Reliability,  Modelling  & Optimization 
RMO research group
Reliable Design, Materials & Manufactur​ing 

RMO / Research / Reliable Design

Reliable Design

At the RMO lab, our reliable design approach focuses on the developement of computational tools for design under uncertainties as well as the implementation of these tools in industrial applications. The main research challenge includes avoiding a conservative and overdimensioned design based on the employment of traditional empirical safety factors. Instead, a probabilistic framework is applied taking into account uncertainties with a true-to-nature representation of the stochasticity in loading, material and design parameters.

Machine-Learning based reliability assessment and uncertainty modelling

In reliability assessment, a performance function can typically be defined by the difference between a computed value, for instance, the maximum stress in a structure, and an allowed value. When the performance function includes uncertainties, the problem of accurately and effectively computing the failure risk arises. The uncertainties can generally be of two types: aleatory and epistemic. Aleatory uncertainty is due to inherent randomness while epistemic uncertainty is an uncertainty in the model of a problem.

At the Reliability Modelling and Optimization (RMO) Lab, we develop novel risk/reliability assessment methods using adaptive surrogate modelling techniques as well as space-time varying uncertainty quantification (UQ) and propagation methods. Our ongoing data-driven modelling efforts handles different types of uncertainties including "known unknowns" and "unknown uknowns".

Structural design under uncertainty

The inherent stochastic nature of loads and structural response poses significant challenges in structural design, especially when high reliability requirements are essential. The inability to properly account for statistical uncertainties may lead to over-dimensioned structures due to the application of unnecessarily large safety-factors. This, in turn, is a major disadvantage from both cost and sustainability aspects.

To address this challenge, we develop novel efficient probabilistic design frameworks that accounts for uncertainties in loads, material as well as dimensional tolerances. An important aspect in our ongoing work is to validate the probabilistic framework against Design Standards and to ensure that the design satisfies target failure probability criteria.  To this aim, a global safety factor is computed based on the proposed probabilistic framework, which is used to validate the partial safety factor method employed in typical Design Standards.

Fatigue design

Numerous engineering components experience cyclic loading, which can lead to progressive material degradation and eventual fatigue failure. Ensuring a robust fatigue design is crucial in preventing unexpected breakdowns, thus enhancing the safety and longevity of structures. In our research, we employ and develop fatigue models for both High Cycle Fatigue (HCF) and Low Cycle Fatigue (LCF). In particular, we focus on modelling and mitigating the impact of variability in fatigue life, in order to reduce the uncertainty in predicting component lifespan.

Selected representative publications

R. Mansour, P. Enblom, M. Subasic, A. Ireland, F. Gustavsson, B. Forssgren and P. Efsing, "Influence of viscoplastic relaxation and strain-induced martensitic transformation on the fatigue life of 304L stainless steel through a single hold time," International Journal of Fatigue, 2025.

M. Subasic, M. Olsson, S. Dadbakhsh, X. Zhao, P. Krakhmalev, and R. Mansour, "Fatigue strength improvement of additively manufactured 316L stainless steel with high porosity through preloading," International Journal of Fatigue, vol. 180, p. 108077, 2024.

M. Subasic, A. Ireland, R. Mansour, P. Enblom, P. Krakhmalev, M. Åsberg, A. Fazi, J. Gårdstam, J. Shipley, P. Waernqvist, B. Forssgren, P. Efsing, "Experimental investigation and numerical modelling of the cyclic plasticity and fatigue behavior of additively manufactured 316 L stainless steel," International journal of plasticity, vol. 176, p. 103966, 2024.

Z. Hu, R. Mansour, M. Olsson, and X. Du, "Second-order reliability methods: a review and comparative study," Structural and multidisciplinary optimization, pp. 1-31, 2021.

R. Mansour and M. Olsson, "Efficient reliability assessment with the conditional probability method," J Mech Design, vol. 140, no. 8, p. 081402, 2018.

D. Sandberg, R. Mansour, and M. Olsson, "Fatigue probability assessment including aleatory and epistemic uncertainty with application to gas turbine compressor blades," International Journal of Fatigue, vol. 95, pp. 132-142, 2017