Hallmark professionalism in probabilistic analysis is to quantify the uncertainties involved in construction materials subject to intrinsic randomness in its physical and mechanical properties and is now gaining popularity in civil engineering arena. As well, knowledge of behaviour of materials is continuously evolving and its statistical descriptors are also changing when more and more data collected or even data updated and hence reliability analysis has to be carried out with the updated data as a continuous process. As per the committee report ACI 544.2R, it is found that there is no attempt made for probabilistic relation between cube compressive strength and cylinder compressive strength for fiber reinforced concrete. In consequence of this report, a robust relation between cube and cylinder of experimentally conducted compressive strength was established by Monte-Carlo simulation technique for different types of fibrous concrete like steel, alkali resistant glass and polyester fibrous concrete before and after thermoshock considering various uncertainties. Nevertheless simulated probabilistic modals, characteristic modals, optimized factor of safety and allowable designed cylinder compressive strength have been developed from the drawn probability of failure graph, which exhibits robust performance in realistic Civil Engineering materials and structures.
The accuracy of vehicle speed measured by a speedometer is analysed. The stress on the application of skew normal distribution is laid. The accuracy of measured vehicle speed depends on many error sources: construction of speedometer, measurement method, model inadequacy to real physical process, transferring information signal, external conditions, production process technology etc. The errors of speedometer are analysed in a complex relation to errors of the speed control gauges, whose functionality is based on the Doppler effect. Parameters of the normal distribution and skew normal distribution were applied in the errors analysis. It is shown that the application of maximum permissible errors to control the measuring results of vehicle speed gives paradoxical results when, in the case of skew normal distribution, the standard deviations of higher vehicle speeds are smaller than the standard deviations of lower speeds. In the case of normal distribution a higher speed has a greater standard deviation. For the speed measurements by Doppler speed gauges it is suggested to calculate the vehicle weighted average speed instead of the arithmetic average speed, what will correspond to most real dynamic changes of the vehicle speed parameters.
While the Slope Fault Model method can solve the soft-fault diagnosis problem in linear analog circuit effectively, the challenging tolerance problem is still unsolved. In this paper, a proposed Normal Quotient Distribution approach was combined with the Slope Fault Model to handle the tolerances problem in soft-fault diagnosis for analog circuit. Firstly, the principle of the Slope Fault Model is presented, and the huge computation of traditional Slope Fault Characteristic set was reduced greatly by the elimination of superfluous features. Several typical tolerance handling methods on the ground of the Slope Fault Model were compared. Then, the approximating distribution function of the Slope Fault Characteristic was deduced and sufficient conditions were given to improve the approximation accuracy. The monotonous and continuous mapping between Normal Quotient Distribution and standard normal distribution was proved. Thus the estimation formulas about the ranges of the Slope Fault Characteristic were deduced. After that, a new test-nodes selection algorithm based on the reduced Slope Fault Characteristic ranges set was designed. Finally, two numerical experiments were done to illustrate the proposed approach and demonstrate its effectiveness.