Machine Learning-Based Seismic Damage Assessment of Residential Buildings Considering Multiple Earthquake and Structure Uncertainties
Abstract
Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP seismic classifier and fragility curves perform equally well when predicting minor damage. However, the MLP classifier is more accurate than the fragility curves in prediction of moderate and severe damage. Compared with the existing fragility curves with earthquake intensity measures as inputs, machine-learning-based seismic classifiers can incorporate multiple parameters of earthquakes and structures as input features, thus providing a promising tool for accurate seismic damage assessment in a portfolio scale. Once trained, the MLP classifier can predict damage classes of the 6,113 structures within 0.07 s on a general-purpose computer.
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Data Availability Statement
All raw data and materials supporting the conclusions of this article may be made available upon request from the corresponding author.
Acknowledgments
Financial support to complete this study was provided in part by the US Department of Transportation, Office of Assistant Secretary for Research and Technology under the auspices of Mid-America Transportation Center at the University of Nebraska, Lincoln (Grant No. 00059709). The authors would like to thank Dr. Chiun-lin Wu for sharing his synthesized ground motions for cities near the NMSZ.
References
Atkinson, G. M., and I. A. Beresnev. 2002. “Ground motions at Memphis and St. Louis from M 7.5-8.0 earthquakes in the New Madrid Seismic Zone.” Bull. Seismol. Soc. Am. 92 (3): 1015–1024. https://doi.org/10.1785/0120010203.
Attary, N., J. W. van de Lindt, H. Mahmoud, S. Smith, C. M. Navarro, Y. W. Kim, and J. S. Lee. 2018. “Hindcasting community-level building damage for the 2011 Joplin EF5 tornado.” Nat. Hazards 93 (3): 1295–1316. https://doi.org/10.1007/s11069-018-3353-5.
Baker, J. W., and C. A. Cornell. 2005. “A vector-valued ground motion intensity measure consisting of spectral acceleration and epsilon.” Earthquake Eng. Struct. Dyn. 34 (10): 1193–1217. https://doi.org/10.1002/eqe.474.
Camelo, V. S. 2003. Dynamic characteristics of woodframe buildings. Pasadena, CA: California Institute of Technology.
Caruana, R., and A. Niculescu-Mizil. 2006. “An empirical comparison of supervised learning algorithms.” In Proc., 23rd Int. Conf. on Machine Learning, 161–168. New York: Association for Computer Machinery.
Cerda, P., G. Varoquaux, and B. Kégl. 2018. “Similarity encoding for learning with dirty categorical variables.” Mach. Learn. 107 (8): 1477–1494. https://doi.org/10.1007/s10994-018-5724-2.
Chase, R. E., A. B. Liel, N. Luco, and B. W. Baird. 2019. “Seismic loss and damage in light-frame wood buildings from sequences of induced earthquakes.” Earthquake Eng. Struct. Dyn. 48 (12): 1365–1383. https://doi.org/10.1002/eqe.3189.
Chen, G., N. Anderson, R. Luna, R. Stephenson, M. El-Engebawy, P. Silva, and R. Zoughi. 2005. Earthquake hazards assessment and mitigation: A pilot study in the new Madrid seismic zone. McLean, VA: Federal Highway Administration.
Chen, G., Y. Zeng, D. J. Hoffman, M. A. El-Engebawy, J. D. Rogers, and R. B. Herrmann. 2004. “Sensitivity study on near-fault rock motions within the New Madrid Seismic Zone using a composite source model.” In Proc., 11th Int. Conf. on Soil Dynamics & Earthquake Engineering and the 3rd Int. Conf. on Earthquake Geotechnical Engineering. Rolla, MO: Missouri Univ. of Science and Technology.
Chen, S., C. Fan, and J. Pan. 2010. “Experimental study on full-scale light frame wood house under lateral load.” J. Struct. Eng. 136 (7): 805–812. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000178.
de Lautour, O. R., and P. Omenzetter. 2009. “Prediction of seismic-induced structural damage using artificial neural networks.” Eng. Struct. 31 (2): 600–606. https://doi.org/10.1016/j.engstruct.2008.11.010.
Demuth, H., and M. Beale. 2002Neural network toolbox—For use with MATLAB. Natick, MA: MathWorks.
Du, A., and J. E. Padgett. 2020. “Investigation of multivariate seismic surrogate demand modeling for multi-response structural systems.” Eng. Struct. 207 (Mar): 110210. https://doi.org/10.1016/j.engstruct.2020.110210.
Du, A., J. E. Padgett, and A. Shafieezadeh. 2020. “Influence of intensity measure selection on simulation-based regional seismic risk assessment.” Earthquake Spectra 36 (2): 647–672. https://doi.org/10.1177/8755293019891717.
Ellingwood, B. R., D. V. Rosowsky, Y. Li, and J. H. Kim. 2004. “Fragility assessment of light-frame wood construction subjected to wind and earthquake hazards.” J. Struct. Eng. 130 (12): 1921–1930. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:12(1921).
Ellingwood, B. R., D. V. Rosowsky, and W. Pang. 2008. “Performance of light-frame wood residential construction subjected to earthquakes in regions of moderate seismicity.” J. Struct. Eng. 134 (8): 1353–1363. https://doi.org/10.1061/(ASCE)0733-9445(2008)134:8(1353).
Fan, J., S. Upadhye, and A. Worster. 2006. “Understanding receiver operating characteristic (ROC) curves.” Can. J. Emergency Med. 8 (1): 19–20. https://doi.org/10.1017/S1481803500013336.
FEMA. 1997. NEHRP guidelines for the seismic rehabilitation of buildings. FEMA-273. Washington, DC: FEMA.
Filiatrault, A., I. P. Christovasilis, A. Wanitkorkul, and J. W. van de Lindt. 2010. “Experimental seismic response of a full-scale light-frame wood building.” J. Struct. Eng. 136 (3): 246–254. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000112.
Gardoni, P., J. Van De Lindt, B. Ellingwood, T. Mcallister, J. S. Lee, H. Cutler, W. Peacock, and D. Cox. 2018. “The interdependent networked community resilience modeling environment (IN-CORE).” In Proc., 16th European Conf. on Earthquake Engineering, 18–21. Gaithersburg, MD: NIST.
Hafeez, G., G. Doudak, and G. McClure. 2018. “Establishing the fundamental period of light-frame wood buildings on the basis of ambient vibration tests.” Can. J. Civ. Eng. 45 (9): 752–765. https://doi.org/10.1139/cjce-2017-0348.
Haselton, C. B., C. A. Goulet, J. Mitrani-Reiser, J. L. Beck, G. G. Deierlein, K. A. Porter, J. P. Stewart, and E. Taciroglu. 2008. An assessment to benchmark the seismic performance of a code-conforming reinforced concrete moment-frame building. Berkeley, CA: Pacific Earthquake Engineering Research Center.
Jeong, S.-H., and A. S. Elnashai. 2007. “Probabilistic fragility analysis parameterized by fundamental response quantities.” Eng. Struct. 29 (6): 1238–1251. https://doi.org/10.1016/j.engstruct.2006.06.026.
Jiang, L., J. Zhong, and W. Yuan. 2020. “The pulse effect on the isolation device optimization of simply supported bridges in near-fault regions.” Structures 27 (Oct): 853–867. https://doi.org/10.1016/j.istruc.2020.06.034.
Karimzadeh, S., A. Askan, M. A. Erberik, and A. Yakut. 2018. “Seismic damage assessment based on regional synthetic ground motion dataset: A case study for Erzincan, Turkey.” Nat. Hazards 92 (3): 1371–1397. https://doi.org/10.1007/s11069-018-3255-6.
Kasal, B., M. S. Collins, P. Paevere, and G. C. Foliente. 2004. “Design models of light frame wood buildings under lateral loads.” J. Struct. Eng. 130 (8): 1263–1271. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:8(1263).
Kim, J. H., S. M. Kent, and D. V. Rosowsky. 2006. “The effect of biological deterioration on the seismic performance of woodframe shearwalls.” Comput.-Aided Civ. Infrastruct. Eng. 21 (3): 205–215. https://doi.org/10.1111/j.1467-8667.2006.00428.x.
Kircher, C. A., R. K. Reitherman, R. V. Whitman, and C. Arnold. 1997. “Estimation of earthquake losses to buildings.” Earthquake Spectra 13 (4): 703–720. https://doi.org/10.1193/1.1585976.
Kostinakis, K., and K. Morfidis. 2020. “Application of artificial neural networks for the assessment of the seismic damage of buildings with irregular infills’ distribution.” In Seismic behaviour and design of irregular and complex civil structures III, 291–306. Cham, Switzerland: Springer.
Lagaros, N. D., and M. Fragiadakis. 2007. “Fragility assessment of steel frames using neural networks.” Earthquake Spectra 23 (4): 735–752. https://doi.org/10.1193/1.2798241.
Lu, X., Y. Xu, Y. Tian, B. Cetiner, and E. Taciroglu. 2021. “A deep learning approach to rapid regional post-event seismic damage assessment using time-frequency distributions of ground motions.” Earthquake Eng. Struct. Dyn. 50 (6): 1612–1627. https://doi.org/10.1002/eqe.3415.
Lucksiri, K., T. H. Miller, R. Gupta, S. Pei, and J. W. van de Lindt. 2012a. “Effect of plan configuration on seismic performance of single-story wood-frame dwellings.” Nat. Hazard. Rev. 13 (1): 24–33. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000061.
Lucksiri, K., T. H. Miller, R. Gupta, S. Pei, and J. W. Van De Lindt. 2012b. “A procedure for rapid visual screening for seismic safety of wood-frame dwellings with plan irregularity.” Eng. Struct. 36 (Mar): 351–359. https://doi.org/10.1016/j.engstruct.2011.12.023.
Luco, N., and P. Bazzurro. 2007. “Does amplitude scaling of ground motion records result in biased nonlinear structural drift responses?” Earthquake Eng. Struct. Dyn. 36 (13): 1813–1835. https://doi.org/10.1002/eqe.695.
Luco, N., and C. A. Cornell. 2007. “Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions.” Earthquake Spectra 23 (2): 357–392. https://doi.org/10.1193/1.2723158.
Mangalathu, S., G. Heo, and J. S. Jeon. 2018. “Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes.” Eng. Struct. 162 (May): 166–176. https://doi.org/10.1016/j.engstruct.2018.01.053.
Memari, M., N. Attary, H. Masoomi, H. Mahmoud, J. W. van de Lindt, S. F. Pilkington, and M. R. Ameri. 2018. “Minimal building fragility portfolio for damage assessment of communities subjected to tornadoes.” J. Struct. Eng. 144 (7): 04018072. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002047.
Morfidis, K., and K. Kostinakis. 2018. “Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks.” Eng. Struct. 165 (Jun): 120–141. https://doi.org/10.1016/j.engstruct.2018.03.028.
Morfidis, K., and K. Kostinakis. 2019. “Comparative evaluation of MFP and RBF neural networks’ ability for instant estimation of r/c buildings’ seismic damage level.” Eng. Struct. 197 (Oct): 109436. https://doi.org/10.1016/j.engstruct.2019.109436.
Pang, W., D. V. Rosowsky, B. R. Ellingwood, and Y. Wang. 2009. “Seismic fragility analysis and retrofit of conventional residential wood-frame structures in the Central United States.” J. Struct. Eng. 135 (3): 262–271. https://doi.org/10.1061/(ASCE)0733-9445(2009)135:3(262).
Pei, S., J. W. van de Lindt, N. Wehbe, and H. Liu. 2013. “Experimental study of collapse limits for wood frame shear walls.” J. Struct. Eng. 139 (9): 1489–1497. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000730.
Ren, Y., P. Zhao, Y. Sheng, D. Yao, and Z. Xu. 2017. “Robust softmax regression for multi-class classification with self-paced learning.” In Proc., IJCAI Int. Joint Conf. on Artificial Intelligence, 2641–2647. Washington, DC: AAAI Press.
Scotta, R., D. Trutalli, L. Marchi, and L. Pozza. 2018. “On the anchoring of timber walls to foundations: Available strategies to prevent wood deterioration and on-site installation problems.” Procedia Struct. Integrity 11 (Jan): 282–289. https://doi.org/10.1016/j.prostr.2018.11.037.
Singh, D., and B. Singh. 2020. “Investigating the impact of data normalization on classification performance.” Appl. Soft Comput. 97 (Dec): 105524. https://doi.org/10.1016/j.asoc.2019.105524.
Soltis, L. A., and R. H. Falk. 1992. “Seismic performance of low-rise wood buildings.” Shock Vibr. Dig. 24 (12): 3–6. https://doi.org/10.1177/058310249202401202.
van de Lindt, J. W. 2005. “Damage-based seismic reliability concept for woodframe structures.” J. Struct. Eng. 131 (4): 668–675. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:4(668).
van de Lindt, J. W., S. Pei, S. E. Pryor, H. Shimizu, and H. Isoda. 2010. “Experimental seismic response of a full-scale six-story light-frame wood building.” J. Struct. Eng. 136 (10): 1262–1272. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000222.
Vaseghiamiri, S., M. Mahsuli, M. A. Ghannad, and F. Zareian. 2020. “Surrogate SDOF models for probabilistic performance assessment of multistory buildings: Methodology and application for steel special moment frames.” Eng. Struct. 212 (Jun): 110276. https://doi.org/10.1016/j.engstruct.2020.110276.
Wald, D. J., B. C. Worden, V. Quitoriano, and K. L. Pankow. 2006. ShakeMap manual: Technical manual, user’s guide, and software guide. Reston, VA: USGS.
Wang, Z., N. Pedroni, I. Zentner, and E. Zio. 2018. “Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment.” Eng. Struct. 162 (May): 213–225. https://doi.org/10.1016/j.engstruct.2018.02.024.
Wen, Y. K., M. Eeri, C. L. Wu, and M. Eeri. 2001. “Uniform hazard ground motions for mid-America cities.” Earthquake Spectra 17 (2): 359–384. https://doi.org/10.1193/1.1586179.
Xu, Y., X. Lu, Y. Tian, and Y. Huang. 2020. “Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning.” J. Earthquake Eng. 26 (8): 4259–4279. https://doi.org/10.1080/13632469.2020.1826371.
Yang, T., X. Yuan, J. Zhong, and W. Yuan. 2023. “Near-fault pulse seismic ductility spectra for bridge columns based on machine learning.” Soil Dyn. Earthquake Eng. 164 (Jan): 107582. https://doi.org/10.1016/j.soildyn.2022.107582.
Yi, Z. 2020. Performance-based analytics-driven seismic retrofit of woodframe buildings. Los Angeles: Univ. of California.
Yi, Z., and H. Burton. 2020. “Machine learning based regional seismic retrofit design optimization of soft-story woodframe buildings.” In Proc., 17th World Conf. on Earthquake Engineering. Kanpur, India: National Information Centre for Earthquake Engineering.
Yuan, X., G. Chen, P. Jiao, L. Li, J. Han, and H. Zhang. 2022. “A neural network-based multivariate seismic classifier for simultaneous post-earthquake fragility estimation and damage classification.” Eng. Struct. 255 (Mar): 113918. https://doi.org/10.1016/j.engstruct.2022.113918.
Yuan, X., D. Tanksley, P. Jiao, L. Li, G. Chen, and D. Wunsch. 2021. “Encoding rime-series ground motions as images for convolutional neural networks-based seismic damage evaluation.” Front. Built Environ. 7 (Apr): 660103. https://doi.org/10.3389/fbuil.2021.660103.
Zhao, Y., H. Hu, L. Bai, M. Tang, H. Chen, and D. Su. 2021. “Fragility analyses of bridge structures using the logarithmic piecewise function-based probabilistic seismic demand model.” Sustainability 13 (14): 7814. https://doi.org/10.3390/su13147814.
Zhong, J., L. Jiang, Y. Pang, and W. Yuan. 2020. “Near-fault seismic risk assessment of simply supported bridges.” Earthquake Spectra 36 (4): 1645–1669. https://doi.org/10.1177/8755293020935145.
Information & Authors
Information
Published In
Natural Hazards Review
Volume 24 • Issue 3 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 12, 2022
Accepted: Feb 27, 2023
Published online: May 5, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 5, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Buildings
- Computer programming
- Computing in civil engineering
- Curvature
- Earthquake engineering
- Earthquakes
- Engineering fundamentals
- Frames
- Geohazards
- Geometry
- Geotechnical engineering
- Mathematics
- Residential buildings
- Seismic effects
- Seismic tests
- Structural engineering
- Structural members
- Structural systems
- Structures (by type)
- Tests (by type)
- Wood frames
- Wood structures
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