This paper presents an evaluation of the minimum dipper stick service life in the unsafe cross-section; it is based on statistical simulation known as Monte Carlo method. Source data on the bearing capacity parameters as well as on the values of stresses in the unsafe cross-section are presented as small samples. Statistical simulation is used to create a finite general population of data on machine parts my means of an intermediate subpopulation using order statistics distributions.
Abstract.This paper applies the statistical modeling method to provide a quantitative assessment of risks emerging at sudden failures of construction equipment. The risk mathematical model takes into consideration the distribution parameters of operation, downtime and repair hours determined for a general population of the failed part finite volume. The proposed modeling algorithm is applied to assess risks at sudden dipper stick failures. The modeling results allow one to evaluate the tolerable period for the equipment use under the set risk levels.
Abstract. In this paper, the researchers propose taking into account construction equipment recovery time to estimate the time consumption of construction and installation works. Post-failure recovery time can be modeled using Monte-Carlo simulation. Statistical simulation is used to create a general population of machine parts by means of intermediate subpopulation. The population is made of smaller samples by creating an intermediate subpopulation using distributions of order statistics; the method is referred to as FISP method.
Identification of dynamic characteristics of the machine suspension materials is required for troubleshooting and improving the ride quality. The method of identifying the suspension materials characteristics, based on the application of needle-shaped variation of the L.S. Pontryagin dynamical system to the invariant features of the dynamic system actual movement is presented in this study. The use of the needle-shaped parameters variation to the Hamilton-Ostrogradskyi action integral gives a condition of the objective functional minimum in the form of the maximum principle with the exception of the consideration of the vector of conjugate variables. As a result, the computational costs are reduced, the task of identification and optimization is simplified the convergence and accuracy of the algorithms is improved [1 - 5].
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