China
Africa
Explore chapters and articles related to this topic
Fatigue life updating for vessel fleets
Published in Jaap Bakker, Dan M. Frangopol, Klaas van Breugel, Life-Cycle of Engineering Systems, 2017
Mark D. Groden, Yan Liu, Matthew D. Collette
While both fatigue and permanent set damage can be driven by the same probabilistic loading process, they represent different characteristics of the loading process. Permanent set records the extreme pressure experienced laterally on a plate, so a extreme value distribution from the loading process is needed. Fatigue failure is driven by all the life-cycle load reversals, so a per-peak distribution is needed here. Following on a wide range of existing loading work in the marine industry, the per-pea distribution is assumed to follow a two-parameter Weibull distribution. In this case, the corresponding extreme value distribution can be shown to follow as Gumbel distribution. Soares & Teixeira (2000) proposed an approximate formulation to determine the parameters of the extreme value Gumbel distribution from the underlying Weibull distribution given a number of cycles, n: xn=a(ln(n))1/βσ=aβ(ln(n))1/(β−1)
LSD: normal approximation
Published in Arup Bose, Koushik Saha, Random Circulant Matrices, 2018
Remark 3.3.1. The random variable – log E has the standard Gumbel distribution with mean γ=limn→∞[1+12+⋅⋅⋅+1n−logn]≈0.57721 (the Euler-Mascheroni constant). It follows that r = e–γ/2 ≈ 0.74930.
Deep learning-based underground object detection for urban road pavement
Published in International Journal of Pavement Engineering, 2020
Namgyu Kim, Kideok Kim, Yun-Kyu An, Hyun-Jong Lee, Jong-Jae Lee
Figures 3(a) and 4(a) show examples of typical A-scan waveforms of GPR data. The maximum absolute amplitudes of each A-scan waveform are plotted in Figures 3(b) and 4(b). Figures 3(c–d) and 4(c–d) are the histograms of the maximum absolute amplitudes of GPR data. Here, totally 588,880 and 366,200 A-scan waveforms are used to plot the histograms, respectively. The histograms are then fitted with Gamma and Gumbel distributions. Gumbel distribution, also known as type I extreme value distribution, is generally used to model extreme cases such as the distribution of the maximum or the minimum of a value of samples. The probability density function of Gumbel distribution can be defined aswhere μ is the location parameter, and σ (σ > 0) is the scale parameter. From the fitted distributions, it has been found that Gumbel distribution can better reflect the distribution of maximum absolute amplitudes of GPR data than Gamma distribution. Table 1 shows the obtained location and scale parameters of Gumbel distribution for all field surveying regions. It has been found that the location parameters are almost 3 times of scale parameters regardless of the scanning distance.
Modelling tropical cyclone risks for present and future climate change scenarios using geospatial techniques
Published in International Journal of Digital Earth, 2018
Muhammad Al-Amin Hoque, Stuart Phinn, Chris Roelfsema, Iraphne Childs
In this study, the historical cyclone data (1960–2015) were used for frequency analysis to calculate different return-period extreme surge heights. The coastal area of Bangladesh is divided into three distinct zones based on geomorphic characteristics (Figure 1) (Karim and Mimura 2008). The study site is located in the western coastal zone. Therefore, cyclone data from the western coastal region were only considered in the frequency analysis for better output (Table 2). Gumbel distribution is considered as the best probability distribution function for extreme value analysis (Stedinger, Vogel, and Georgiou 1993). In this study, the Gumbel Distribution was used for frequency analysis to determine extreme surge heights for 5, 10, 20, 50 and 100 cyclone return periods. The calculated extreme surge heights were 4.65, 5.39, 6.10, 7.01 and 7.70 m for return periods 5, 10, 20, 50 and 100, respectively.
An integrated approach to evaluating inland waterway disruptions using economic interdependence, agent-based, and Bayesian models
Published in The Engineering Economist, 2023
Paul M. Johnson, Hiba Baroud, Craig Philip, Mark Abkowitz
We then model Gumbel distributions for each river gauge to predict the return periods (measured in years) of the expected number of closures due to flood conditions at that location. The Gumbel distribution is a special case of the generalized extreme value distribution, which models the asymptotic behavior of extreme values expressed as a Poisson process over a given number of samples (Gilleland & Katz, 2016). Researchers often use the Gumbel distribution to analyze flood data and in particular to model the return period of high-water levels (Bhagat, 2017; Onen & Bagatur, 2017; Solomon et al., 2013). The Gumbel distribution’s density function is given in Equation 1: