Engineering Informatics to Support Civil Systems Engineering Practice

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10864)


Systems engineering is the interdisciplinary engineering field that focuses on the design of complex physical systems to optimize the system’s performance over its life-cycle. To support such optimization efforts a number of computational modeling methods are required: ontological modeling, stochastic modeling, and process simulation modeling. Despite this need, the field of systems engineering has mainly focused on the development and discussion of managerial methods. This paper tries to provide a first starting point for a discussion about a framework to understand how the above mentioned computational methods can support system engineers. The paper introduces a first set of important methods and tries to integrate them in an overall framework for analysing engineered systems from different points of view. For each of the methods we also provide a simple illustrative example from our ongoing systems engineering teaching efforts at the TU Berlin.


International Roughness Index Transverse Span Bridge Deterioration Equivalent Single Axle Loads Parametric Modeling Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Farlow, S.J.: Partial differential equations for scientists and engineers. Courier Corporation, North Chelmsford (1993)Google Scholar
  2. Flager, F., Welle, B., Bansal, P., Soremekun, G., Haymaker, J.: Multidisciplinary process integration and design optimization of a classroom building. J. Inf. Technol. Constr. (ITcon) 14(38), 595–612 (2009)Google Scholar
  3. Forrester, A., Keane, A., et al.: Engineering Design Via Surrogate Modelling: A Practical Guide. Wiley, Hoboken (2008)CrossRefGoogle Scholar
  4. Geyer, P.: Multidisciplinary grammars supporting design optimization of buildings. Res. Eng. Design 18(4), 197–216 (2008)CrossRefGoogle Scholar
  5. Kapurch, S.J.: NASA Systems Engineering Handbook. Diane Publishing, Collingdale (2010)Google Scholar
  6. Krötzsch, M., Simancik, F., Horrocks, I.: A description logic primer. arXiv preprint arXiv:1201.4089 (2012)
  7. Lantz, B.: Machine Learning with R. Packt Publishing Ltd., Birmingham (2013)Google Scholar
  8. Luhmann, N.: Soziale Systeme, vol. 478. Suhrkamp, Frankfurt am Main (1984)Google Scholar
  9. Luzeaux, D., et al.: Systems of Systems. Wiley, Hoboken (2013)CrossRefGoogle Scholar
  10. Matloff, N.: From Algorithms to Z-scores: Probabilistic and Statistical Modeling in Computer Science. Creative Commons License (2009)Google Scholar
  11. Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: A guide to creating your first ontology (2001)Google Scholar
  12. Pahl, G., Beitz, W.: Engineering Design: A Systematic Approach. Springer Science & Business Media, London (2013)Google Scholar
  13. Ropohl, G.: Allgemeine Technologie: eine Systemtheorie der Technik. KIT Scientific Publishing, Karlsruhe (2009)CrossRefGoogle Scholar
  14. Ropohl, G.: Allgemeine Systemtheorie: Einführung in transdisziplinäres Denken. edition sigma, Berlin (2012)Google Scholar
  15. Saltelli, A., Chan, K., Scott, E.M., et al.: Sensitivity Analysis, vol. 1. Wiley, New York (2000)zbMATHGoogle Scholar
  16. Wainer, G.A., Mosterman, P.J.: Discrete-Event Modeling and Simulation: Theory and Applications. CRC Press, Boca Raton (2016)Google Scholar
  17. Ziari, H., Sobhani, J., Ayoubinejad, J., Hartmann, T.: Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods. Int. J. Pavement Eng. 17(9), 776–788 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Civil Systems EngineeringTU BerlinBerlinGermany

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