◽  
Author(s):  
M. Hari Prasad ◽  
Avinash J. Gaikwad ◽  
A. Srividya ◽  
A.K. Verma
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Failure Probability ◽  
Passive System ◽  
Probability Evaluation
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