Parametric and Machine Learning-Based Analysis of the Seismic Vulnerability of Adobe Historical Buildings Damaged After the September 2017 Mexico Earthquakes-Reference-Cited by-同舟云学术

Parametric and Machine Learning-Based Analysis of the Seismic Vulnerability of Adobe Historical Buildings Damaged After the September 2017 Mexico Earthquakes

Published:2023-04-16 Issue: Volume: Page:1-24
ISSN:1558-3058
Container-title:International Journal of Architectural Heritage
language:en
Short-container-title:International Journal of Architectural Heritage

Author:

Ramírez Eudave Rafael¹^ORCID,Ferreira Tiago Miguel²^ORCID,Vicente Romeu³^ORCID,Lourenco Paulo B.¹^ORCID,Peña Fernando⁴^ORCID

Affiliation:

1. Institute for Sustainability and Innovation in Structural Engineering (ISISE), Department of Civil Engineering, University of Minho, Guimaraes, Portugal

2. School of Engineering, College of Arts, Technology and Environment (CATE), University of the West of England (UWE Bristol), Bristol, UK

3. RISCO, Departamento de Engenharia Civil, Universidade de Aveiro, Aveiro, Portugal

4. Instituto de Ingeniería, Universidad Nacional Autónoma de México, Mexico City, Mexico

Funder

Universidad Nacional Autónoma de México

Publisher

Informa UK Limited

Subject

Visual Arts and Performing Arts,Architecture,Conservation

Link

https://www.tandfonline.com/doi/pdf/10.1080/15583058.2023.2200739

Reference47 articles.

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3. ANÁLISIS DE LOS DAÑOS EN VIVIENDAS Y EDIFICIOS COMERCIALES DURANTE LA OCURRENCIA DEL SISMO DEL 19 DE SEPTIEMBRE DE 2017

4. Buitinck, L., G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, et al. 2013. API design for machine learning software: Experiences from the scikit-learn project 1–15.

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