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

No entro en nada

No entro en nada 2