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Department of Mechanical Engineering

Papers in Journals with peer review and impact factor


  1. Ypsilantis, K., Faes, M., Lagaros, N. , Aage, N., Moens, D. (2024)
    Robust topology and discrete fiber orientation optimization under material uncertainty
    Computers and Structures
    Volume 300, 15 August 2024, 107421
    preprint (available for download)

  2. Jafari,J., Lara Montaño, O., Mirjalili,S., Faes, M.  (2024)
    A Meta-heuristic approach for Reliability-Based Design Optimization of Shell-and-Tube Heat Exchangers
    Applied Thermal Engineering
    Volume 248, Part A, 01 July 2024, 123161
    preprint (available for download)

  3. Awd, M. , Saeed, L. , Münstermann,S. , Faes, M. , Walther, F. (2024)
    Mechanistic machine learning for metamaterial fatigue strength design from first principles in additive manufacturing
    Materials and Design.
    Volume 241, May 2024, 112889
    preprint (available for download)

  4. Dang, C. , Cicirello, A. , Valdebenito, M. , Faes, M., Wei, P., Beer, M. (2024)
    Structural reliability analysis with extremely small failure probabilities: A quasi-Bayesian active learning method
    Probabilistic Engineering Mechanics.
    Volume 76, 03 April 2024, 103613
    paper (available for download)

  5. Dang, C. , Valdebenito, M., Wei, P. , Song, J., Beer, M. (2024)
    Bayesian active learning line sampling with log-normal process for rare event probability estimation.
    Reliability Engineering & System Safety.
    Volume 246, 03 April 2024, 110053
    paper (available for download)

  6. Feng,C., Valdebenito,  M.A. , Chwała, M.,  Liao,K., Broggi,M.,  Beer, M. (2024)
    Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments
    Journal of Rock Mechanics and Geotechnical Engineering.
    Volume 16, April 2024, 1140-1152
    paper (available for download)

  7. Dang, C., Faes, M. , Valdebenito, M. , Wei, P., Beer, M. (2024). 
    Partially Bayesian active learning cubature for structural reliability analysis with extremely small failure probabilities.
    Computer Methods in Applied Mechanics and Engineering.
    Volume 422 , 15 March 2024, 116828
    preprint (available for download)
  8. Chang, Q., Zhou, C., Faes, M. , Valdebenito, M. (2024).
    Design optimization with variable screening by interval-based sensitivity analysis.
    ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering.
    Article in Press.
    preprint (available for download)
  9. Acevedo, C., Valdebenito, M. , Gonzalez, I., Jensen, H., Faes, M. , Liu, Y. (2024)
    Control variates with splitting for aggregating results of Monte Carlo simulation and perturbation analysis
    Structural Safety.
    Volume 108 , May 2024, 102445
    paper (available for download)
  10. Wang, C., Beer, M., Faes, M. , Feng, D. (2024). 
    Resilience assessment under imprecise probability.
    ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 
    Volume 10, Issue 2 , June 2024, 101061
    preprint (available for download).
  11. Yuan, X., Zheng, W., Zhao, C., Valdebenito, M. , Faes, M., Dong, Y. (2024).
    Line sampling for time-variant failure probability estimation using adaptive combination approach.
    Reliability Engineering and System Safety.
    Volume 243 , March 2024, 109885
    preprint (available for download) .
  12. Jerez, D. Fragkoulis, V., Ni, P., Mitseas, I., Valdebenito, M., Faes, M., Beer, M. (2024).
    Operator norm-based determination of failure probability of nonlinear oscillators with fractional derivative elements subject to imprecise stationary Gaussian loads.
    Mechanical Systems and Signal Processing.
    Volume 208 , 15 February 2024, 111043
    preprint (available for download).
  13. Abdollahi, A., Shahraki, H., Faes, M., Rashki, M. (2024)
    Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach
    Structural Safety
    Volume 106 , January 2024, 102391
    10.1016/j. strusafe.2023.102391
    preprint (available for download)
  14. Song, J., Cui, Y., Wei, P., Valdebenito, M. , Zhang, W. (2024)
    Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis
    Reliability Engineering & System Safety
    Volume 241 , January 2024, 109613
    preprint (available for download



  1. Böddecker, M.,  Faes, M. , Menzel, A.,  Valdebenito, M. (2023).
    Effect of uncertainty of material parameters on stress triaxiality and Lode angle in finite elasto-plasticity - a variance-based global sensitivity analysis.
    Advances in Industrial and Manufacturing Engineering.
    Volume 7 , November 2023, 100128
    preprint (available for download)
  2. Hong, F., Wei, P., Song, J., Valdebenito, MA ,  Faes, M. , Beer, M. (2023).
    Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties
    Computer Methods in Applied Mechanics and Engineering
    Volume 417, Part A , December 2023, 116410
    preprint (available for download)
  3. Ypsilantis, K., Kazakis, G., Faes, M. , Ivens, J., Lagaros, N. Moens, D. (2023).
    A topology-based in-plane filtering technique for the combined topology and discrete fiber orientation optimization
    Computer Methods in Applied Mechanics and Engineering
    Volume 417, Part A , 1 December 2023, 116400
    preprint (available for download)
  4. Valdebenito,  M., Yuan, X., Faes, M. (2023)
    Augmented first-order reliability method for estimating fuzzy failure probabilities
    Structural Safety
    Volume 105, November 2023, 102380
    preprint (available for download)
  5. Van Bavel, B., Zhao, Y., Faes, M., Vandepitte, D., Moens, D. (2023).
    Efficient quantification of composite spatial variability: A multiscale framework that captures intercorrelation.
    Composite Structures.
    Volume 323, 1 November 2023, 117462
    preprint (available for download)
  6. Hong, F., Wei, P., Song, J., Faes, M., Valdebenito, M., Beer, M. (2023).
    Combining Data and Physical Models for Probabilistic Analysis: A Bayesian Augmented Space Learning Perspective.
    Probabilistic Engineering Mechanics.
    Volume 73, July 2023, 103474
    preprint (available for download)
  7. Dang, C., Valdebenito, M., Faes, M., Song, J., Wei, P., Beer, M. (2023).
    Structural reliability analysis by line sampling: A Bayesian active learning treatment.
    Structural Safety.
    Volume 104, September 2023, 102351
    preprint (available for download)
  8. Bogaerts, L., Dejans, A., Faes, M., Moens, D. (2023).
    A machine learning approach for efficient and robust resistance spot welding monitoring.
    Welding in the World.
    preprint (available for download)
  9. Rashki, M., Faes, M. (2023).
    No-Free-Lunch theorems for reliability analysis.
    ASCE/ASME Journal for Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering.
    Vol. 9, Issue 3, September 2023,
    preprint (available for download)
  10. Yuan, X., Valdebenito, M.A., Zhang, B., Faes, M., Beer, M. (2023).
    Efficient decoupling approach for reliability-based optimization based on augmented Line Sampling and combination algorithm.
    Computers & Structures.
    Volume 280, May 2023, 107003
    preprint (available for download)
  11. van Mierlo, C., Persoons, A., Faes, M., Moens, D. (2023).
    Robust design optimization of expensive stochastic simulators under lack-of-knowledge.
    ASCE/ASME Journal for Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering.
    Volume 9(2), 021205. 
    preprint (available for download)
  12. Behrendt, M., Faes, M., Valdebenito, M.A., Beer, M. (2023).
    Estimation of an imprecise power spectral density function with optimised bounds from scarce data for epistemic uncertainty quantification.
    Mechanical Systems and Signal Processing
    Volume 189, 15 April 2023, 110072.
    preprint (available for download)
  13. Yuan, X., Wang, S., Valdebenito, M.A., Faes, M., Beer, M. (2023).
    Sample regeneration algorithm for structural failure probability function estimation.
    Probabilistic Engineering Mechanics. 
    Volume 71, January 2023, 103387
    preprint (available for download)
  14. Fina, M., Lauff, C., Faes, M., Valdebenito, M., Wagner, W., Freitag. S. (2022).
    Bounding Imprecise Failure Probabilities in Structural Mechanics based on Maximum Standard Deviation.
    Structural Safety. 
    Volume 101, March 2023, 102293
    preprint (available for download)
  15. Yuan, X., Qian, Y. Chen, J. Faes, M., Valdebenito, M., Beer, M. (2023).
    Global failure probability function estimation based on an adaptive strategy and combination algorithm.
    Reliability Engineering and System Safety. 
    Volume 231, March 2023, 108937
    preprint (available for download)
  16. Van Mierlo, C., Persoons, A., Faes, M., Moens, D. (2023).
    Robust design optimisation under lack-of-knowledge uncertainty.
    Computers & Structures. 
    Volume 275, 15 January 2023, 106910
    preprint (available for download)
  17. Ding, C., Dang, C., Valdebenito, M., Faes, M., Broggi, M., Beer,M. (2022).
    First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approach.
    Mechanical Systems and Signal Processing. 
    Volume 185, 15 February 2023, 109775.
    preprint (available for download)
  18. Bartsoen, L., Faes, M.G.R., Skipper Andersen, M., Wirix-Speetjens, R., Moens, D., Jonkers, I., Vander Sloten, J. (2023).
    Bayesian parameter estimation of ligament properties based on tibio-femoral kinematics during squatting. 
    Mechanical Systems and Signal Processing, 
    Volume 182, 1 January 2023, 109525. 
    preprint (available for download)
  19. Zheng, Z., Valdebenito, M.A., Beer, M., Nackenhorst, U.
    A stochastic finite element scheme for solving partial differential equations defined on random domains.
    Computer Methods in Applied Mechanics and Engineering.
    Volume 405, 15 February 2023, 115860.
    preprint (available for download)
  20. Wang, X., Yang, L., Xie, M., Valdebenito, M.A., Beer, M.
    Bayesian maximum entropy method for stochastic model updating using measurement data and statistical information
    Mechanical Systems and Signal Processing
    Volume 188, 1 April 2023, 110012
    preprint (available for download)
  21. Feng, C., Faes, M., Broggi, M., Dang, C., Yang, K., Zheng, Z., Beer, M. (2023). 
    Application of interval field method to the stability analysis of slopes in presence of uncertainties.
    Computers & Geotechnics. 
    Volume 153, January 2023, 105060
    preprint (available for download)
  22. Z. Zheng and M. Valdebenito and M. Beer and U.Nackenhorst (2023)
    Simulation of random fields on random domains
    Probabilistic Engineering Mechanics
    Volume 73, July 2023, 103455 
    preprint (available for download)


  1. Bartsoen, L., Faes, M.G.R., Wirix-Speetjens, R., Moens, D., Jonkers, I., Vander Sloten, J. (2022).
    Probabilistic planning for ligament-balanced TKA - identification of critical ligament properties.
    Frontiers in Bioengineering and Biotechnology. 
    Volume 10.
    preprint (available for download)
  2. Dang, C., Valdebenito, M., Faes, M.G.R., Wei, P., Beer, M. (2022). 
    Structural Reliability Analysis, a Bayesian perspective, 
    Structural Safety,
    Volume 99, November 2022, 102259. 
    preprint (available for download)
  3. Wang, G., Faes, M.G.R., Shi, T., Peng, G. (2022).
    Extension of Dashpot Model with Elastoplastic Deformation and Rough Surface in Impact Behavior, 
    Chaos, Solitons and Fractals, 
    Volume 162, September 2022, 112402.
  4. Dang, C., Wei, P., Faes, M.G.R., Beer, M. (2022). 
    Bayesian probabilistic propagation of hybrid uncertainties: Estimation of response expectation function, its variable importance and bounds, 
    Computers & Structures, 
    Volume 270, 1 October 2022, 106860. 
    preprint (available for download)
  5. Ypsilantis, K.I., Faes, M.G.R., Ivens, J., Laragos, N.,  Moens, D.  (2022).
    An Approach for the Concurrent Homogenization-based Microstructure Type and Topology Optimization Problem, 
    Computers & Structures, 
    Volume 272, November 2022, 106859
    preprint (available for download)
  6. Dang, C., Wei, P., Faes, M., Valdebenito, M. A., Beer, M. (2022).
    Parallel adaptive Bayesian quadrature for rare event estimation, 
    Reliability Engineering and System Safety,
    Volume 225, September 2022, 108621.
    preprint (available for download)
  7. Zhao, Y., Yang, J., Faes, M., Bi, S., Wang, Y. (2022).
    The sub-interval similarity: A general uncertainty quantification metric for both stochastic and interval model updating,
    Mechanical Systems and Signal Processing,
    Vol.178, 109319,
    preprint (available for download)
  8. Faes, M., Broggi, M., Chen, G., Phoon, K.-K., Beer, M. (2022).
    Distribution-free P-box processes based on translation theory: definition and simulation. 
    Probabilistic Engineering Mechanics,
    Vol. 69, 103287
    preprint (available for download)
  9. Faes, M., Broggi, M., Spanos, P.D., Beer, M. (2022).
    Elucidating appealing features of differentiable auto-correlation functions: a study on the modified exponential kernel. 
    Probabilistic Engineering Mechanics,
    Vol. 69, 103287

    preprint (available for download)
  10. Callens, R., Faes, M., Moens, D. (2022). 
    Multilevel Quasi-Monte Carlo For Interval Analysis. 
    International Journal For Uncertainty Quantification,
    Issue 12(4). pp. 1–19.
    doi: 10.1615/Int.J.UncertaintyQuantification.2022039245

  11. Dang, C., Wei, P., Faes, M. , Valdebenito, M. , Beer, M. (2022).
    Interval uncertainty propagation by a parallel Bayesian global optimization method. 
    Applied Mathematical Modeling,
    Vol. 128, Pp. 220-235
    preprint (available for download)