Combined machine learning – 3D physics based approach for building damage evaluation: the case of L’Aquila 2009
https://www.nature.com/articles/s41598-026-45377-5
Publish Date: 2026-03-26 23:45:00
Source Domain: www.nature.com
Sun, Z. et al. A review of earth artificial intelligence. Comput. Geosci. 159, 105034 (2022).
Zhao, T. et al. Artificial intelligence for geoscience: Progress, challenges and perspectives. Innovation (2024).
Zhang, W. et al. Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge. Gondwana Res. 109, 1–17 (2022).
Monsalvo Franco, I. E. et al. Seismic fragility curves with unconventional ground motion intensity measures from physics-based simulations. Bull. Earthq. Eng. 23(5), 1885–1915 (2025).
Rossetto, T., Ioannou, I., Grant, D. & Maqsood, T. Guidelines for empirical vulnerability assessment (X, GEM Technical Reports, 2014).
Del Gaudio, C. et al. Empirical fragility curves from damage data on rc buildings after the 2009 L’Aquila earthquake. Bull. Earthq. Eng. 15, 1425–1450 (2017).
Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z. & Burton, H. V. Classifying earthquake damage to buildings using machine learning. Earthq. Spectra 36(1), 183–208 (2020).
Roeslin, S., Ma, Q., Juárez-Garcia, H., Gómez-Bernal, A., Wicker, J. & Wotherspoon, L. A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake. Earthq. Spectra 36(2_suppl), 314–339 (2020).
Harirchian, E. et al. A synthesized study based on machine learning approaches for rapid classifying earthquake damage grades to rc buildings. Appl. Sci. 11(16), 7540 (2021).
Stojadinović, Z., Kovačević, M., Marinković, D. & Stojadinović, B. Rapid earthquake loss assessment based on machine learning and representative…
