Sensitivity of Value of Information to Model and Measurement Errors
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 4
Abstract
The value of information (VoI) framework, based on Bayesian preposterior analysis, can be used to estimate the most likely benefit associated with a particular structural health monitoring (SHM) strategy. The errors within the VoI framework can be traced to the underlying predictive models and the inspection instruments. Conventional VoI analysis assumes a nonerroneous predictive model. Also, it considers only the (unbiased) random errors associated with inspection instruments. In this paper, the authors propose a VoI framework that explicitly considers the different uncertain errors within the predictive models and inspection instruments. Global sensitivity analysis and parametric investigations are performed to study the sensitivity of the VoI framework to various error parameters by estimating Sobol’ indices through Monte Carlo simulations and polynomial chaos expansions. It is found that the VoI framework is highly sensitive to the errors within the predictive model. This study recommends that any VoI analysis should be preceded with a thorough quantification of the errors within the predictive models lest an inaccurate estimate of the VoI is obtained.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The following data, models, or code generated or used during the study are available from the corresponding author by request: (1) Python 3.7 code for estimating the Sobol’ indices for EVI or NEVI using MCS and PCE, and (2) Python 3.7 code for estimating EVI and NEVI for a given design of input parameters.
Acknowledgments
The authors are grateful to the IITB-Monash Research Academy for funding this collaborative study. The authors would like to thank the contributions of the COST Action Project TU1402: Quantifying the Value of Structural Health Monitoring (Thöns et al. 2017). The resources made available online in the form of scientific papers, reports, workshops, and videos have greatly helped the authors in studying and understanding the value of information framework for quantifying the benefits of structural health monitoring.
References
Agdas, D., J. Rice, J. Martinez, and I. Lasa. 2016. “Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods.” J. Perform. Constr. Facil. 30 (3): 04015049. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000802.
Ang, A. H.-S., and W. H. Tang. 2006. Probability concepts in engineering planning: Emphasis on applications to civil and environmental engineering. 2nd ed. Hoboken, NJ: Wiley.
Biondini, F., and D. Frangopol. 2016. “Life-cycle performance of deteriorating structural systems under uncertainty: Review.” J. Struct. Eng. 142 (9): F4016001. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001544.
Blatman, G., and B. Sudret. 2010. “An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis.” Probab. Eng. Mech. 25 (2): 183–197. https://doi.org/10.1016/j.probengmech.2009.10.003.
Blatman, G., and B. Sudret. 2011. “Adaptive sparse polynomial chaos expansion based on least angle regression.” J. Comput. Phys. 230 (6): 2345–2367. https://doi.org/10.1016/j.jcp.2010.12.021.
Bolado, R. L., and A. C. Badea. 2009. Review of sensitivity analysis methods and experience for geological disposal of radioactive waste and spent nuclear fuel. Petten, Netherlands: European Commission Joint Research Committee.
Caleyo, F., L. Alfonso, J. Espina-Hernández, and J. Hallen. 2007. “Criteria for performance assessment and calibration of in-line inspections of oil and gas pipelines.” Meas. Sci. Technol. 18 (7): 1787–1799. https://doi.org/10.1088/0957-0233/18/7/001.
Cappello, C., D. Zonta, and B. Glisic. 2016. “Expected utility theory for monitoring-based decision-making.” Proc. IEEE 104 (8): 1647–1661. https://doi.org/10.1109/JPROC.2015.2511540.
Chang, P., A. Flatau, and S. Liu. 2003. “Review paper: Health monitoring of civil infrastructure.” Struct. Health Monit. 2 (3): 257–267. https://doi.org/10.1177/1475921703036169.
Der Kiureghian, A., and O. Ditlevsen. 2009. “Aleatory or epistemic? Does it matter?” Struct. Saf. 31 (2): 105–112. https://doi.org/10.1016/j.strusafe.2008.06.020.
Faber, M. 2005. “On the treatment of uncertainties and probabilities in engineering decision analysis.” J. Offshore Mech. Arct. Eng. 127 (3): 243–248. https://doi.org/10.1115/1.1951776.
Faber, M., and S. Thöns. 2014. “On the value of structural health monitoring.” In Proc., European Safety and Reliability Conf. on Safety, Reliability and Risk Analysis: Beyond the Horizon, 2535–2544. London: CRC Press.
Faroz, S., N. Pujari, and S. Ghosh. 2016. “Reliability of a corroded RC beam based on Bayesian updating of the corrosion model.” Eng. Struct. 126 (Nov): 457–468. https://doi.org/10.1016/j.engstruct.2016.08.003.
Folsø, R., S. Otto, and G. Parmentier. 2002. “Reliability-based calibration of fatigue design guidelines for ship structures.” Mar. Struct. 15 (6): 627–651. https://doi.org/10.1016/S0951-8339(01)00031-4.
Frangopol, D. 2011. “Life-cycle performance, management, and optimisation of structural systems under uncertainty: Accomplishments and challenges.” Struct. Infrastruct. Eng. 7 (6): 389–413. https://doi.org/10.1080/15732471003594427.
Frangopol, D., and M. Liu. 2007. “Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost.” Struct. Infrastruct. Eng. 3 (1): 29–41. https://doi.org/10.1080/15732470500253164.
Frangopol, D., and M. Soliman. 2016. “Life-cycle of structural systems: Recent achievements and future directions.” Struct. Infrastruct. Eng. 12 (1): 1–20. https://doi.org/10.1080/15732479.2014.999794.
Frangopol, D., A. Strauss, and S. Kim. 2008. “Bridge reliability assessment based on monitoring.” J. Bridge Eng. 13 (3): 258–270. https://doi.org/10.1061/(ASCE)1084-0702(2008)13:3(258).
Ghosn, M., et al. 2016. “Performance indicators for structural systems and infrastructure networks.” J. Struct. Eng. 142 (9): F4016003. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001542.
Herman, J., and W. Usher. 2017. “SALib: An open-source Python library for sensitivity analysis.” The Journal of Open Source Software 2 (9): 97. https://doi.org/10.21105/joss.00097.
JCSS (Joint Committee on Structural Safety). 2001. Probabilistic model code.. London: Joint Committee on Structural Safety.
Kennedy, M., and A. O’Hagan. 2001. “Bayesian calibration of computer models.” J. R. Stat. Soc. Ser. B: Stat. Methodol. 63 (3): 425–464. https://doi.org/10.1111/1467-9868.00294.
Konakli, K., B. Sudret, and M. H. Faber. 2016. “Numerical investigations into the value of information in lifecycle analysis of structural systems.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 2 (3): B4015007. https://doi.org/10.1061/AJRUA6.0000850.
Malings, C., and M. Pozzi. 2016. “Value of information for spatially distributed systems: Application to sensor placement.” Reliab. Eng. Syst. Saf. 154 (Oct): 219–233. https://doi.org/10.1016/j.ress.2016.05.010.
Malings, C., and M. Pozzi. 2017. “Optimal sensor placement and scheduling with value of information for spatio-temporal infrastructure system management.” In Proc., 12th Int. Conf. on Structural Safety and Reliability, 3320–3330. Vienna, Austria: TU-Verlag.
Omenzetter, P., M. Limongelli, and U. Yazgan. 2016. “Quantifying the value of seismic structural health monitoring of buildings.” In Vol. 1 of Proc., 8th European Workshop on Structural Health Monitoring, 510–519. Bad Breisig, Germany: NDT.net.
Otieno, M., H. Beushausen, and M. Alexander. 2012. “Prediction of corrosion rate in reinforced concrete structures—A critical review and preliminary results.” Mater. Corros. 63 (9): 777–790. https://doi.org/10.1002/maco.201106282.
Papaioannou, I., K. Breitung, and D. Straub. 2018. “Reliability sensitivity estimation with sequential importance sampling.” Struct. Saf. 75 (Nov): 24–34. https://doi.org/10.1016/j.strusafe.2018.05.003.
Pozzi, M., and A. Der Kiureghian. 2011. “Assessing the value of information for long-term structural health monitoring.” In Vol. 7984 of Proc., SPIE—The International Society for Optical Engineering. Bellingham, WA: Society of Photo-optical Instrumentation Engineers.
Qin, J., S. Thöns, and M. Faber. 2015. “On the value of SHM in the context of service life integrity management.” In Proc., 12th Int. Conf. on Applications of Statistics and Probability in Civil Engineering. Vancouver, BC, Canada: Univ. of British Columbia.
Quirk, L., J. Matos, J. Murphy, and V. Pakrashi. 2018. “Visual inspection and bridge management.” Struct. Infrastruct. Eng. 14 (3): 320–332. https://doi.org/10.1080/15732479.2017.1352000.
Raiffa, H., and R. Schlaifer. 1968. Applied statistical decision theory. Cambridge, MA: MIT Press.
Raupach, M. 2006. “Models for the propagation phase of reinforcement corrosion—An overview.” Mater. Corros. 57 (8): 605–613. https://doi.org/10.1002/maco.200603991.
Saltelli, A., P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola. 2010. “Variance based sensitivity analysis of model output: Design and estimator for the total sensitivity index.” Comput. Phys. Commun. 181 (2): 259–270. https://doi.org/10.1016/j.cpc.2009.09.018.
Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. 2008. Global sensitivity analysis: The primer. Hoboken, NJ: Wiley.
Sánchez-Silva, M., D. Frangopol, J. Padgett, and M. Soliman. 2016. “Maintenance and operation of infrastructure systems: Review.” J. Struct. Eng. 142 (9): F4016004. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001543.
Simola, K., and U. Pulkkinen. 1998. “Models for non-destructive inspection data.” Reliab. Eng. Syst. Saf. 60 (1): 1–12. https://doi.org/10.1016/S0951-8320(97)00087-2.
Sobol, I. 2001. “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.” Math. Comput. Simul 55 (1–3): 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6.
Stewart, M., and A. Al-Harthy. 2008. “Pitting corrosion and structural reliability of corroding RC structures: Experimental data and probabilistic analysis.” Reliab. Eng. Syst. Saf. 93 (3): 373–382. https://doi.org/10.1016/j.ress.2006.12.013.
Stewart, M., and D. Rosowsky. 1998. “Time-dependent reliability of deteriorating reinforced concrete bridge decks.” Struct. Saf. 20 (1): 91–109. https://doi.org/10.1016/S0167-4730(97)00021-0.
Straub, D. 2004. “Generic approaches to risk based inspection planning for steel structures.” Ph.D. thesis, Institute of Structural Engineering, ETH Zurich.
Straub, D. 2014. “Value of information analysis with structural reliability methods.” Struct. Saf. 49 (Jul): 75–85. https://doi.org/10.1016/j.strusafe.2013.08.006.
Sudret, B. 2008. “Global sensitivity analysis using polynomial chaos expansions.” Reliab. Eng. Syst. Saf. 93 (7): 964–979. https://doi.org/10.1016/j.ress.2007.04.002.
Thomas, E. 1994. “A primer on multivariate calibration.” Anal. Chem. 66 (15): 795–804. https://doi.org/10.1021/ac00087a722.
Thöns, S. 2018. “On the value of monitoring information for the structural integrity and risk management.” Comput.-Aided Civ. Infrastruct. Eng. 33 (1): 79–94. https://doi.org/10.1111/mice.12332.
Thöns, S., et al. 2017. “Progress of the COST action TU1402 on the quantification of the value of structural health monitoring.” In Vol. 1 of Proc., 11th Int. Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, 1314–1323. Lancaster, PA: DEStech Publications.
Thöns, S., M. Döhler, and L. Long. 2018. “On damage detection system information for structural systems.” Struct. Eng. Int. 28 (3): 255–268. https://doi.org/10.1080/10168664.2018.1459222.
Thöns, S., and M. Faber. 2013. “Assessing the value of structural health monitoring.” In Proc., 11th Int. Conf. on Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures, 2543–2550. London: CRC Press.
Thöns, S., R. Schneider, and M. Faber. 2015. “Quantification of the value of structural health monitoring information for fatigue deteriorating structural systems.” In Proc., 12th Int. Conf. on Applications of Statistics and Probability in Civil Engineering. Vancouver, BC, Canada: Univ. of British Columbia.
Zheng, R., and B. Ellingwood. 1998. “Role of non-destructive evaluation in time-dependent reliability analysis.” Struct. Saf. 20 (4): 325–339. https://doi.org/10.1016/S0167-4730(98)00021-6.
Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 4 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jul 26, 2019
Accepted: May 26, 2020
Published online: Jul 23, 2020
Published in print: Dec 1, 2020
Discussion open until: Dec 23, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.