A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering

Investigations made to evaluate the site-effect characteristics and to develop a reliable site classification scheme have received paramount importance for the urban areas planning and reliable site-specific seismic hazard assessment. This paper presents a novel non-objective and data-driven approac...

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Autores principales:	Sayed S. R. Moustafa, Mohamed S. Abdalzaher, Farhan Khan, Mohamed Metwaly, Eslam A. Elawadi, Nassir S. Al-Arifi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Machine learning seismic site classification HVSR clustering affinity propagation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/647478acc7cd426f8232d6c7d274be84
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spelling	oai:doaj.org-article:647478acc7cd426f8232d6c7d274be842021-11-26T00:00:36ZA Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering2169-353610.1109/ACCESS.2021.3128284https://doaj.org/article/647478acc7cd426f8232d6c7d274be842021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615045/https://doaj.org/toc/2169-3536Investigations made to evaluate the site-effect characteristics and to develop a reliable site classification scheme have received paramount importance for the urban areas planning and reliable site-specific seismic hazard assessment. This paper presents a novel non-objective and data-driven approach for preliminary seismic site-specific classification maps using machine learning (ML) based on affinity propagation (AP) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves inside King Saud University (KSU) campus, which is among the main areas in Saudi Arabia. Besides, the proposed model aims to overcome the clustering error due to the dependency of the interpreter’s experience. Measurements of the ambient vibrations were performed to cover the entire campus area by about 307 stations. Recording at each station lasted for 20 minute length and a sample rate of 128 Hz for each station to satisfy the criteria for reliable and unambiguous HVSR results. Frequency and amplification values were used for subsequent site classification by passing messages between data points. The obtained results illustrate that the microtremor spectral ratio can be a remarkably robust tool in determining site effects. Accordingly, the proposed methodology can assist the decision-makers to set the priorities of managing land uses, estimating the earthquake losses, conducting programs for reducing the vulnerability of existing structures, enforcing building codes, planning for emergency response and long-term recovery, and designing and implementing phases of new constructions.Sayed S. R. MoustafaMohamed S. AbdalzaherFarhan KhanMohamed MetwalyEslam A. ElawadiNassir S. Al-ArifiIEEEarticleMachine learningseismic site classificationHVSRclusteringaffinity propagationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155297-155313 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning seismic site classification HVSR clustering affinity propagation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Machine learning seismic site classification HVSR clustering affinity propagation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Sayed S. R. Moustafa Mohamed S. Abdalzaher Farhan Khan Mohamed Metwaly Eslam A. Elawadi Nassir S. Al-Arifi A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering
description	Investigations made to evaluate the site-effect characteristics and to develop a reliable site classification scheme have received paramount importance for the urban areas planning and reliable site-specific seismic hazard assessment. This paper presents a novel non-objective and data-driven approach for preliminary seismic site-specific classification maps using machine learning (ML) based on affinity propagation (AP) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves inside King Saud University (KSU) campus, which is among the main areas in Saudi Arabia. Besides, the proposed model aims to overcome the clustering error due to the dependency of the interpreter’s experience. Measurements of the ambient vibrations were performed to cover the entire campus area by about 307 stations. Recording at each station lasted for 20 minute length and a sample rate of 128 Hz for each station to satisfy the criteria for reliable and unambiguous HVSR results. Frequency and amplification values were used for subsequent site classification by passing messages between data points. The obtained results illustrate that the microtremor spectral ratio can be a remarkably robust tool in determining site effects. Accordingly, the proposed methodology can assist the decision-makers to set the priorities of managing land uses, estimating the earthquake losses, conducting programs for reducing the vulnerability of existing structures, enforcing building codes, planning for emergency response and long-term recovery, and designing and implementing phases of new constructions.
format	article
author	Sayed S. R. Moustafa Mohamed S. Abdalzaher Farhan Khan Mohamed Metwaly Eslam A. Elawadi Nassir S. Al-Arifi
author_facet	Sayed S. R. Moustafa Mohamed S. Abdalzaher Farhan Khan Mohamed Metwaly Eslam A. Elawadi Nassir S. Al-Arifi
author_sort	Sayed S. R. Moustafa
title	A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering
title_short	A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering
title_full	A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering
title_fullStr	A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering
title_full_unstemmed	A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering
title_sort	quantitative site-specific classification approach based on affinity propagation clustering
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/647478acc7cd426f8232d6c7d274be84
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A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering

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