Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures-Reference-Cited by-同舟云学术

Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures

Published:2024-03 Issue: Volume:21 Page:101750
ISSN:2590-1230
Container-title:Results in Engineering
language:en
Short-container-title:Results in Engineering

Author:

Harirchian Ehsan^ORCID,Aghakouchaki Hosseini Seyed Ehsan,Novelli Viviana,Lahmer Tom,Rasulzade Shahla

Funder

Deutsche Forschungsgemeinschaft

Bauhaus-Universität Weimar

Publisher

Elsevier BV

Reference70 articles.

1. State of the art of seismic Protection Technologies for non-engineered buildings (N-EBs) in developing regions of the world;Galano;J. Earthq. Eng.,2023

2. Seismic fragility models for base‐isolated unreinforced masonry buildings with fibre‐reinforced elastomeric isolators;Losanno;Earthq. Eng. Struct. Dynam.,2023

3. Review of seismic risk mitigation policies in earthquake-prone countries: lessons for earthquake resilience in the United States;Zhang;J. Earthq. Eng.,2022

4. Development of a uniform exposure model for the African continent for use in disaster risk assessment;Paul;Int. J. Disaster Risk Reduc.,2022

5. Scenario-based earthquake risk assessment for central-southern Malawi: the case of the Bilila-Mtakataka Fault;Goda;Int. J. Disaster Risk Reduc.,2022

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