A Self-Configuring Membership-Function-Based Approach for Fuzzy Fatigue Reliability Optimization of Welded A-Type Frame Considering Multi-Source Uncertainties

Abstract

A large number of sample data is needed to ascertain the characteristic parameters of traditional membership function, so that the calculated fuzzy fatigue reliability based on this method has certain errors for engineering structures without enough samples. A fuzzy fatigue reliability analysis method based on self-configuring membership function is proposed, while considering its multi-source uncertainties in the design, manufacture, and use stage in order to accurately evaluate fatigue reliability of welded A-type frame. In this paper, a novel membership function was presented on account of a small amount of sample data, which some experimental results verified. The mathematical expression for failure probability was deduced from the suggested model, as well as fatigue reliability. Subsequently, the thickness of steel plate defined in design stage, the material properties of weld metal that is produced in manufacture stage, and the loads at different connection sites determined in use stage were all considered as the random variables, which were obtained from Latin hypercube sampling, and the fatigue limit of weld metal was deemed as the fuzzy variable. Based on the response surface method, the fuzzy fatigue reliability performance function was constructed to assess failure probability of welded A-type frame under the condition of downhill and turning braking with full load, while its fatigue reliability was found to be far less than 90%. The fuzzy fatigue reliability optimization that was based on genetic algorithm was implemented, which showed that its reliability varied from 69.47% to 95.12%.

Keywords

• electric wheel dump truck
• fuzzy membership function
• multi-source uncertainties
• reliability performance function
• genetic algorithm