Seismic Drift Demand Estimation for Steel Moment Frame Buildings: From Mechanics-Based to Data-Driven Models
Publication: Journal of Structural Engineering
Volume 147, Issue 6
Abstract
A spectrum of simplified methods for estimating building seismic drift demands is conceptualized. On one extreme are mechanics-based approaches that are derived solely from fundamental engineering principles. On the other end are purely data-driven models that are developed using parametric data sets generated from nonlinear response history analyses. Between these two extremes, there are models that combine elements of basic engineering principles and statistical learning (hybrid models). First, the benefits and drawbacks of four existing simplified seismic response estimation methodologies that fall within this spectrum of approaches are critically examined. Subsequently, a generalized framework for developing and validating hybrid and/or purely data-driven seismic demand estimation models is proposed. Using this framework, two new machine learning–based models are developed and rigorously evaluated. Finally, a comparative assessment of the existing and newly developed models is conducted while focusing on their predictive performance and the level of effort needed to implement them.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies (Guan et al. 2020a, b).
Acknowledgments
This research is supported by National Science Foundation Award No. 1554714. The authors would also like to acknowledge valuable discussions with Dr. Han Sun on this research.
References
AISC (American Institute of Steel Construction). 2016a. Prequalified connections for special and intermediate steel moment frames for seismic applications. ANSI/AISC 358. Chicago: AISC.
AISC (American Institute of Steel Construction). 2016b. Seismic provisions for structural steel buildings. ANSI/AISC 341. Chicago: AISC.
AISC (American Institute of Steel Construction). 2016c. Specification for structural steel buildings. ANSI/AISC 360. Chicago: AISC.
ASCE. 2016. Minimum design loads and associated criteria for buildings and other structures. ASCE 7. Reston, VA: ASCE.
Bennett, J. O., W. L. Briggs, and A. Badalamenti. 2008. Using and understanding mathematics: A quantitative reasoning approach. New York: Pearson Addison Wesley Reading.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Chen, T., and C. Guestrin. 2016. “Xgboost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 785–794. New York: Association for Computing Machinery.
Chopra, A. K. 1995. Dynamics of structures theory and applications to earthquake engineering. Harlow, UK: Pearson.
Cook, D., K. Wade, C. Haselton, J. Baker, and D. J. DeBock. 2018. “A structural response prediction engine to support advanced seismic risk assessment.” In Proc., 11th National Conf. Earthquake Engineering, Oakland, CA: Earthquake Engineering Research Institute.
DeBock, D. J., and A. B. Liel. 2015. “A comparative evaluation of probabilistic regional seismic loss assessment methods using scenario case studies.” J. Earthquake Eng. 19 (6): 905–937. https://doi.org/10.1080/13632469.2015.1015754.
Deierlein, G. 2004. “Overview of a framework methodology for earthquake performance assessment.” In Proc., Performance-Based Seismic Design: Concepts and Implementation, 15–26. Berkeley, CA: Pacific Earthquake Engineering Research Center.
Efron, B., and R. J. Tibshirani. 1994. An introduction to the bootstrap. London: CRC Press.
FEMA. 2003. Multi-hazard loss estimation methodology earthquake model: Technical manual. Washington, DC: National Institute of Standards and Technology.
FEMA. 2012. Seismic performance assessment of buildings. Redwood City, CA: Applied Technology Council.
FEMA 2018. Guidelines for performance-based seismic design of buildings. Redwood City, CA: Applied Technology Council.
Guan, X., H. Burton, and M. Shokrabadi. 2020a. “A database of seismic designs, nonlinear models, and seismic responses for steel moment resisting frame buildings.” DesignSafe-CI. https://doi.org/10.17603/ds2-8yc7-1285.
Guan, X., H. Burton, and M. Shokrabadi. 2020b. “A database of seismic designs, nonlinear models, and seismic responses for steel moment resisting frame buildings.” Earthquake Spectra. https://doi.org/10.1177/8755293020971209.
Gupta, A., and H. Krawinkler. 2000. “Estimation of seismic drift demands for frame structures.” Earthquake Eng. Struct. Dyn. 29 (9): 1287–1305. https://doi.org/10.1002/1096-9845(200009)29:9%3C1287::AID-EQE971%3E3.0.CO;2-B.
Ho, T. K. 1995. “Random decision forests.” In Proc., 3rd Int. Conf. Document Analysis and Recognition, 278–282. New York: IEEE.
Huang, Y.-N., and A. Whittaker. 2012. Simplified analysis procedures for next-generation performance-based seismic design. Redwood City, CA: Applied Technology Council.
ICC (International Code Council). 2018. International building code. Country Club Hills, IL: ICC.
Lin, Y.-Y., and E. Miranda. 2009. “Estimation of maximum roof displacement demands in regular multistory buildings.” J. Eng. Mech. 136 (1): 1–11. https://doi.org/10.1061/(ASCE)0733-9399(2010)136:1(1).
McCulloch, W. S., and W. Pitts. 1943. “A logical calculus of the ideas immanent in nervous activity.” Bull. Math. Biophys. 5 (4): 115–133. https://doi.org/10.1007/BF02478259.
Miranda, E. 1999. “Approximate seismic lateral deformation demands in multistory buildings.” J. Struct. Eng. 125 (4): 417–425. https://doi.org/10.1061/(ASCE)0733-9445(1999)125:4(417).
Miranda, E., and C. J. Reyes. 2002. “Approximate lateral drift demands in multistory buildings with nonuniform stiffness.” J. Struct. Eng. 128 (7): 840–849. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:7(840).
Morfidis, K., and K. Kostinakis. 2017. “Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks.” Adv. Eng. Software 106 (Apr): 1–16. https://doi.org/10.1016/j.advengsoft.2017.01.001.
Nadaraya, E. A. 1964. “On estimating regression.” Theory Probab. Appl. 9 (1): 141–142. https://doi.org/10.1137/1109020.
Pagani, M., et al. 2014. “OpenQuake engine: An open hazard (and risk) software for the global earthquake model.” Seismol. Res. Lett. 85 (3): 692–702. https://doi.org/10.1785/0220130087.
Sun, H., H. Burton, Y. Zhang, and J. Wallace. 2018. “Interbuilding interpolation of peak seismic response using spatially correlated demand parameters.” Earthquake Eng. Struct. Dyn. 47 (5): 1148–1168. https://doi.org/10.1002/eqe.3010.
Yan, X., and X. Su. 2009. Linear regression analysis: Theory and computing. Singapore: World Scientific.
Zhang, R., Z. Chen, S. Chen, J. Zheng, O. Büyüköztürk, and H. Sun. 2019. “Deep long short-term memory networks for nonlinear structural seismic response prediction.” Comput. Struct. 220 (Aug): 55–68. https://doi.org/10.1016/j.compstruc.2019.05.006.
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Published In
Journal of Structural Engineering
Volume 147 • Issue 6 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 12, 2020
Accepted: Jan 11, 2021
Published online: Mar 23, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 23, 2021
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