Reliability and Robustness of Structures and Systems – Institute for Risk and Reliability

The assessment of reliability and robustness of structures and systems as well as the design for reliability and robustness is in the centre of our expertise. Structures and systems are exposed to uncertain environments, which can be characterized by stochastic loads and boundary conditions. This includes randomly fluctuating static loads as well as dynamic loads, such as wind or earthquake excitations, traffic loads and sea states, and conditions that describe the interaction of the structure or system with its environment, for example, foundation soil characteristics. In addition, uncertainties are inevitably present in structures and systems themselves, specifically in form of fluctuating material properties, geometrical imperfections, tolerances in production and construction, deviations of the mechanical behaviour of joints from the model assumptions, etc. These uncertainties can depend not only on time but also on spatial coordinates, e.g. be fluctuating over the structure or system, and possess dependencies on one another. Use and operation of our structures and systems add on another layer of uncertainties.

Our developments target at a comprehensive and realistic modelling and processing of this wide range of uncertainties including dependencies as a basis to ensure a safe use and operation of our structures and systems. We develop advanced stochastic models and high performance algorithms for stochastic simulation in order to enable a realistic uncertainty quantification for industry size structures and systems. These algorithms and associated techniques can be combined with high fidelity deterministic models for structural and systems analysis, including nonlinear models, to arrive at most realistic results. Highly efficient numerical algorithms formulated for high performance computing enable large-scale stochastic analyses. On this basis we analyse not only the reliability and robustness of structures and systems but also their life time, life cycle performance, and time dependent inspection and maintenance needs.

We develop algorithms and techniques for design under uncertainty, which ensure a safe and robust performance of our structures and systems under real world conditions with all uncertainties and even under harsh, extreme or safety critical conditions. Our approaches help to design structures and systems superior to those designed with traditional deterministic approaches in terms of reliability, performance, maintenance and cost. Performance objectives are met with greater efficiency and confidence. Performance criteria are met in an optimal manner while avoiding over-engineered solutions.

Application areas include but are not limited to realistic uncertainty modelling, efficient and realistic analysis of structures, systems and processes, quality assurance of systems and components, and efficient simulation strategies.

We provide tailored solutions for a variety of problems from this spectrum and beyond.

Selected References

Eckert, C., Beer, M.; Spanos, P.D. (2020): A Polynomial Chaos Method for Arbitrary Random Inputs using B-Splines, Probabilistic Engineering Mechanics, 60, Article 103051.
DOI: 10.1016/j.probengmech.2020.103051
Jiang, Y.B.; Zhao, L.J.; Beer, M.; Wang, L.; Zhang, J.R. (2020): Dominant failure mode analysis using representative samples obtained by multiple response surfaces method, Probabilistic Engineering Mechanics, 59, Article 103005.
DOI: 10.1016/j.probengmech.2019.103005
Song, J.W.; Valdebenito, M.; Wei, P.F.; Beer, M.; Lu, Z.Z. (2020): Non-intrusive imprecise stochastic simulation by line sampling, Structural Safety, 84, Article 101936.
DOI: https://doi.org/10.1016/j.strusafe.2020.101936
Wei, P.F.; Zhang, X.; Beer, M. (2020): Adaptive Experiment Design for Probabilistic Integration, Computer Methods in Applied Mechanics and Engineering, 365, Article 113035.
DOI: 10.1016/j.cma.2020.113035
Yan, W.J.; Zhao, M.Y.; Beer, M.; Ren, W.X.; Chronopoulos, D. (2020): A Unified Scheme to Solving Arbitrary Complex-valued Ratio Distribution with Application to Statistical Inference for Frequency Response Functions and Transmissibility Functions, Mechanical Systems and Signal Processing, 145, Article 106886.
Feng, J.W.; Liu, L.; Wu, D.; Li, G.Y.; Beer, M.; Gao, W. (2019): Dynamic reliability analysis using the extended support vector regression (X-SVR), Mechanical Systems and Signal Processing, 126, 368-391.
DOI: 10.1016/j.ymssp.2019.02.027
Fragkoulis, V.C.; Kougioumtzoglou, I.A.; Pantelous, A.A.; Beer, M. (2019): Non-stationary response statistics of nonlinear oscillators with fractional derivative elements under evolutionary stochastic excitation, Nonlinear Dynamics, 7, 1–13, doi 10.1007/s11071-019-05124-0.
Mitseas, I.P.; Beer, M. (2019): Modal decomposition method for response spectrum based analysis of nonlinear and non-classically damped systems, Mechanical Systems and Signal Processing, 131:469-485.
DOI: 10.1016/j.ymssp.2019.05.056
Song, Y.P.; Chen, J.B.; Beer, M.; Comerford, L. (2019): Wind Speed Field Simulation via Stochastic Harmonic Function Representation based on Wavenumber-Frequency Spectrum, ASCE Journal of Engineering Mechanics, 145(11), 04019086.
Wang, C.; Zhang, H.; Beer, M. (2019): Structural Time-dependent Reliability Assessment with A New Power Spectral Density Function, ASCE's Journal of Structural Engineering, 145(12): 04019163, 10 pages.
DOI: 10.1061/(ASCE)ST.1943-541X.0002476
Zhang, Y.; Gomes, A.T.; Beer, M.; Neumann, I.; Nackenhorst, U.; Kim, C.-W. (2019): Reliability analysis with consideration of asymmetrically dependent variables: discussion and application to geotechnical examples, Reliability Engineering and System Safety, 185, 261-277.
DOI: 10.1016/j.ress.2018.12.025
de Angelis, M.; Patelli, E.; Beer, M. (2017): Forced Monte Carlo Simulation Strategy for the Design of Maintenance Plans with Multiple Inspections, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(2), D4016001, 1–9.
DOI: 10.1061/AJRUA6.0000868
Jiang, Y.B.; Luo, J.; Beer, M.; Patelli, E.; Broggi, M.; He, Y.H.; Zhang, J.R. (2017): Multiple response surfaces method with advanced classification of samples for structural failure function fitting, Structural Safety; 64: 87-97.
DOI: 10.1016/j.strusafe.2016.10.002
Mitseas, I. P.; Kougioumtzoglou, I. A.; Beer, M. (2016): An approximate stochastic dynamics approach for nonlinear structural system performance-based multi-objective optimum design, Structural Safety; 60: 67-76.
DOI: 10.1016/j.strusafe.2016.01.003
Beer, M.; Liebscher, M. (2008): Designing robust structures - A nonlinear simulation based approach, Computers and Structures; 86(10): 1102—1122.
Spanos, P. D.; Beer, M.; Red-Horse, J. (2007): Karhunen–Loéve Expansion of Stochastic Processes with a Modified Exponential Covariance Kernel, Journal of Engineering Mechanics; 133(7): 773—779.