Publikationen von Dr. techn. Matteo Broggi

Buchbeiträge

  • Behrensdorf, J.; Broggi, M.; Beer, M. (2018): Efficient Reliability and Risk Analysis of Complex Interconnected SystemsIn: Beer, M.; Huang, H.W.; Ayyub, B.M.; Zhang, D.M.; Phillips, B.M. (eds.), Resilience Engineering for Urban Tunnels, American Society of Civil Engineers, Infrastructure Resilience Publications (IRP) IRP 2, 43–54.
    DOI: 10.1061/9780784415139.ch04

Journal-Artikel

  • Behrensdorf, J.; Broggi, M.; Beer, M. (2024): Interval Predictor Model for the Survival Signature using Monotone Radial Basis FunctionsASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, in press.
  • Shi, Y.; Behrensdorf, J.; Zhou, J.Y.; Hu, Y.; Broggi, M.; Beer, M. (2024): Network Reliability Analysis through Survival Signature and Machine Learning TechniquesReliability Engineering and System Safety, 242, Article 109806
    DOI: 10.1016/j.ress.2023.109806
  • Ding, C.; Dang, C.; Valdebenito, M.A.; Faes, M.G.R.; Broggi, M.; Beer, M. (2023): First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approachMechanical Systems and Signal Processing, 185, Article 109775.
    DOI: 10.1016/j.ymssp.2022.109775
  • Feng, C.X.; Faes, M.; Broggi, M.; Dang, C.; Yang, J.S.; Zheng, Z.B.; Beer, M. (2023): Application of interval field method to the stability analysis of slopes in presence of uncertaintiesComputers and Geotechnics, 153, Article 105060.
    DOI: 10.1016/j.compgeo.2022.105060
  • Grashorn, J.; Urrea-Quintero, J.-H.; Broggi, M.; Chamoin, L.; Beer, M. (2023): Transport map Bayesian parameter estimation for dynamical systemsProceedings in Applied Mathematics and Mechanics, 23(1), Article e202200136
    DOI: 10.1002/pamm.202200136
  • Persoons, A.; Wei, P.F.; Broggi, M.; Beer, M. (2023): A new reliability method combining adaptive Kriging and active variance reduction using multiple importance samplingStructural and Multidisciplinary Optimization, 66, Article 144.
    DOI: 10.1007/s00158-023-03598-6
  • Winnewisser, N.R.; Salomon, J.; Broggi, M.; Beer, M (2023): The Concept of Diagonal Approximated Signature: New Surrogate Modeling Approach for Continuous-State Systems in the Context of Resilience OptimizationDisaster Prevention and Resilience, 2(2):4.
    DOI: 10.20517/dpr.2023.03
  • Faes, M.G.R-; Broggi, M.; Chen, G., Phoon, K.K.; Beer M. (2022): Distribution-free P-box processes based on translation theory: definition and simulationProbabilistic Engineering Mechanics, 69, Article 103287.
    DOI: 10.1016/j.probengmech.2022.103287
  • Faes, M.G.R.; Broggi, M.; Spanos, P.D.; Beer, M. (2022): Elucidating appealing features of differentiable auto-correlation functions: a study on the modified exponential kernelProbabilistic Engineering Mechanics, 69, Article 103269.
    DOI: 10.1016/j.probengmech.2022.103269
  • Kitahara, M.; Bi, S.F.; Broggi, M.; Beer, M. (2022): Nonparametric Bayesian stochastic model updating with hybrid uncertaintiesMechanical Systems and Signal Processing, 163, Article 108195.
  • Kitahara, M.; Song, J.W.; Wei, P.F.; Broggi, M.; Beer, M. (2022): A distributionally robust approach for mixed aleatory and epistemic uncertainties propagationAIAA Journal, in press.
  • Kitahara, M.;Bi, S.F.; Broggi, M.; Beer, M. (2022): Distribution-free stochastic model updating of dynamic systems with parameter dependenciesStructural Safety, 97, Article 102227
  • Lye, A.; Kitahara, M.; Broggi, M.; Patelli, E. (2022): Robust optimization of a dynamic Black-box system under severe uncertainty: A distribution-free frameworkMechanical Systems and Signal Processing, 167, Article 108522.
  • Salomon, J.; Behrensdorf, J.; Winnewisser, N.; Broggi, M.; Beer, M. (2022): Multidimensional Resilience Decision-Making for Complex and Substructured SystemsResilient Cities and Structures, 1, 61–78.
    DOI: 10.1016/j.rcns.2022.10.005
  • Bai, Y.T.; Li, Y.S.; Tang, Z.Y.; Bittner, M.; Broggi, M.; Beer, M. (2021): Seismic collapse fragility of low-rise steel moment frames with mass irregularity based on shaking table testBulletin of Earthquake Engineering.
    DOI: 10.1007/s10518-021-01076-2
  • Behrensdorf, J.; Regenhardt, T.-E.; Broggi, M.; Beer, M. (2021): Numerically efficient computation of the survival signature for the reliability analysis of large networksReliability Engineering and System Safety, 216, Article 107935.
    DOI: 10.1016/j.ress.2021.107935
  • Jerez, D.J.; Jensen, H.A.; Beer, M.; Broggi, M. (2021): Contaminant Source Identification in Water Distribution Networks: A Bayesian FrameworkMechanical Systems and Signal Processing, 159, Article 107834.
    DOI: https://doi.org/10.1016/j.ymssp.2021.107834
  • Kitahara, M.; Bi, S.F.; Broggi, M.; Beer, M. (2021): Bayesian Model Updating in Time Domain with Metamodel-based Reliability MethodASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(3), Article 04021030.
  • Kitahara, M.; Broggi, B.; Beer, M (2021): Residual seismic performance estimation of seismic-isolated bridges based on model updating using approximate Bayesian computationJournal of Japan Society of Civil Engineers, part A1 (Structural Engineering & Earthquake Engineering), 77, I_61–I_70.
  • Salomon, J.; Winnewisser, N.; Wei, P.F.; Broggi, M.; Beer, M. (2021): Efficient Reliability Analysis of Complex Systems in Consideration of ImprecisionReliability Engineering and System Safety, 216, Article 107972.
    DOI: 10.1016/j.ress.2021.107972
  • Valdebenito, M.A.; Wei, P.F.; Song, J.W.; Beer, M.; Broggi, M. (2021): Failure Probability Estimation of a Class of Series Systems by Multidomain Line SamplingReliability Engineering and System Safety, 213, Article 107673.
  • Yang, L.C.; Bi, S.F.; Faes, M.G.R.; Broggi, M.; Beer, M. (2021): Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metricMechanical Systems and Signal Processing, 162, Article 107954.
  • Kitahara, M.; Broggi, M.; Beer, M. (2020): Efficient seismic performance estimation method by surrogate modeling based on adaptive Kriging and Markov chain Monte CarloJournal of Japan Society of Civil Engineers, part A2 (Applied Mechanics), 76, 75–86.
  • Mi, J.H, Beer, M.; Li, Y.F.; Broggi, M.; Cheng, Y.H. (2020): Reliability and Importance Analysis of Uncertain System with Common Cause Failures Based on Survival SignatureReliability Engineering and System Safety, 201, Article 106988.
    DOI: https://doi.org/10.1016/j.ress.2020.106988
  • Mi, J.H; Li, Y.F.; Beer, M.; Broggi, M.; Cheng, Y.H. (2020): Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian networkMaintenance and reliability, 22 (1), 112–120.
    DOI: https://doi.org/10.17531/ein.2020.1.13
  • Behrensdorf, J.; Broggi, M.; Beer, M. (2019): Reliability analysis of networks interconnected with copulasASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 5(4), Article 041006.
    DOI: 10.1115/1.4044043
  • Bi, S.F.; Broggi, M.; Wei, P.F.; Beer, M. (2019): The Bhattacharyya distance: enriching the P-box in stochastic sensitivity analysisMechanical Systems and Signal Processing, 129, pp. 265-281.
    DOI: 10.1016/j.ymssp.2019.04.035
  • Faes, M.; Broggi, M.; Patelli, E.; Govers, Y.; Mottershead, J.; Beer, M.; Moens, D. (2019): A multivariate interval approach for inverse uncertainty quantification with limited experimental dataMechanical Systems and Signal Processing, 118, 534–548.
    DOI: 10.1016/j.ymssp.2018.08.050
  • Faes, M.; Sadeghi, J.; Broggi, M.; De Angelis, M.; Patelli, E.; Beer, M.; Moens, D. (2019): On the robust estimation of small failure probabilities for strong non-linear modelsASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 5, Article 041007.
    DOI: 10.1115/1.4044044
  • He, L.X.; Gomes, A.T.; Broggi, M.; Beer, M. (2019): Failure Analysis of Soil Slopes with Advanced Bayesian NetworksPeriodica Polytechnica Civil Engineering.
    DOI: 10.3311/PPci.14092
  • He, L.X.; Wang, L.; Beer, M.; Liu, Y.; Broggi, M.; Bi, S.F. (2019): Estimation of failure probability in a braced excavation by Bayesian networks integrating with model updating approachesUnderground Space, 5(4), 315–323.
  • Miro, S.; Willeke, T.; Broggi, M.; Seume, J.R.; Beer, M. (2019): Reliability Analysis of an Axial Compressor Based on One-Dimensional Flow Modeling and Survival SignatureASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 5(3), art. no. 031003.
    DOI: 10.1115/1.4043150
  • Salomon, J.; Broggi, M.; Kruse, S.; Weber, S.; Beer, M. (2019): Resilience Decision-Making Method For Complex SystemsASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(2), Article 020901. | Datei |
    DOI: 10.1115/1.4044907
  • Song, J.W.; Wei, P.F.; Valdebenito, M.; Bi, S.F.; Broggi, M.; Beer, M.; Lei, Z.X. (2019): Generalization of non-intrusive imprecise stochastic simulation for mixed uncertain variablesMechanical Systems and Signal Processing, 134, 106316, 17 pages.
  • Wei, P.; Song, J.; Bi, S.; Broggi, M.; Beer, M.; Lu, Z.; Yue, Z. (2019): Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysisMechanical Systems and Signal Processing, 126:227-247.
    DOI: 10.1016/j.ymssp.2019.02.015
  • Wei, P.F.; Song, J.W.; Bi, S.F.; Broggi, M.; Beer, M.; Lu, Z.Z.; Yue, Z.F. (2019): Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimationMechanical Systems and Signal Processing, 124, 349-368.
    DOI: 10.1016/j.ymssp.2019.01.058
  • Bi, S.F.; Broggi, M.; Beer, M. (2018): The role of the Bhattacharyya distance in stochastic model updatingMechanical Systems and Signal Processing, 117: 437-452.
    DOI: 10.1016/j.ymssp.2018.08.017
  • Rocchetta, R.; Broggi, M.; Huchet, Q.; Patelli, E. (2018): On-line Bayesian model updating for structural health monitoringMechanical Systems and Signal Processing; 103: 174-195.
    DOI: 10.1016/j.ymssp.2017.10.015
  • Rocchetta, R.; Broggi, M.; Patelli, E. (2018): Do we have enough data? Robust reliability via uncertainty quantificationApplied Mathematical Modelling, 54: 710-721.
    DOI: 10.1016/j.apm.2017.10.020
  • 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 fittingStructural Safety; 64: 87-97.
    DOI: 10.1016/j.strusafe.2016.10.002
  • Patelli, E.; Govers, Y.; Broggi, M.; Martins Gomes, H.; Link, M.; Mottershead, J. (2017): Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test dataArchive of Applied Mechanics, Volume 87, Issue 5, pp 905–925.
    DOI: 10.1007/s00419-017-1233-1
  • Evans, Ll.M.; Arregui-Mena, J.D.; Mummery, P.M.; Akers, R.; Surrey, E.; Shterenlikht, A.; Broggi, M.; Margetts, L. (2016): Use of massively parallel computing to improve modelling accuracy within the nuclear sectorThe International Journal of Multiphysics, 10, 2.
    DOI: 10.21152/1750-9548.10.2.215
  • Rocchetta, R.; Patelli, E.; Broggi, M.; Schewe, S. (2016): Robust probabilistic risk/safety analysis of complex systems and critical infrastructuresReliability Engineering and System Safety, 136, 47-61.
  • Venturini, Tiziano; Trefolini, Emanuele; Patelli, Edoardo; Broggi, Matteo; Tuliani, Giacomo; Disperati, Leonardo (2016): Mapping slope deposits depth by means of cluster analysis: a comparative assessmentRendiconti online della societa geologica italiana, Vol. 39/2016: 47-50.
    DOI: 10.3301/ROL.2016.44
  • Patelli, E.; Alvarez, D. A.; Broggi, M.; de Angelis, M. (2015): Uncertainty Management in Multidisciplinary Design of Critical Safety SystemsJournal of Aerospace Information Systems; 12(1): 140-169.
  • Patelli, E.; Murat Panayirci, H.; Broggi, M.; Goller, B.; Beaurepaire, P.; Pradlwarter, H. J.; Schuëller, G. I. (2012): General purpose software for efficient uncertainty management of large finite element modelsFinite Elements in Analysis and Design; 51(1): 31—48.
  • Zio, E.; Broggi, M.; Golea, L. R.; Pedroni, N. (2012): Failure and reliability predictions by infinite impulse response locally recurrent neural networksChemical Engineering Transactions; 26: 117—122.
  • Broggi, M.; Calvi, A.; Schuëller, G. (2011): Reliability assessment of axially compressed composite cylindrical shells with random imperfectionsInternational Journal of Structural Stability and Dynamics; 11(2): 215—236.
    DOI: 10.1142/S0219455411004063
  • Broggi, M.; Schuëller, G. (2011): Efficient modeling of imperfections for buckling analysis of composite cylindrical shellsEngineering Structures; 33(5): 1796—1806.
  • Goller, B.; Broggi, M.; Calvi, A.; Schuëller, G. (2011): A stochastic model updating technique for complex aerospace structuresFinite Elements in Analysis and Design; 47(7): 739—752.
  • Zio, E.; Broggi, M.; Pedroni, N. (2009): Nuclear reactor dynamics on-line estimation by Locally Recurrent Neural NetworksProgress in Nuclear Energy; 51(3): 573—581.
  • Zio, E.; Pedroni, N.; Broggi, M.; Golea, L. R. (2009): Modelling the dynamics of the lead bismuth eutectic experimental accelerator driven system byan infinite impulse response locally recurrent neural networkNuclear Engineering and Technology; 41(10): 1293—1306.