Machine Learning-Based Classification for Rapid Seismic Damage Assessment of Buildings at a Regional Scale

References

• Adhikari, B., S. R. Mishra, and S. Raut. 2016. Rebuilding earthquake struck Nepal through community engagement. Frontiers in Public Health 4:121. doi:10.3389/fpubh.2016.00121.
   PubMed Web of Science ®Google Scholar
• Ahmed, B., S. Mangalathu, and J. S. Jeon. 2022. Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks. Journal of Building Engineering 46:103737. doi:10.1016/j.jobe.2021.103737.
   Web of Science ®Google Scholar
• Ahmed, A., and K. Shahzada. 2020. Structures, Vol. 27, 639–49. doi:10.1016/j.istruc.2020.06.007.
   Web of Science ®Google Scholar
• Applied Technology Council. 1985. Earthquake damage evaluation data for California (ATC-13). Redwood, CA: Applied Technology Council.
   Google Scholar
• Applied Technology Council. 1995. ATC-20 procedures for post-earthquake building safety evaluation procedures. Redwood, CA: Applied Technology Council.
   Google Scholar
• Arslan, M. H. 2010. An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks. Engineering Structures 32 (7):1888–98. doi:10.1016/j.engstruct.2010.03.010.
   Web of Science ®Google Scholar
• Arslan, M. H., M. Ceylan, and T. Koyuncu. 2015. Determining earthquake performances of existing reinforced concrete buildings by using ANN. International Journal of Civil and Environmental Engineering 9 (8):1097–101.
   Google Scholar
• Baker, J. W., and C. Allin Cornell. 2005. A vector‐valued ground motion intensity measure consisting of spectral acceleration and epsilon. Earthquake Engineering & Structural Dynamics 34 (10):1193–217. doi:10.1002/eqe.474.
   Web of Science ®Google Scholar
• Bazzurro, P., C. A. Cornell, C. Menun, and M. Motahari. 2004. Guidelines for seismic assessment of damaged buildings. Proceedings of the 13th world conference on earthquake engineering, Vancouver, Canada, August.
   Google Scholar
• Bennett, J. 2010. OpenStreetMap. 1st ed. Birmingham: Packt Publishing.
   Google Scholar
• Bhatta, S., and J. Dang. 2023. Seismic damage prediction of RC buildings using machine learning. Earthquake Engineering and Structural Dynamics 52:3504–3527. doi:10.1002/eqe.3907.
   Web of Science ®Google Scholar
• Boağzi i University. 2022. Regional earthquake-tsunami monitoring center (RETMC). Accessed April 4, 2022. http://www.koeri.boun.edu.tr/sismo/2/en/.
   Google Scholar
• Bommer, J. J., and H. Crowley. 2006. The influence of ground-motion variability in earthquake loss modelling. Bulletin of Earthquake Engineering 4 (3):231–48. doi:10.1007/s10518-006-9008-z.
   Web of Science ®Google Scholar
• Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40. doi:10.1007/BF00058655.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Burton, H. V., S. Sreekumar, M. Sharma, and H. Sun. 2017. Estimating aftershock collapse vulnerability using mainshock intensity, structural response and physical damage indicators. Structural Safety 68:85–96. doi:10.1016/j.strusafe.2017.05.009.
   Web of Science ®Google Scholar
• Calvi, G. M., R. Pinho, G. Magenes, J. J. Bommer, L. F. Restrepo-Vélez, and H. Crowley. 2006. Development of seismic vulnerability assessment methodologies over the past 30 years. ISET Journal of Earthquake Technology 43 (3):75–104.
   Google Scholar
• Castellazzi, G., C. Gentilini, and L. Nobile. 2013. Seismic vulnerability assessment of a historical church: Limit analysis and nonlinear finite element analysis. Advances in Civil Engineering 2013:1–12. doi:10.1155/2013/517454.
   Google Scholar
• Castori, G., A. Borri, A. De Maria, M. Corradi, and R. Sisti. 2017. Seismic vulnerability assessment of a monumental masonry building. Engineering Structures 136:454–65. doi:10.1016/j.engstruct.2017.01.035.
   Web of Science ®Google Scholar
• Ceroni, F., M. Pecce, S. Sica, and A. Garofano. 2012. Assessment of seismic vulnerability of a historical masonry building. Buildings 2 (3):332–58. doi:10.3390/buildings2030332.
   Google Scholar
• Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–57. doi:10.1613/jair.953.
   Web of Science ®Google Scholar
• Chen, Y., G. Kai, Z. Suling, and H. Zhibin. 2015. Status quo of China earthquake networks and analyses on its early warning capacity. Acta Seismologica Sinica 37 (3):508–15.
   Google Scholar
• Coskun, O., A. Aldemir, and M. Sahmaran. 2020. Rapid screening method for the determination of seismic vulnerability assessment of RC building stocks. Bulletin of Earthquake Engineering 18 (4):1401–16. doi:10.1007/s10518-019-00751-9.
   Web of Science ®Google Scholar
• D’Ayala, D. 2013. Assessing the seismic vulnerability of masonry buildings. In Handbook of seismic risk analysis and management of civil infrastructure systems, 334–65. Woodhead publishing. doi:10.1533/9780857098986.3.334.
   Google Scholar
• De Lautour, O. R., and P. Omenzetter. 2009. Prediction of seismic-induced structural damage using artificial neural networks. Engineering Structures 31 (2):600–06. doi:10.1016/j.engstruct.2008.11.010.
   Web of Science ®Google Scholar
• Del Gaudio, C., M. Di Ludovico, M. Polese, G. Manfredi, A. Prota, P. Ricci, and G. M. Verderame. 2020. Seismic fragility for Italian RC buildings based on damage data of the last 50 years. Bulletin of Earthquake Engineering 18 (5):2023–59. doi:10.1007/s10518-019-00762-6.
   Web of Science ®Google Scholar
• Del Gaudio, C., P. Ricci, G. M. Verderame, and G. Manfredi. 2017. Urban-scale seismic fragility assessment of RC buildings subjected to L’Aquila earthquake. Soil Dynamics and Earthquake Engineering 96:49–63. doi:10.1016/j.soildyn.2017.02.003.
   Web of Science ®Google Scholar
• De Luca, F., G. M. Verderame, and G. Manfredi. 2015. Analytical versus observational fragilities: The case of Pettino (L’aquila) damage data database. Bulletin of Earthquake Engineering 13 (4):1161–81. doi:10.1007/s10518-014-9658-1.
   Web of Science ®Google Scholar
• Demertzis, K., K. Kostinakis, K. Morfidis, and L. Iliadis. 2022. A comparative evaluation of machine learning algorithms for the prediction of R/C buildings’ seismic damage. arXiv preprint arXiv:2203.13449.
   Google Scholar
• Domaneschi, M., A. Z. Noori, M. V. Pietropinto, and G. P. Cimellaro. 2021. Seismic vulnerability assessment of existing school buildings. Computers & Structures 248:106522. doi:10.1016/j.compstruc.2021.106522.
   Web of Science ®Google Scholar
• Estabrooks, A., and N. Japkowicz. 2001. A mixture-of-experts framework for learning from imbalanced data sets. Advances in Intelligent Data Analysis: 4th International Conference, IDA 2001 Cascais, Portugal, September 13–15, 2001 Proceedings, 34-43. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• European Plate Observing System. European Mediterranean Seismological Center (EMSC). Accessed April 4, 2022. https://www.seismicportal.eu/.
   Google Scholar
• Fawagreh, K., M. M. Gaber, and E. Elyan. 2014. Random forests: From early developments to recent advancements. Systems Science and Control Engineering: An Open Access Journal 2 (1):602–09. doi:10.1080/21642583.2014.956265.
   Google Scholar
• Federal Emergency Management Agency. 2015. Rapid visual screening of buildings for potential seismic hazards: A handbook. 3rd ed. Washington, DC, USA: FEMA P-154; Homeland Security Department.
   Google Scholar
• Friedman, J., T. Hastie, and R. Tibshirani. 2001. The elements of statistical learning (Springer series in statistics), Vol. 1. Berlin: Springer. doi:10.1007/978-0-387-21606-5_1.
   Google Scholar
• Gautam, D., R. Adhikari, and R. Rupakhety. 2021. Seismic fragility of structural and non-structural elements of Nepali RC buildings. Engineering Structures 232:111879. doi:10.1016/j.engstruct.2021.111879.
   Web of Science ®Google Scholar
• Gehl, P., D. M. Seyedi, and J. Douglas. 2013. Vector-valued fragility functions for seismic risk evaluation. Bulletin of Earthquake Engineering 11 (2):365–84. doi:10.1007/s10518-012-9402-7.
   Web of Science ®Google Scholar
• Ghimire, S., P. Guéguen, S. Giffard-Roisin, and D. Schorlemmer. 2022. Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake. Earthquake Spectra 38 (4):2970–93. doi:10.1177/87552930221106495.
   Web of Science ®Google Scholar
• Goda, K., T. Kiyota, R. M. Pokhrel, G. Chiaro, T. Katagiri, K. Sharma, and S. Wilkinson. 2015. The 2015 Gorkha Nepal earthquake: Insights from earthquake damage survey. Frontiers in Built Environment 1:8. doi:10.3389/fbuil.2015.00008.
   Google Scholar
• Goretti, A., and G. Di Pasquale. 2002. An overview of post-earthquake damage assessment in Italy. Eeri invitational workshop. An action plan to develop earthquake damage and loss data protocols, California.
   Google Scholar
• Grillanda, N., M. Valente, G. Milani, A. Chiozzi, and A. Tralli. 2020. Advanced numerical strategies for seismic assessment of historical masonry aggregates. Engineering Structures 212:110441. doi:10.1016/j.engstruct.2020.110441.
   Web of Science ®Google Scholar
• Grünthal, G. 1998. European macroseismic scale 1998 EMS-98.
   Google Scholar
• Han, S. W., W. T. Kim, and D. A. Foutch. 2007. Tensile strength equation for HSS bracing members having slotted end connections. Earthquake Engineering & Structural Dynamics 36 (8):995–1008. doi:10.1002/eqe.665.
   Web of Science ®Google Scholar
• Hansapinyo, C., P. Latcharote, and S. Limkatanyu. 2020. Seismic building damage prediction from GIS-based building data using artificial intelligence system. Frontiers in Built Environment 6:576919. doi:10.3389/fbuil.2020.576919.
   Google Scholar
• Harirchian, E., S. E. A. Hosseini, K. Jadhav, V. Kumari, S. Rasulzade, E. Işık, M. Wasif, and T. Lahmer. 2021. A review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings. Journal of Building Engineering 43:102536. doi:10.1016/j.jobe.2021.102536.
   Web of Science ®Google Scholar
• Harirchian, E., V. Kumari, K. Jadhav, R. Raj Das, S. Rasulzade, and T. Lahmer. 2020a. A machine learning framework for assessing seismic hazard safety of reinforced concrete buildings. Applied Sciences 10 (20):7153. doi:10.3390/app10207153.
   Google Scholar
• Harirchian, E., T. Lahmer, S. Buddhiraju, K. Mohammad, and A. Mosavi. 2020b. Earthquake safety assessment of buildings through rapid visual screening. Buildings 10 (3):51. doi:10.3390/buildings10030051.
   Web of Science ®Google Scholar
• Harirchian, E., T. Lahmer, V. Kumari, and K. Jadhav. 2020c. Application of support vector machine modeling for the rapid seismic hazard safety evaluation of existing buildings. Energies 13 (13):3340. doi:10.3390/en13133340.
   Web of Science ®Google Scholar
• Hoskere, V., Y. Narazaki, T. A. Hoang, and B. F. Spencer Jr. 2018. Towards automated post-earthquake inspections with deep learning-based condition-aware models. arXiv preprint arXiv:1809.09195.
   Google Scholar
• Hwang, S. H., S. Mangalathu, J. Shin, and J. S. Jeon. 2021. Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames. Journal of Building Engineering 34:101905. doi:10.1016/j.jobe.2020.101905.
   Web of Science ®Google Scholar
• Kegyes-Brassai, O. R. S. O. L. Y. A. 2019. Vulnerability assessment of buildings based on rapid visual screening and pushover: Case study of gyor, hungary, Vol. 185, 63–74. Southampton, UK: WIT Press.
   Google Scholar
• Kermani, E., Y. Jafarian, and M. H. Baziar. 2009. New predictive models for the vmax/amax ratio of strong ground motions using genetic programming.
   Google Scholar
• Kiani, J., C. Camp, and S. Pezeshk. 2019. On the application of machine learning techniques to derive seismic fragility curves. Computers & Structures 218:108–22. doi:10.1016/j.compstruc.2019.03.004.
   Web of Science ®Google Scholar
• Kia, A., and S. Şensoy. 2014. Assessment the effective ground motion parameters on seismic performance of R/C buildings using artificial neural network.
   Google Scholar
• Luca, F., and G. Verderame. 2015. Seismic vulnerability assessment: Reinforced concrete structures, 1–31. Berlin/Heidelberg, Germany: Springer. doi:10.1007/978-3-642-36197-5_252-1.
   Google Scholar
• Lu, X., V. Plevris, G. Tsiatas, and D. De Domenico. 2022. Editorial: Artificial intelligence-powered methodologies and applications in earthquake and structural engineering. Frontiers in Built Environment 8. doi:10.3389/fbuil.2022.876077.
   Google Scholar
• Mangalathu Sivasubramanian Pillai, S. 2017. Performance based grouping and fragility analysis of box-girder bridges in California. Doctoral dissertation, Georgia Institute of Technology.
   Google Scholar
• Mangalathu, S., H. Sun, C. C. Nweke, Z. Yi, and H. V. Burton. 2020. Classifying earthquake damage to buildings using machine learning. Earthquake Spectra 36 (1):183–208. doi:10.1177/8755293019878137.
   Web of Science ®Google Scholar
• Meskouris, K., W. B. Kratzig, and U. Hanskotter. 1992. Seismic motion damage potential for R/C wall-stiffened buildings. Nonlinear seismic analysis and design of reinforced concrete buildings, 125–36. Oxford: Elsevier Applied Science.
   Google Scholar
• Molas, G. L., and F. Yamazaki. 1995. Neural networks for quick earthquake damage estimation. Earthquake Engineering & Structural Dynamics 24 (4):505–16. doi:10.1002/eqe.4290240404.
   Web of Science ®Google Scholar
• Morfidis, K., and K. Kostinakis. 2017. Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks. Advances in Engineering Software 106:1–16. doi:10.1016/j.advengsoft.2017.01.001.
   Web of Science ®Google Scholar
• Morfidis, K., and K. Kostinakis. 2018. Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks. Engineering Structures 165:120–41. doi:10.1016/j.engstruct.2018.03.028.
   Web of Science ®Google Scholar
• National Planning Commission. 2022. Nepal earthquake: Open data portal. Accessed August 11, 2022. http://www.eq2015.npc.gov.np/.
   Google Scholar
• NIED. The NIED strong-motion seismograph networks. Accessed March 1, 2022. http://www.kyoshin.bosai.go.jp.
   Google Scholar
• Ningthoujam, M. C., and R. P. Nanda. 2018. Rapid visual screening procedure of existing building based on statistical analysis. International Journal of Disaster Risk Reduction 28:720–30. doi:10.1016/j.ijdrr.2018.01.033.
   Web of Science ®Google Scholar
• OECD. 2018. Financial management of earthquake risk. Accessed June 10, 2022. http://www.oecd.org/finance/insurance/Financial-management-of-earthquake-risk.pdf.
   Google Scholar
• Okamura, M., N. P. Bhandary, S. Mori, N. Marasini, and H. Hazarika. 2015. Report on a reconnaissance survey of damage in Kathmandu caused by the 2015 Gorkha Nepal earthquake. Soils and Foundations 55 (5):1015–29. doi:10.1016/j.sandf.2015.09.005.
   Web of Science ®Google Scholar
• Porter, K. 2015. A beginner’s guide to fragility, vulnerability, and risk. Encyclopedia of Earthquake Engineering 139: 235–60.
   Google Scholar
• Pothon, A., P. Gueguen, S. Buisine, and P. Y. Bard. 2020. Comparing probabilistic seismic hazard maps with ShakeMap footprints for Indonesia. Seismological Research Letters 91 (2A):847–58. doi:10.1785/0220190171.
   Google Scholar
• Preciado, A. 2015. Seismic vulnerability and failure modes simulation of ancient masonry towers by validated virtual finite element models. Engineering Failure Analysis 57:72–87. doi:10.1016/j.engfailanal.2015.07.030.
   Web of Science ®Google Scholar
• Rai, D. C. 2005. Seismic evaluation and strengthening of existing buildings, 1–120. Gandhinagar, India: IIT Kanpur and Gujarat State Disaster Mitigation Authority.
   Google Scholar
• Riedel, I., and P. Guéguen. 2018. Modeling of damage-related earthquake losses in a moderate seismic-prone country and cost–benefit evaluation of retrofit investments: Application to France. Natural Hazards 90 (2):639–62. doi:10.1007/s11069-017-3061-6.
   Web of Science ®Google Scholar
• Riedel, I., P. Guéguen, M. Dalla Mura, E. Pathier, T. Leduc, and J. Chanussot. 2015. Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods. Natural Hazards 76 (2):1111–41. doi:10.1007/s11069-014-1538-0.
   Web of Science ®Google Scholar
• Roeslin, S., Q. Ma, H. Juárez-Garcia, A. Gómez-Bernal, J. Wicker, and L. Wotherspoon. 2020. A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake. Earthquake Spectra 36 (2_suppl):314–39. doi:10.1177/8755293020936714.
   Web of Science ®Google Scholar
• Rofooei, F. R., A. Kaveh, and F. M. Farahani. 2011. Estimating the vulnerability of the concrete moment resisting frame structures using artificial neural networks. International Journal of Optimization in Civil Engineering 1 (3):433–48.
   Google Scholar
• Rossetto, T., and A. Elnashai. 2003. Derivation of vulnerability functions for European-type RC structures based on observational data. Engineering Structures 25 (10):1241–63. doi:10.1016/S0141-0296(03)00060-9.
   Web of Science ®Google Scholar
• Rota, M., A. Penna, and G. Magenes. 2010. A methodology for deriving analytical fragility curves for masonry buildings based on stochastic nonlinear analyses. Engineering Structures 32 (5):1312–23. doi:10.1016/j.engstruct.2010.01.009.
   Web of Science ®Google Scholar
• Sajan, K. C., A. Bhusal, D. Gautam, and R. Rupakhety. 2023. Earthquake damage and rehabilitation intervention prediction using machine learning. Engineering Failure Analysis 144:106949. doi:10.1016/j.engfailanal.2022.106949.
   Web of Science ®Google Scholar
• Schorlemmer, D., T. Beutin, N. Hirata, K. Hao, M. Wyss, F. Cotton, and K. Prehn. 2017. Global dynamic exposure and the OpenBuildingMap-communicating risk and involving communities, Geophysical Research Abstracts 19:7060.
   Google Scholar
• Silva, V., S. Akkar, J. Baker, P. Bazzurro, J. M. Castro, H. Crowley, M. Dolsek, C. Galasso, S. Lagomarsino, R. Monteiro, et al. 2019. Current challenges and future trends in analytical fragility and vulnerability modeling. Earthquake Spectra 35 (4):1927–52. doi:10.1193/042418EQS101O.
   Web of Science ®Google Scholar
• Stojadinović, Z., M. Kovačević, D. Marinković, and B. Stojadinović. 2022. Rapid earthquake loss assessment based on machine learning and representative sampling. Earthquake Spectra 38 (1):152–77. doi:10.1177/87552930211042393.
   Web of Science ®Google Scholar
• Sun, H., H. V. Burton, and H. Huang. 2021. Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering 33:101816. doi:10.1016/j.jobe.2020.101816.
   Web of Science ®Google Scholar
• Sun, H., H. Burton, and J. Wallace. 2019. Reconstructing seismic response demands across multiple tall buildings using kernel‐based machine learning methods. Structural Control and Health Monitoring 26 (7):e2359. doi:10.1002/stc.2359.
   Web of Science ®Google Scholar
• Tang, Q., J. Dang, Y. Cui, X. Wang, and J. Jia. 2022. Machine learning-based fast seismic risk assessment of building structures. Journal of Earthquake Engineering 26 (15):8041–62. doi:10.1080/13632469.2021.1987354.
   Web of Science ®Google Scholar
• Trifunac, M. D., and M. I. Todorovska. 1997. Northridge, California, earthquake of 1994: Density of red-tagged buildings versus peak horizontal velocity and intensity of shaking. Soil Dynamics and Earthquake Engineering 16 (3):209–22. doi:10.1016/S0267-7261(96)00043-7.
   Web of Science ®Google Scholar
• United State Geological Survey. Accessed August 11, 2022. https://earthquake.usgs.gov/earthquakes/eventpage/us20002926/shakemap/intensity.
   Google Scholar
• United States Geological Survey (USGS). Center for Engineering Strong Motion Data (CESMD). Accessed April 3, 2022. https://strongmotioncenter.org/
   Google Scholar
• Vafaei, M., A. B. Adnan, and A. B. Abd Rahman. 2013. Real-time seismic damage detection of concrete shear walls using artificial neural networks. Journal of Earthquake Engineering 17 (1):137–54. doi:10.1080/13632469.2012.713559.
   Web of Science ®Google Scholar
• Vallejo, C. B. 2010. Rapid visual screening of buildings in the city of Manila, Philippines. 5th Civil Engineering Conference in the Asian Region and Australasian Structural Engineering Conference 2010, 513–18. Sydney, NSW: Engineers Australia, January.
   Google Scholar
• Xie, Y., M. Ebad Sichani, J. E. Padgett, and R. DesRoches. 2020. The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthquake Spectra 36 (4):1769–801. doi:10.1177/8755293020919419.
   Web of Science ®Google Scholar
• Xiong, C., X. Lu, J. Huang, and H. Guan. 2019. Multi-LOD seismic-damage simulation of urban buildings and case study in Beijing CBD. Bulletin of Earthquake Engineering 17 (4):2037–57. doi:10.1007/s10518-018-00522-y.
   Web of Science ®Google Scholar
• Xu, Y., X. Lu, Y. Tian, and Y. Huang. 2022. Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning. Journal of Earthquake Engineering 26 (8):4259–79. doi:10.1080/13632469.2020.1826371.
   Web of Science ®Google Scholar
• Xu, Z., Y. Wu, M. Z. Qi, M. Zheng, C. Xiong, and X. Lu. 2020. Prediction of structural type for city-scale seismic damage simulation based on machine learning. Applied Sciences 10 (5):1795. doi:10.3390/app10051795.
   Google Scholar
• Zhang, Y., H. V. Burton, H. Sun, and M. Shokrabadi. 2018. A machine learning framework for assessing post-earthquake structural safety. Structural Safety 72:1–16. doi:10.1016/j.strusafe.2017.12.001.
   Web of Science ®Google Scholar