Abstract
Assessment of risk under natural hazards is associated with a significant computational burden when comprehensive numerical models and simulation-based methodologies are involved. Despite recent advances in computer and computational science that have contributed in reducing this burden and have undoubtedly increased the popularity of simulation-based frameworks for quantifying/estimating risk in such settings, in many instances, such as for real-time risk estimation, this burden is still considered as prohibitive. This chapter discusses the use of kriging surrogate modeling for addressing this challenge. Kriging establishes a computationally inexpensive input/output relationship based on a database of observations obtained through the initial (expensive) simulation model. The up-front cost for obtaining this database is of course high, but once the surrogate model is established, all future evaluations require small computational effort. For illustration, two different applications are considered, involving two different hazards: seismic risk assessment utilizing stochastic ground motion modeling and real-time hurricane risk estimation. Various implementation issues are discussed, such as (a) advantages of kriging over other surrogate models, (b) approaches for obtaining high efficiency when the output under consideration is high dimensional through integration of principal component analysis, and (c) the incorporation of the prediction error associated with the metamodel into the risk assessment.
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References
Abrams, D. P., Elnashai, A. S., & Beavers, J. E. (2002). A new engineering paradigm: Consequence-based engineering. Linbeck Lecture Series in Earthquake Engineering: Challenges of the New Millennium, University of Notre Dame, Linbeck Distinguished Lecture Series, Notre Dame, IN.
Aslani, H., & Miranda, E. (2005). Probability-based seismic response analysis. Engineering Structures, 27(8), 1151–1163.
Au, S. K., & Beck, J. L. (2003). Subset simulation and its applications to seismic risk based on dynamic analysis. Journal of Engineering Mechanics, ASCE, 129(8), 901–917.
Boore, D. M. (2003). Simulation of ground motion using the stochastic method. Pure and Applied Geophysics, 160, 635–676.
Bozorgnia, Y., & Bertero, V. (2004). Earthquake engineering: From engineering seismology to performance-based engineering. Boca Raton, FL: CRC Press.
Breitkopf, P., Naceur, H., Rassineux, A., & Villon, P. (2005). Moving least squares response surface approximation: Formulation and metal forming applications. Computers & Structures, 83(17–18), 1411–1428.
Bunya, S., Dietrich, J. C., Westerink, J. J., Ebersole, B. A., Smith, J. M., Atkinson, J. H., et al. (2010). A high resolution coupled riverine flow, tide, wind, wind wave and storm surge model for Southern Louisiana and Mississippi. Part I: Model development and validation. Monthly Weather Review, 138(2), 345–377.
Buratti, N., Ferracuti, B., & Savoia, M. (2010). Response surface with random factors for seismic fragility of reinforced concrete frames. Structural Safety, 32(1), 42–51.
Christopoulos, C., & Filiatrault, A. (2006). Principles of passive supplemental damping and seismic isolation. Pavia, Italy: IUSS Press.
Das, H. S., Jung, H., Ebersole, B., Wamsley, T., & Whalin, R. W. (2010). An efficient storm surge forecasting tool for coastal Mississippi. Paper presented at the 32nd International Coastal Engineering Conference, Shanghai, China.
Der Kiureghian, A. (1996). Structural reliability methods for seismic safety assessment: A review. Engineering Structures, 18(6), 412–424.
Dietrich, J. C., Zijlema, M., Westerink, J. J., Holthuijsen, L. H., Dawson, C., Luettich, R. A., et al. (2011). Modeling hurricane waves and storm surge using integrally-coupled, scalable computations. Coastal Engineering, 58, 45–65.
Dubourg, V., Sudret, B., & Bourinet, J.-M. (2011). Reliability-based design optimization using kriging surrogates and subset simulation. Structural Multidisciplinary Optimization, 44(5), 673–690.
Ellingwood, B. R. (2001). Earthquake risk assessment of building structures. Reliability Engineering & System Safety, 74(3), 251–262.
FEMA-P-58. (2012). Seismic performance assessment of buildings. Redwood City, CA: American Technology Council.
Fujimoto, R. M. (2001). Parallel simulation: Parallel and distributed simulation systems. In: Proceedings of the 33rd Winter Simulation Conference (pp. 147–157). Arlington, Virginia.
Gardoni, P., Der Kiureghian, A., & Mosalam, K. H. (2002). Probabilistic capacity models and fragility estimates for reinforced concrete columns based on experimental observations. Journal of Engineering Mechanics, 128(10), 1024–1038.
Gardoni, P., Mosalam, K. M., & der Kiureghian, A. (2003). Probabilistic seismic demand models and fragility estimates for RC bridges. Journal of Earthquake Engineering, 7, 79–106.
Gavin, H. P., & Yau, S. C. (2007). High-order limit state functions in the response surface method for structural reliability analysis. Structural Safety, 30(2), 162–179.
Gidaris, I., & Taflanidis, A. A. (2015). Performance assessment and optimization of fluid viscous dampers through life-cycle cost criteria and comparison to alternative design approaches. Bulletin of Earthquake Engineering, 13(4), 1003–1028.
Gidaris, I., Taflanidis, A. A., & Mavroeidis, G. M. (2014). Multiobjective formulation for the life-cycle cost based design of fluid viscous dampers. Paper presented at the IX International Conference on Structural Dynamics (EURODYN 2014), Porto, Portugal, June 30–July 2.
Gidaris, I., Taflanidis, A. A., & Mavroeidis, G. P. (2015). Kriging metamodeling in seismic risk assessment based on stochastic ground motion models. Earthquake Engineering and Structural Dynamics. 44(14), 2377–2399.
Goulet, C. A., Haselton, C. B., Mitrani-Reiser, J., Beck, J. L., Deierlein, G., Porter, K. A., et al. (2007). Evaluation of the seismic performance of code-conforming reinforced-concrete frame building-From seismic hazard to collapse safety and economic losses. Earthquake Engineering and Structural Dynamics, 36(13), 1973–1997.
Hajela, P., & Berke, L. (1992). Neural networks in engineering analysis and design: An overview. Computing Systems in Engineering, 31(1–4), 525–538.
Hardyniec, A., & Charney, F. (2015). A new efficient method for determining the collapse margin ratio using parallel computing. Computers & Structures, 148, 14–25.
Holland, G. J. (1980). An analytic model of the wind and pressure profiles in hurricanes. Monthly Weather Review, 108(8), 1212–1218.
Ibarra, L. F., Medina, R. A., & Krawinkler, H. (2005). Hysteretic models that incorporate strength and stiffness deterioration. Earthquake Engineering and Structural Dynamics, 34(12), 1489–1511.
Irish, J., Resio, D., & Cialone, M. (2009). A surge response function approach to coastal hazard assessment. Part 2: Quantification of spatial attributes of response functions. Natural Hazards, 51(1), 183–205.
Jalayer, F., & Cornell, C. (2009). Alternative non-linear demand estimation methods for probability-based seismic assessments. Earthquake Engineering and Structural Dynamics, 38(8), 951–972.
Jaynes, E. T. (2003). Probability theory: The logic of science. Cambridge, UK: Cambridge University Press.
Jensen, H. A., & Kusanovic, D. S. (2014). On the effect of near-field excitations on the reliability-based performance and design of base-isolated structures. Probabilistic Engineering Mechanics, 36, 28–44.
Jia, G., Gidaris, I., Taflanidis, A. A., & Mavroeidis, G. P. (2014). Reliability-based assessment/design of floor isolation systems. Engineering Structures, 78, 41–56.
Jia, G., & Taflanidis, A. A. (2013). Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment. Computer Methods in Applied Mechanics and Engineering, 261–262, 24–38.
Jin, R., Chen, W., & Simpson, T. W. (2001). Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, 23(1), 1–13.
Kennedy, A. B., Westerink, J. J., Smith, J., Taflanidis, A. A., Hope, M., Hartman, M., et al. (2012). Tropical cyclone inundation potential on the Hawaiian islands of Oahu and Kauai. Ocean Modelling, 52–53, 54–68.
Kijewski-Correa, T., Smith, N., Taflanidis, A. A., Kennedy, A., Liu, C., Krusche, M., et al. (2014). CyberEye: Development of integrated cyber-infrastructure to support rapid hurricane risk assessment. Journal of Wind Engineering and Industrial Aerodynamics, 133(211–224).
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1137–1145). Montreal, Canada.
Kramer, S. L. (1996). Geotechnical earthquake engineering. Upper Saddle River, NJ: Prentice Hall.
Kumar, R., Cline, D. B. H., & Gardoni, P. (2015). A stochastic framework to model deterioration in engineering systems. Structural Safety, 53, 36–43.
Liel, A. B., Haselton, C. B., Deierlein, G. G., & Baker, J. W. (2009). Incorporating modeling uncertainties in the assessment of seismic collapse risk of buildings. Structural Safety, 31(2), 197–211.
Lophaven, S. N., Nielsen, H. B., & Sondergaard, J. (2002). DACE-A MATLAB kriging toolbox. Technical University of Denmark.
Loweth, E. L., De Boer, G. N., & Toropov, V. V. (2010). Practical recommendations on the use of moving least squares metamodel building. Paper presented at the Thirteenth International Conference on Civil, Structural and Environmental Engineering Computing, Crete, Greece.
Mavroeidis, G. P., & Papageorgiou, A. S. (2003). A mathematical representation of near-fault ground motions. Bulletin of the Seismological Society of America, 93(3), 1099–1131.
McKenna, F. (2011). OpenSees: A framework for earthquake engineering simulation. Computing in Science & Engineering, 13(4), 58–66.
Moehle, J., & Deierlein, G. (2004). A framework methodology for performance-based earthquake engineering. In: Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, Canada, August 1–6, 2004.
Möller, O., Foschi, R. O., Quiroz, L. M., & Rubinstein, M. (2009). Structural optimization for performance-based design in earthquake engineering: Applications of neural networks. Structural Safety, 31(6), 490–499.
Pellissetti, M. (2008). Parallel processing in structural reliability. In: Proceedings of the 4th International Conference on Advances in Structural Engineering and Mechanics (ASEM).
Picheny, V., Ginsbourger, D., Roustant, O., Haftka, R. T., & Kim, N. H. (2010). Adaptive designs of experiments for accurate approximation of a target region. Journal of Mechanical Design, 132(7), 071008.
Porter, K. A., Kennedy, R. P., & Bachman, R. E. (2006). Developing fragility functions for building components (Report to ATC-58). Applied Technology Council, Redwood City, CA.
Porter, K. A., Kiremidjian, A. S., & LeGrue, J. S. (2001). Assembly-based vulnerability of buildings and its use in performance evaluation. Earthquake Spectra, 18(2), 291–312.
Rackwitz, R. (2001). Reliability analysis—A review and some perspectives. Structural Safety, 23, 365–395.
Resio, D. T., Boc, S. J., Borgman, L., Cardone, V., Cox, A., Dally, W. R., et al. (2007). White paper on estimating hurricane inundation probabilities. Consulting Report prepared by USACE for FEMA.
Resio, D. T., Irish, J. L., Westering, J. J., & Powell, N. J. (2012). The effect of uncertainty on estimates of hurricane surge hazards. Natural Hazards, 66(3), 1443–1459.
Resio, D. T., & Westerink, J. J. (2008). Modeling of the physics of storm surges. Physics Today, 61(9), 33–38.
Rezaeian, S., & Der Kiureghian, A. (2010). Simulation of synthetic ground motions for specified earthquake and site characteristics. Earthquake Engineering and Structural Dynamics, 39(10), 1155–1180.
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, 4(4), 409–435.
Schotanus, M., Franchin, P., Lupoi, A., & Pinto, P. (2004). Seismic fragility analysis of 3D structures. Structural Safety, 26(4), 421–441.
Shahi, S. K., & Baker, J. W. (2011). An empirically calibrated framework for including the effects of near-fault directivity in probabilistic seismic hazard analysis. Bulletin of Seismological Society of America, 101(2), 742–755.
Shome, N. (1999). Probabilistic seismic demand analysis of nonlinear structures. Ph.D Thesis. Stanford University, Stanford, CA.
Simpson, T. W., Peplinski, J. D., Koch, P. N., & Allen, J. K. (2001). Metamodels for computer-based engineering design: Survey and recommendations. Engineering with Computers, 17, 129–150.
Smith, J. M., Westerink, J. J., Kennedy, A. B., Taflanidis, A. A., & Smith, T. D. (2011). SWIMS Hawaii hurricane wave, surge, and runup inundation fast forecasting tool. In: Proceedings of the 2011 Solutions to Coastal Disasters Conference, Anchorage, Alaska, June 26–29, 2011.
Taflanidis, A. A. (2010). Reliability-based optimal design of linear dynamical systems under stochastic stationary excitation and model uncertainty. Engineering Structures, 32(5), 1446–1458.
Taflanidis, A. A., & Beck, J. L. (2008). An efficient framework for optimal robust stochastic system design using stochastic simulation. Computer Methods in Applied Mechanics and Engineering, 198(1), 88–101.
Taflanidis, A. A., & Beck, J. L. (2009). Life-cycle cost optimal design of passive dissipative devices. Structural Safety, 31(6), 508–522.
Taflanidis, A. A., Jia, G., Kennedy, A. B., & Smith, J. (2012). Implementation/Optimization of moving least squares response surfaces for approximation of hurricane/storm surge and wave responses. Natural Hazards, 66(2), 955–983.
Taflanidis, A. A., Jia, G., Norberto, N.-C., Kennedy, A. B., Melby, J., & Smith, J. M. (2014). Development of real-time tools for hurricane risk assessment. Paper presented at the Second International Conference on Vulnerability and Risk Analysis and Management/Sixth International Symposium on Uncertainty Modeling and Analysis, Liverpool, England, July 13–16.
Taflanidis, A. A., Kennedy, A. B., Westerink, J. J., Smith, J., Cheung, K. F., Hope, M., et al. (2013a). Rapid assessment of wave and surge risk during landfalling hurricanes; probabilistic approach. Journal of Waterway, Port, Coastal, and Ocean Engineering, 139(3), 171–182.
Taflanidis, A. A., Loukogeorgaki, E., & Angelides, D. A. (2011). Risk assessment and sensitivity analysis for offshore wind turbines. Paper presented at the 21st International Offshore (Ocean) and Polar Engineering Conference, Maui, Hawaii, June 19–24.
Taflanidis, A. A., Vetter, C., & Loukogeorgaki, E. (2013b). Impact of modeling and excitation uncertainties on operational and structural reliability of Tension Leg Platforms. Applied Ocean Research, 43, 131–147.
Tipping, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society B, 61, 611–622.
Toro, G. R., Resio, D. T., Divoky, D., Niedoroda, A., & Reed, C. (2010). Efficient joint-probability methods for hurricane surge frequency analysis. Ocean Engineering, 37, 125–134.
Tsompanakis, Y., Lagaros, N. D., Psarropoulos, P. N., & Georgopoulos, E. C. (2009). Simulating the seismic response of embankments via artificial neural networks. Advances in Engineering Software, 40(8), 640–651.
Vetter, C. R., Taflanidis, A. A., & Mavroeidis, G. P. (2016). Tuning of stochastic ground motion models for compatibility with ground motion prediction equations. Earthquake Engineering and Structural Dynamics, 45(6), 893–912.
Vickery, P. J., Skerlj, P. F., Lin, J., & Twisdale, L. A. (2006). HAZUS-MH hurricane model methodology. II: Damage and loss estimation. Natural Hazards Review, 7(2), 94–103.
Vickery, P. J., Wadhera, D., Powell, M. D., & Chen, Y. (2009). A hurricane boundary layer and wind field model for use in engineering applications. Journal of Applied Meteorology and Climatology, 48(2), 381–405.
Wen, Y. K., & Kang, Y. J. (2001). Minimum building life-cycle cost design criteria. I: Methodology. Journal of Structural Engineering, 127(3), 330–337.
Zhang, J., & Foschi, R. O. (2004). Performance-based design and seismic reliability analysis using designed experiments and neural networks. Probabilistic Engineering Mechanics, 19(3), 259–267.
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Taflanidis, A.A., Jia, G., Gidaris, I. (2016). Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling. In: Gardoni, P., LaFave, J. (eds) Multi-hazard Approaches to Civil Infrastructure Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-29713-2_4
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