Prediction of Inelastic Response Spectra Using Artificial Neural Networks

Bojórquez, Edén; Ruiz, Sonia E.; Reyes‐Salazar, Alfredo

doi:10.1155/2012/937480

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…Analytical Network Process (ANP) [113] is a more general form and extension of the Analytical Hierarchy Process (AHP) developed by [114][115][116][117]. ANP represents hierarchical relationships, whereas AHP is based on a hierarchical structure.…”

Section: Analytic Network Process (Anp) Approachmentioning

confidence: 99%

A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment

et al. 2018

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Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city's vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management.

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Section: Analytic Network Process (Anp) Approachmentioning

confidence: 99%

A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment

et al. 2018

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“…In terms of functionality, MLP has no difference from the neural networks used in both NARNN and NARXNN models if the input is time series. Additionally MLPs have been proven to be able to approximate any continuous function by adjusting the number of nodes in the hidden layer [12], with numerous cases of successful applications [13,14,[22][23][24]. Figure 3 illustrates the general structure of a three-layer MLP with one hidden layer of nodes, a -dimensional input vector X, and a -dimensional output vector Y.…”

Section: Spatial Feedforward Neural Network Forecasting Modelmentioning

confidence: 99%

Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models

Guo

Xue

2014

Mathematical Problems in Engineering

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Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

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“…Con ello se pretende demostrar la eficiencia de I B como predictor de la respuesta de estructuras con comportamiento inelástico no-lineal y bajo la influencia de modos superiores, comparado con I Np y con Sa(T 1 ). Finalmente, aunque resulta de mayor complejidad estimar I B en comparación con otras medidas de intensidad sísmica; dado que la obtención del parámetro propuesto depende de la forma espectral, éste podría calcularse a partir de las características del sitio y la fuente por ejemplo utilizando redes neuronales artificiales (Alcantara et al, 2009, Bojórquez et al, 2012b.…”

Section: Casos Particulares En Estudio De I Bunclassified

Una Medida De Intensidad Sísmica Que Predice El Comportamiento No Lineal Y El Efecto De Los Modos Superiores

Mora¹,

López

Ruiz³

et al. 2014

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RESUMENEl objetivo de este trabajo es analizar la eficiencia de una medida de intensidad sísmica (IS) capaz de predecir tanto el comportamiento no lineal como el efecto de los modos superiores de estructuras sometidas a sismos de diversas características. La medida definida como intensidad de Bojórquez está inspirada en un parámetro para caracterizar la forma espectral denominado N p . Aunque normalmente la forma espectral más utiliza es en términos de la pseudo-aceleración, en este trabajo con la finalidad de mejorar la eficiencia de la medida de intensidad, se utilizan otros parámetros tales como velocidad y desplazamiento. La eficiencia de la medida de intensidad sísmica aquí presentada se obtiene al someter varias edificaciones de acero a movimientos sísmicos registrados en suelos con distintas características. Se concluye que el parámetro analizado muestra buena relación con la respuesta estructural y es más eficiente que otras medidas de intensidad sísmica usadas frecuentemente.Palabras clave: intensidad sísmica; forma espectral; eficiencia; respuesta sísmica; marcos de acero A GROUND MOTION INTENSITY MEASURE TO PREDICT NONLINEAR BEHAVIOR AND HIGHER MODE EFFECTS ABSTRACTThe aim of this paper is to analyze the Bojórquez ground motion intensity measure in order to improve its efficiency to predict both the nonlinear behavior and the higher modes effects of structures subjected to earthquakes of different characteristics. The intensity measure is generally based on a proxy of the spectral shape named N p . Although the most commonly spectral shape used is in term of the spectral acceleration, in this paper with the aim to improve the efficiency of the Artículo recibido el 8 de noviembre de 2012 y aprobado para su publicación el 24 de abril de 2014. Se aceptarán comentarios y/o discusiones hasta cinco meses después de su publicación.

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Prediction of Inelastic Response Spectra Using Artificial Neural Networks

Cited by 15 publications

References 21 publications

A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment

A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment

Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models

Una Medida De Intensidad Sísmica Que Predice El Comportamiento No Lineal Y El Efecto De Los Modos Superiores

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