Multi-objective topology optimization design of truss structures based on evidence theory under limited information

This resource gives academic guidance on how to model and analyze multi‑objective robust topology optimization for truss structures when the uncertainty is epistemic. When the available uncertainty data are scarce and highly discrete, ordinary probabilistic methods often do not work well. The study tackles this problem by using evidence theory to describe epistemic uncertainty. It measures plausibility to define the highest possible failure probability, which serves as a reliability metric. The optimization aims at two opposing goals: * Reduce the total weight of the structure. * Increase the structure’s reliability.

To explore the resulting robust Pareto front without needing gradient information, the authors use a parallelized Differential Evolution for Multi‑Objective Optimization (DEMO) algorithm. The paper shows the method’s success by optimizing the reliability of a wooden truss. The elastic modulus and the applied load are treated as uncertain variables. Physical validation was done with static random‑loading tests on six identical truss specimens, and failure probability was assessed using stress criteria. For M.Tech students, this resource provides a clear framework and implementation tips for non‑gradient optimization methods, uncertainty quantification, and reliability‑based design in structural engineering.

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