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Multiobjective optimization (MO) techniques allow a designer to model a specific problem considering a more realistic behavior, which commonly involves the satisfaction of several targets simultaneously. A fundamental concept, which is adopted in the multicriteria optimization task, is that of Pareto optimality. In this paper we test several well-known procedures to deal with multiobjective optimization problems (MOP) and propose a novel modified procedure that when applied to the existing Normal Boundary Intersection (NBI) method and Normal Constraint (NC) method for more than two objectives overcomes some of their deficiencies. For the three and four objective applications analyzed here, the proposed scheme presents the best performance both in terms of quality and efficiency to obtain a set of proper Pareto points, when compared to the analyzed existing approaches.

In this work, failure prediction of corroded pipelines under combined loads of internal pressure and axial compressive force is investigated through the Finite Element Method. The study was carried out using the PIPEFLAW program, which was specially developed to automatically generate and analyze corroded pipelines models. Two configurations of idealized defects in pipeline were investigated: with two and three rectangular defects. This work was divided into two stages where, on the first stage, the analysis is performed considering only internal pressure. On the second stage, combined loads are simulated considering internal pressure and axial compressive forces. Results show that failure pressure values decrease with the increase in the axial compressive force, for both Finite Elements models analyzed.

Purpose – Optimization under a deterministic approach generally leads to a final design in which the performance may degrade significantly and/or constraints can be violated because of perturbations arising from uncertainties. The purpose of this paper is to obtain a better strategy that would obtain an optimum design which is less sensitive to changes in uncertain parameters. The process of finding these optima is referred to as robust design optimization (RDO), in which improvement of the performance and reduction of its variability are sought, while maintaining the feasibility of the solution. This overall process is very time consuming, requiring a robust tool to conduct this optimum search efficiently. Design/methodology/approach – In this paper, the authors propose an integrated tool to efficiently obtain RDO solutions. The tool encompasses suitable multiobjective optimization (MO) techniques (encompassing: Normal-Boundary Intersection, Normalized Normal-Constraint, weighted sum method and min-max methods), a surrogate model using reduced order method for cheap function evaluations and adequate procedure for uncertainties quantification (Probabilistic Collocation Method). Findings – To illustrate the application of the proposed tool, 2D structural problems are considered. The integrated tool prove to be very effective reducing the computational time by up to five orders of magnitude, when compared to the solutions obtained via classical standard approaches. Originality/value – The proposed combination of methodologies described in the paper, leads to a very powerful tool for structural optimum designs, considering uncertainty parameters, that can be extended to deal with other class of applications.