A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Machine Learning for Damage Classification, Risk Mitigation and Post-earthquake Management
https://orcid.org/http://orcid.org/0000-0003-1244-6942
https://orcid.org/http://orcid.org/0000-0003-4872-4713
https://orcid.org/http://orcid.org/0000-0001-6546-8390
https://orcid.org/http://orcid.org/0000-0003-0161-5923
https://orcid.org/http://orcid.org/0000-0003-4827-9058
https://orcid.org/http://orcid.org/0000-0001-6528-6562
In recent years, we have witnessed a steady increase in the amount of data collected and made available to the scientific community. Simultaneously, Artificial Intelligence (AI) shows great potential in transforming data to knowledge offering increasingly more accurate tools to analyze, interpret and extract information from the data. In this context, data-driven approaches, in the fields of seismology, geophysics, and earthquake engineering show enormous promise. In this work a Machine Learning (ML) study is presented, based on a dataset containing around 3000 buildings damaged by the 2009 L’Aquila earthquake. This event was the first in a series of strong earthquakes that hit central Italy, resulting in many casualties, and having enormous economic and social impact. Each building in the dataset is described by 22 characteristics. Among them the damage level, divided, into six classes, from D0, corresponding to no damage, to D5, corresponding to heavy damage or collapse. We employ a Random Forest based algorithm to predict the level of damage accounting for different combinations of damage levels. As a case study we consider the following binary target variables-D0-D1-D2-D3 (no to medium damage) and D4-D5 (serious to heavy damage)-D0-D1 (no to light damage) and D2-D3-D4-D5 (moderate to heavy damage)-D0-D1-D02 (light to moderate damage) and D3-D4-D5 (medium to heavy damage).
Machine Learning for Damage Classification, Risk Mitigation and Post-earthquake Management
https://orcid.org/http://orcid.org/0000-0003-1244-6942
https://orcid.org/http://orcid.org/0000-0003-4872-4713
https://orcid.org/http://orcid.org/0000-0001-6546-8390
https://orcid.org/http://orcid.org/0000-0003-0161-5923
https://orcid.org/http://orcid.org/0000-0003-4827-9058
https://orcid.org/http://orcid.org/0000-0001-6528-6562
In recent years, we have witnessed a steady increase in the amount of data collected and made available to the scientific community. Simultaneously, Artificial Intelligence (AI) shows great potential in transforming data to knowledge offering increasingly more accurate tools to analyze, interpret and extract information from the data. In this context, data-driven approaches, in the fields of seismology, geophysics, and earthquake engineering show enormous promise. In this work a Machine Learning (ML) study is presented, based on a dataset containing around 3000 buildings damaged by the 2009 L’Aquila earthquake. This event was the first in a series of strong earthquakes that hit central Italy, resulting in many casualties, and having enormous economic and social impact. Each building in the dataset is described by 22 characteristics. Among them the damage level, divided, into six classes, from D0, corresponding to no damage, to D5, corresponding to heavy damage or collapse. We employ a Random Forest based algorithm to predict the level of damage accounting for different combinations of damage levels. As a case study we consider the following binary target variables-D0-D1-D2-D3 (no to medium damage) and D4-D5 (serious to heavy damage)-D0-D1 (no to light damage) and D2-D3-D4-D5 (moderate to heavy damage)-D0-D1-D02 (light to moderate damage) and D3-D4-D5 (medium to heavy damage).
Machine Learning for Damage Classification, Risk Mitigation and Post-earthquake Management
https://orcid.org/http://orcid.org/0000-0003-1244-6942
https://orcid.org/http://orcid.org/0000-0003-4872-4713
https://orcid.org/http://orcid.org/0000-0001-6546-8390
https://orcid.org/http://orcid.org/0000-0003-0161-5923
https://orcid.org/http://orcid.org/0000-0003-4827-9058
https://orcid.org/http://orcid.org/0000-0001-6528-6562
In recent years, we have witnessed a steady increase in the amount of data collected and made available to the scientific community. Simultaneously, Artificial Intelligence (AI) shows great potential in transforming data to knowledge offering increasingly more accurate tools to analyze, interpret and extract information from the data. In this context, data-driven approaches, in the fields of seismology, geophysics, and earthquake engineering show enormous promise. In this work a Machine Learning (ML) study is presented, based on a dataset containing around 3000 buildings damaged by the 2009 L’Aquila earthquake. This event was the first in a series of strong earthquakes that hit central Italy, resulting in many casualties, and having enormous economic and social impact. Each building in the dataset is described by 22 characteristics. Among them the damage level, divided, into six classes, from D0, corresponding to no damage, to D5, corresponding to heavy damage or collapse. We employ a Random Forest based algorithm to predict the level of damage accounting for different combinations of damage levels. As a case study we consider the following binary target variables-D0-D1-D2-D3 (no to medium damage) and D4-D5 (serious to heavy damage)-D0-D1 (no to light damage) and D2-D3-D4-D5 (moderate to heavy damage)-D0-D1-D02 (light to moderate damage) and D3-D4-D5 (medium to heavy damage).
Machine Learning for Damage Classification, Risk Mitigation and Post-earthquake Management
Lecture Notes in Civil Engineering
Erberik, Murat Altug (editor) / Askan, Aysegul (editor) / Kockar, Mustafa Kerem (editor) / Di Michele, F. (author) / Giannopoulou, O. (author) / Stagnini, E. (author) / Pera, D. (author) / Rubino, B. (author) / Aloisio, R. (author) / Askan, A. (author)
International Conference on Energy and Environmental Science ; 2023 ; Antalya, Türkiye
Proceedings of the 7th International Conference on Earthquake Engineering and Seismology ; Chapter: 16 ; 181-190
2024-06-13
10 pages
Article/Chapter (Book)
Electronic Resource
English
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