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Reliability assessment of mining equipment using genetic algorithms
Published in G. N. Panagiotou, T. N. Michalakopoulos, Mine Planning and Equipment Selection 2000, 2018
The significance of high reliability of capital intensive mobile mining equipment (e.g. scooptrams, trucks or shovels) is well recognized within the Canadian mining industry. In general, the reliability can be assessed either by mathematical models using the failure time of a machine as the only variable under consideration or by models with covariates where a covariate could be for example, the operating environment, the machine design, the number of repairs or the age of the equipment under study. Reliability models with or without covariates are based on the use of rigorous and complicated statistical techniques which include, for instance, theoretical probability distribution fitting, trend and serial correlation tests and require assumptions of homogeneous or non-homogeneous Poisson processes or assumptions of proportionality of the hazard rate. The assumptions and statistical constraints of probabilistic reliability models cause limitations to the ability of these models to accurately represent and fit all real life mining conditions (Kumar 1990, Kumar 1993, Paraszczak & Perreault 1994, Hall et al. 1998).
Modelling uncertainty of the EN 1992-4 shear strength model for anchors located near a concrete edge
Published in Airong Chen, Xin Ruan, Dan M. Frangopol, Life-Cycle Civil Engineering: Innovation, Theory and Practice, 2021
The model uncertainty associated with EN 1992-4 was found to have a mean value of μMF=0.84 and a standard deviation of σMF=0.19. A good predictive model is expected to have low bias and uncertainty coupled with insignificant sensitivities with shear breakout design parameters. Such a model can be used as a probabilistic model for reliability analysis. The EN 1992-4 model uncertainty did not display any major trend with shear parameters. This means that the model can predict the influence of most shear parameters on shear resistance with accuracy. However, the general overprediction of mean shear capacity by EN 1992-4 model is a cause for concern. Adequate reliability of EN 1992-4 shear breakout capacity model should be confirmed for anchors with different design parameters. The probability distribution fitting indicates that the EN 1992-4 model uncertainty can be modelled as a lognormally distributed random variable. The model uncertainty statistics derived in this study can directly be used as input in reliability analysis. Therefore, lognormal distributions with mean and coefficient of variation according to the values presented in Table 3 are recommended to probabilistically describe the model uncertainty of EN 1992-4 shear breakout design formulation in reliability analysis.
Hydrological Drought: Water Surface and Duration Curve Indices
Published in Saeid Eslamian, Faezeh Eslamian, Handbook of Drought and Water Scarcity, 2017
Manish Kumar Goyal, Vivek Gupta, Saeid Eslamian
The main disadvantage of the AMS approach is that it identifies only one drought event from any year, though a year may have more than one extreme event that is more extreme than the selected extreme event of other years. Also, we end up with a very small number of drought events if very short-length streamflow data are available, which will create difficulty in probability distribution fitting. Moreover, if in any year, no extreme event occurred, then we will be forced to take a nonextreme event from that year, which will not be statistically identical to the other events in the series. So an additional nonextreme event filter is required in the AMS approach.
Flood estimation at Hathnikund Barrage, River Yamuna, India using the Peak-Over-Threshold method
Published in ISH Journal of Hydraulic Engineering, 2020
Mukesh Kumar, Mohammed Sharif, Sirajuddin Ahmed
In frequency analysis, it is important to fit a probability distribution to the series of the flood peaks, which may be obtained either using the AM or the POT approach. The aim of the probability distribution fitting is to select a distribution that suits the data well. Two distributions, namely Log-Pearson Type III and Gumbel’s Type I distributions, have been fitted to the series of flood peaks data obtained using the AM and the POT approach. According to Gumbel’s theory, the probability of occurrence of an event equal to or larger than a value is