EZRVS: An AI-Based Web Application to Significantly Enhance Seismic Rapid Visual Screening of Buildings

References

• Ahmed, S., A. Abarca, D. Perrone, and R. Monteiro. 2022. Large-scale seismic assessment of RC buildings through rapid visual screening. International Journal of Disaster Risk Reduction 80:103219. ISSN 2212-4209 doi:10.1016/j.ijdrr.2022.103219.
   Web of Science ®Google Scholar
• Ansal, A., K. Ozayd in, A. Edincliler, A. Saglamer, H. Sucuoglu, and P. Ozdemir. 2003. Earthquake master plan for Istanbul. Turkey: Metropolital Municipality of Istanbul, Planning and Construction Directorate, Geotechnical and Earthquake Investigation Department.
   Google Scholar
• Ayden, O., R. Ulusay, and M. Miyajima. 2003. The Bingol earthquake of May 1, 2003. Japan society of civil engineers.
   Google Scholar
• Cardellicchio, A., S. Ruggieri, V. Leggieri, and G. Uva. 2022. View VULMA: Data set for training a machine-learning tool for a fast vulnerability analysis of existing buildings. Data 7 (1):4. doi:10.3390/data7010004.
   Web of Science ®Google Scholar
• Cardellicchio, A., S. Ruggieri, V. Leggieri, and G. Uva. 2023. A machine learning framework to estimate a simple seismic vulnerability index from a photograph: The VULMA project. Procedia Structural Integrity 44:1956–63. ISSN 2452-3216 doi:10.1016/j.prostr.2023.01.250.
   Google Scholar
• Cardellicchio, A., S. Ruggieri, A. Nettis, C. Patruno, G. Uva, and V. Renò. 2022. Deep learning approaches for image-based detection and classification of structural defects in bridges. In Image analysis and processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture notes in computer science, ed. P. L. Mazzeo, E. Frontoni, S. Sclaroff, and C. Distante, vol. 13373. Cham: Springer. doi:10.1007/978-3-031-13321-3_24.
   Google Scholar
• Cardellicchio, A., S. Ruggieri, A. Nettis, V. Renò, and G. Uva. 2023. Physical interpretation of machine learning-based recognition of defects for the risk management of existing bridge heritage. Engineering Failure Analysis 149:107237. ISSN 1350-6307 doi:10.1016/j.engfailanal.2023.107237.
   Web of Science ®Google Scholar
• Christine, C. A., H. Chandima, P. Santiago, L. Lucas, S. Chungwook, P. Aishwarya, and B. Andres. 2018. A cyber platform for sharing scientific research data at datacenterhub. Computing in Science & Engineering 20 (3):49. doi:10.1109/mcse.2017.3301213.
   Web of Science ®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
• E. CEN. 2005. Eurocode 8: Design of structures for earthquake resistance-part 1: General rules, seismic actions and rules for buildings. Brussels: European Committee for Standardization, Management Centre.
   Google Scholar
• Erdik, M. (2001). Report on 1999 Kocaeli and Duzce (Turkey) earthquakes. In Structural control for civil and infrastructure engineering, 149–86. Singapore: World Scientific. doi:10.1142/9789812811707_0018.
   Google Scholar
• FEMA P-154. 2015. Rapid visual screening of buildings for potential seismic hazards: A Handbook, 3rd ed. Washington, D.C.: Federal Emergency Management Agency.
   Google Scholar
• Gentile, R., C. Galasso, Y. Idris, I. Rusydy, and E. Meilianda. 2019. From rapid visual survey to multi-hazard risk prioritisation and numerical fragility of school buildings. Natural Hazards and Earth System Sciences 19 (7):1365–86. doi:10.5194/nhess-19-1365-2019.
   Web of Science ®Google Scholar
• Greek Interventions Code. 2012. Earthquake planning and protection organization. Athens.
   Google Scholar
• Harirchian, E., K. Jadhav, V. Kumari, and T. Lahmer. 2021. ML-EHSAPP: A prototype for machine learning-based earthquake hazard safety assessment of structures by using a smartphone app. European Journal of Environmental and Civil Engineering 21:1964–8189. doi:10.1080/19648189.2021.1892829.
   Google Scholar
• Harirchian, E., and T. Lahmer. 2020a. Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings. In Structures, vol. 28, 1384–99. Elsevier. doi:10.1016/j.istruc.2020.09.048.
   Google Scholar
• Harirchian, E., and T. Lahmer (2020b). Improved rapid assessment of earthquake hazard safety of structures via artificial neural networks. IOP Conference Series: Materials Science and Engineering, Volume 897, 2020 5th International Conference on Civil Engineering and Materials Science (ICCEMS 2020), Singapore, 15–18 May 2020. IOP Publishing. doi:10.1088/1757-899X/897/1/012014.
   Google Scholar
• Harirchian, E., and T. Lahmer. 2020c. Improved rapid visual earthquake hazard safety evaluation of existing buildings using a type-2 fuzzy logic model. Applied Sciences 10 (7):2375. doi:10.3390/app10072375.
   Google Scholar
• Harirchian, E., T. Lahmer, V. Kumari, and K. Jadhav. 2020. 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
• Harirchian, E., T. Lahmer, and S. Rasulzade. 2020. Earthquake hazard safety assessment of existing buildings using optimized multi-layer perceptron neural network. Energies 13 (8):2060. doi:10.3390/en13082060.
   Web of Science ®Google Scholar
• Hassan, A. F., and M. A. Sozen. 1997. Seismic vulnerability assessment of low-rise buildings in regions with infrequent earthquakes. ACI Structural Journal 94 (1):31–39. http://www.concrete.org/Publications/ACIMaterialsJournal/ACIJournalSearch.aspx?m=details&ID=458.
   Web of Science ®Google Scholar
• Hosmer, D. W., Jr, S. Lemeshow, and R. X. Sturdivant. 2013. Applied logistic regression, vol. 398. USA: John Wiley & Sons. https://doi.org/10.1002/9781118548387
   Google Scholar
• Isik, M. F., E. Isik, and M. A. Bulbul. (2018). Application of ios/android based assessment and monitoring system for building inventory under seismic impact. Gradjevinar 70 (12):1043–56.
   Google Scholar
• Islam, M. S., H. Alwashali, D. Sen, and M. Maeda. 2020. A proposal of visual rating method to set the priority of detailed evaluation for masonry infilled RC building. Bulletin of Earthquake Engineering 18 (4):1613–34. doi:10.1007/s10518-019-00763-5.
   Web of Science ®Google Scholar
• Jain, S. K., K. Mitra, M. Kumar, and M. Shah. 2010. A proposed rapid visual screening procedure for seismic evaluation of RC-frame buildings in India. Earthquake Spectra 26 (3):709–29. doi:10.1193/1.3456711.
   Web of Science ®Google Scholar
• Kapetana, P., and S. Dritsos. 2007. Seismic assessment of buildings by rapid visual screening procedures. WIT Transactions on the Built Environment 93:409–18. doi:10.2495/ERES070391.
   Google Scholar
• Kaplan, O., Y. Guney, A. Topcu, and Y. Ozcelikors. 2018. A rapid seismic safety assessment method for mid-rise reinforced concrete buildings. Bulletin of Earthquake Engineering 16 (2):889–915. doi:https://doi.org/10.1007/s10518-017-0229-0.
   Web of Science ®Google Scholar
• Ketsap, A., C. Hansapinyo, N. Kronprasert, and S. Limkatanyu. 2019. Uncertainty and fuzzy decisions in earthquake risk evaluation of buildings. Engineering Journal 23 (5):89–105. doi:10.4186/ej.2019.23.5.89.
   Google Scholar
• Mandas, A., and S. Dritsos. 2004. Vulnerability assessment of rc structures using fuzzy logic. WIT Transactions on Ecology and the Environment 77:51–60. doi:10.2495/RISK040061.
   Google Scholar
• Mangalathu, S., S. H. Hwang, E. Choi, and J. S. Jeon. 2019. Rapid seismic damage evaluation of bridge portfolios using machine learning techniques. Engineering Structures 201:109785. doi:10.1016/j.engstruct.2019.109785.
   Web of Science ®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
• Moolayil, J. 2019. Learn keras for deep neural networks: A fast-track approach to modern deep learning with python. Vancouver, BC, Canada: Apress.
   Google Scholar
• Müller, A. C., and S. Guido. 2016. Introduction to machine learning with python. Sebastopol, CA, USA: O’Reilly.
   Google Scholar
• Ningthoujam, M., 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
• Novelli, V., and D. D’Ayala. 2015. Log-ideah: Logic trees for identification of damage due to earthquakes for architectural heritage. Bulletin of Earthquake Engineering 13 (1):153–76. doi:10.1007/s10518-014-9622-0.
   Web of Science ®Google Scholar
• Ozhendekci, N., and D. Ozhendekci. 2012. Rapid seismic vulnerability assessment of low-to mid-rise reinforced concrete buildings using Bingol’s regional data. Earthquake Spectra 28 (3):1165–87. doi:10.1193/1.4000065.
   Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12:2825–30.
   Web of Science ®Google Scholar
• Perrone, D., M. A. Aiello, M. Pecce, and F. Rossi. 2015. Rapid visual screening for seismic evaluation of RC hospital buildings. Structures 3:57–70. ISSN 2352-0124 doi:10.1016/j.istruc.2015.03.002.
   Web of Science ®Google Scholar
• Perrone, D., G. J. O’Reilly, R. Monteiro, and A. Filiatrault. 2020. Assessing seismic risk in typical Italian school buildings: From in-situ survey to loss estimation. International Journal of Disaster Risk Reduction 44:101448. doi:10.1016/j.ijdrr.2019.101448.
   Web of Science ®Google Scholar
• Rai, D. C. (2005). Review of documents on seismic evaluation of existing buildings. 32. Indian Institute of Technology, Kanpur.
   Google Scholar
• Ruggieri, S., A. Cardellicchio, V. Leggieri, and G. Uva. 2021. Machine-learning based vulnerability analysis of existing buildings. Automation in Construction 132 (103936). ISSN 0926-5805. doi:10.1016/j.autcon.2021.103936.
   Google Scholar
• Ruggieri, S., D. Perrone, M. Leone, G. Uva, and M. A. Aiello. 2020. A prioritization RVS methodology for the seismic risk assessment of RC school buildings. International Journal of Disaster Risk Reduction 51:101807. doi:10.1016/j.ijdrr.2020.101807.
   Web of Science ®Google Scholar
• Sen, Z. 2010. Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling. Expert Systems with Applications 37 (8):5653–60. doi:10.1016/j.eswa.2010.02.046.
   Web of Science ®Google Scholar
• Sim, C., C. Song, N. Skok, A. Irfanoglu, S. Pujol, and M. Sozen. 2016. Database of low-rise reinforced concrete buildings with earthquake damage. Accessed March 2022. https://datacenterhub.org/deedsdv/publications/view/454.
   Google Scholar
• Tesfamariam, S., and Z. Liu. 2010. Earthquake induced damage classification for reinforced concrete buildings. Structural Safety 32 (2):154–64. doi:10.1016/j.strusafe.2009.10.002.
   Web of Science ®Google Scholar
• Tesfamariam, S., and M. Saatcioglu. 2010. Seismic vulnerability assessment of reinforced concrete buildings using hierarchical fuzzy rule base modeling. Earthquake Spectra 26 (1):235–56. doi:10.1193/1.3280115.
   Web of Science ®Google Scholar
• T. J. B. D. P. A. 2001. Seismic evaluation and seismic retrofit of existing reinforced concrete buildings. Japan: The Japan building disaster prevention association.
   Google Scholar