Abstract
Engineers must make decisions or advise decisions makers in problems involving uncertainty and risk. Engineering risk assessments support engineers and scientists in this task, by providing a structured approach to understanding and modeling the risks. Such risk assessments are based on a quantitative engineering modeling approach, which differs from the actuarial approach to risk modeling. Because of limited data, engineers must utilize all available information from multiple sources, including physical and logical models, observed data and expert knowledge. This information is uncertain and often contradicting. The methods presented in this chapter help engineers to consistently combine this information to come up with best estimates of risk and optimal decision support. They also help the engineer in understanding the limitations and sensitivity of risk estimates and facilitate the communication and comparison of risks. Finally, they enable the definition of clear criteria for assessing the acceptability and optimality of engineering solutions to reducing risk.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Selected Bibliography
T. Aven, Foundations of Risk Analysis. A Knowledge and Decision-Oriented Perspective (Wiley, Chichester, 2005)
J.R. Benjamin, C.A. Cornell, Probability, Statistics, and Decision for Civil Engineers (McGraw-Hill, New York, 1970)
I. Papaioannou, D. Straub, Reliability updating in geotechnical engineering including spatial variability of soil. Comput. Geotech. 42, 44–51 (2012)
M.G. Stewart, R.E. Melchers, Probabilistic Risk Assessment of Engineering Systems (Chapman & Hall, London, 1997)
Straub, Lecture notes in engineering risk analysis. TU München (2011)
Straub, Lecture notes in structural reliability methods. TU München (2011)
Additional Literature
G.E. Apostolakis, How useful is quantitative risk assessment? Risk Anal. 24(3), 515–520 (2004)
S.-K. Au, J.L. Beck, Estimation of small failure probabilities in high dimensions by subset simulation. Probab. Eng. Mech. 16, 263–277 (2001)
T. Aven, J.E. Vinnem, On the use of risk acceptance criteria in the offshore oil and gas industry. Reliab. Eng. Syst. Saf. 90(1), 15–24 (2005)
R.E. Barlow, F. Proschan, Mathematical Theory of Reliability. Classics in Applied Mathematics, vol. 17 (SIAM, Philadelphia, 1996)
E.S. Beckjord, M.A. Cunningham, J.A. Murphy, Probabilistic safety assessment development in the United States 1972–1990. Reliab. Eng. Syst. Saf. 39(2), 159–170 (1993)
M.T. Bensi, A. Der Kiureghian, D. Straub, A Bayesian network methodology for infrastructure seismic risk assessment and decision support. PEER Report 2011/02, Pacific Earthquake Engineering Research Center, University of California, Berkeley (2011)
K. Breitung, Asymptotic approximations for multinormal integrals. J. Eng. Mech., Trans. ASCE 110(3), 357–366 (1984)
C.G. Bucher, U. Bourgund, A fast and efficient response surface approach for structural reliability problems. Struct. Saf. 7(1), 57–66 (1990)
S. Coles, L.R. Pericchi, S. Sisson, A fully probabilistic approach to extreme rainfall modeling. J. Hydrol. 273(1–4), 35–50 (2003)
R.M. Cooke, J.M. van Noortwijk, Local probabilistic sensitivity measures for comparing FORM and Monte Carlo calculations illustrated with dike ring reliability calculations. Comput. Phys. Commun. 117(1–2), 86–98 (1999)
C. Czado, E.C. Brechmann, Bayesian risk analysis, in Risk – A Multidisciplinary Introduction, ed. by C. Klüppelberg, D. Straub, I. Welpe (2014)
A. Der Kiureghian, P.-L. Liu, Structural reliability under incomplete probability information. J. Eng. Mech., Trans. ASCE 112(1), 85–104 (1986)
DIN, Eurocode 0—basis of structural design (EN 1990:2002). Deutsches Institut für Normung e.V. (2001)
O. Ditlevsen, H.O. Madsen, Structural Reliability Methods (Wiley, New York, 1996)
S. Engelund, R. Rackwitz, A benchmark study on importance sampling techniques in structural reliability. Struct. Saf. 12(4), 255–276 (1993)
M.H. Faber, I.B. Kroon, E. Kragh, D. Bayly, P. Decosemaeker, Risk assessment of decommissioning options using Bayesian networks. J. Offshore Mech. Arct. Eng. 124(4), 231–238 (2002)
V. Fasen, C. Klüppelberg, A. Menzel, Quantifying extreme risks, in Risk – A Multidisciplinary Introduction, ed. by C. Klüppelberg, D. Straub, I. Welpe (2014)
A. Friis-Hansen, Bayesian networks as a decision support tool in marine applications. PhD thesis, DTU, Lyngby, Denmark (2000)
A. Grêt-Regamey, D. Straub, Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS. Nat. Hazards Earth Syst. Sci. 6(6), 911–926 (2006)
A. Høyland, M. Rausand, System Reliability Theory. Models and Statistical Methods. A Wiley-Interscience Publication (Wiley, New York, 1994)
HSE, Reducing Risks, Protecting People. HSE’s Decision-Making Process. JHSE Books (Health and Safety Executive, Liverpool, 2001)
F.V. Jensen, T.D. Nielsen, Bayesian Networks and Decision Graphs. Information Science and Statistics (Springer, New York, 2007)
R.B. Jongejan, How safe is safe enough? The government’s response to industrial and flood risks. PhD thesis, TU, Delft, NL (2008)
A. Lentz, Acceptability of civil engineering decisions involving human consequences. PhD thesis, TU München (2007)
L.D. Lutes, S. Sarkani, Random Vibrations. Analysis of Structural and Mechanical Systems (Elsevier/Butterworth/Heinemann, Amsterdam, 2004)
R.E. Melchers, Structural Reliability Analysis and Prediction (Wiley, New York, 1999)
NTSB, Aviation accident statistics. National Transportation Safety Board, US (2010). Retrieved June 19, 2011
A. Papoulis, S.U. Pillai, Probability, Random Variables, and Stochastic Processes (McGraw-Hill, Boston, 2009)
M.E. Paté-Cornell, Quantitative safety goals for risk management of industrial facilities. Struct. Saf. 13(3), 145–157 (1994)
J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. The Morgan Kaufmann Series in Representation and Reasoning (Morgan Kaufmann, San Mateo, 1988)
R. Rackwitz, Reliability analysis—a review and some perspectives. Struct. Saf. 23(4), 365–395 (2001)
R. Rackwitz, Optimal and acceptable technical facilities involving risks. Risk Anal. 24(3), 675–695 (2004)
R. Rackwitz, B. Fiessler, Structural reliability under combined load sequences. Comput. Struct. 9, 489–494 (1978)
R.Y. Rubinstein, D.P. Kroese, Simulation and the Monte Carlo Method (Wiley-Interscience, New York, 2007)
S.J. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (Prentice-Hall, Englewood Cliffs, 2003)
J. Song, A. Der Kiureghian, Bounds on system reliability by linear programming. J. Eng. Mech., Trans. ASCE 129(6), 627–636 (2003)
D. Straub, Stochastic modeling of deterioration processes through dynamic Bayesian networks. J. Eng. Mech., Trans. ASCE 135(10), 1089–1099 (2009)
D. Straub, Reliability updating with equality information. Probab. Eng. Mech. 26(2), 254–258 (2011)
D. Straub, A. Der Kiureghian, Bayesian network enhanced with structural reliability methods. Part A: theory. J. Eng. Mech., Trans. ASCE 136(10), 1248–1258 (2010)
D. Straub, M.H. Faber, Risk based inspection planning for structural systems. Struct. Saf. 27(4), 335–355 (2005)
D. Straub, I. Welpe, Decision-making under risk: a normative and behavioral perspective, in Risk – A Multidisciplinary Introduction, ed. by C. Klüppelberg, D. Straub, I. Welpe (2014)
D. Straub, M.T. Bensi, A. Der Kiureghian, Spatial modeling of earthquake hazard and infrastructure performance through Bayesian networks, in Proc. ASCE Engineering Mechanics ’08 Conference, University of Minnesota, Minneapolis (2008)
B. Sudret, Meta-models for structural reliability and uncertainty quantification, in Proc. Asian-Pacific Symposium on Structural Reliability and Its Applications, Singapore (2012)
B. Vogel-Heuser, S. Rösch, Integrated modeling of complex production automation systems to increase dependability, in Risk – A Multidisciplinary Introduction, ed. by C. Klüppelberg, D. Straub, I. Welpe (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Straub, D. (2014). Engineering Risk Assessment. In: Klüppelberg, C., Straub, D., Welpe, I. (eds) Risk - A Multidisciplinary Introduction. Springer, Cham. https://doi.org/10.1007/978-3-319-04486-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-04486-6_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04485-9
Online ISBN: 978-3-319-04486-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)