dor_id: 4133044
506.#.#.a: Público
590.#.#.d: Los artículos enviados a la revista "Geofísica Internacional", se juzgan por medio de un proceso de revisión por pares
510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Scientific Electronic Library Online (SciELO); SCOPUS, Dialnet, Directory of Open Access Journals (DOAJ); Geobase
561.#.#.u: https://www.geofisica.unam.mx/
650.#.4.x: Físico Matemáticas y Ciencias de la Tierra
336.#.#.b: article
336.#.#.3: Artículo de Investigación
336.#.#.a: Artículo
351.#.#.6: http://revistagi.geofisica.unam.mx/index.php/RGI
351.#.#.b: Geofísica Internacional
351.#.#.a: Artículos
harvesting_group: RevistasUNAM
270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx
590.#.#.c: Open Journal Systems (OJS)
270.#.#.d: MX
270.1.#.d: México
590.#.#.b: Concentrador
883.#.#.u: https://revistas.unam.mx/catalogo/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: http://revistagi.geofisica.unam.mx/index.php/RGI/article/view/1414/1428
100.1.#.a: Flores-mendoza, R.; Rodríguez-alcántara, Josué Uriel; Pozos-estrada, Adrian; Gómez, R.
524.#.#.a: Flores-mendoza, R., et al. (2022). Use of Artificial Neural Networks to predict strong ground motion duration of interplate and inslab mexican Earthquakes for soft and firm soils. Geofísica Internacional; Vol. 61 Núm. 3: Julio 1, 2022; 153-179. Recuperado de https://repositorio.unam.mx/contenidos/4133044
245.1.0.a: Use of Artificial Neural Networks to predict strong ground motion duration of interplate and inslab mexican Earthquakes for soft and firm soils
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Geofísica, UNAM
264.#.0.c: 2022
264.#.1.c: 2022-07-01
653.#.#.a: Red neuronal artificial; Duración del movimiento fuerte del terreno; Expresiones empíricas y México; Eventos de subducción; Artificial neural network; Strong ground motion duration; Subduction events; Empirical expressions and Mexico
506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico revistagi@igeofisica.unam.mx
884.#.#.k: http://revistagi.geofisica.unam.mx/index.php/RGI/article/view/1414
001.#.#.#: 063.oai:revistagi.geofisica.unam.mx:article/1414
041.#.7.h: spa
520.3.#.a: Artificial neural network models are developed to predict strong ground motion duration of sub- duction events for soft and firm soils. To train the artificial neural network a database with a total of 3153 seismic records with two horizontal components for interplate and inslab earthquakes is employed. The principal component method is used to carry out a dimensionality reduction of the input parameters to develop the artificial neural network models. The predicted values of the strong ground motion duration trained by the artificial neural network models are compared with those estimated with empirical expressions. In general, the strong ground motion duration predicted with the artificial neural networks follows the same tendency of that calculated with the empirical equa- tions, although in some cases, the strong ground motion duration predicted by using the artificial neural network models presents sudden changes in its behavior. For this reason, it is recommended to carry out several verifications of the trained artificial neural network models before using them for further engineering applications, for example the simulation of synthetic records or the evaluation of seismic damage indices.doi: https://doi.org/10.22201/igeof.00167169p.2022.61.3.2043
773.1.#.t: Geofísica Internacional; Vol. 61 Núm. 3: Julio 1, 2022; 153-179
773.1.#.o: http://revistagi.geofisica.unam.mx/index.php/RGI
022.#.#.a: ISSN-L: 2954-436X; ISSN impreso: 0016-7169
310.#.#.a: Trimestral
300.#.#.a: Páginas: 153-179
264.#.1.b: Instituto de Geofísica, UNAM
doi: https://doi.org/10.22201/igeof.00167169p.2022.61.3.2043
handle: 00c235751171efaf
harvesting_date: 2023-06-20 16:00:00.0
856.#.0.q: application/pdf
file_creation_date: 2022-06-27 17:58:39.0
file_modification_date: 2022-07-20 21:24:09.0
file_creator: R. Flores-Mendoza
file_name: 2baf0d35a39e918e16c014b03c61a7baccb7632ee6d4da02d8e403e91c7ce952.pdf
file_pages_number: 27
file_format_version: application/pdf; version=1.4
file_size: 9979961
245.1.0.b: Use of Artificial Neural Networks to predict strong ground motion duration of interplate and inslab mexican Earthquakes for soft and firm soils
last_modified: 2023-06-20 16:00:00
license_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es
license_type: by-nc-nd
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