Volume 9, Issue 3 (6-2019)                   2019, 9(3): 395-410 | Back to browse issues page

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Asadzadeh Khoshemehr G, Bahadori H. PREDICTIVE MODELS OF THE DOMINANT PERIOD OF SITE USING ARTIFICIAL NEURAL NETWORK AND MICROTREMOR MEASUREMENTS: APPLICATION TO URMIA, IRAN. International Journal of Optimization in Civil Engineering 2019; 9 (3) :395-410
URL: http://ijoce.iust.ac.ir/article-1-397-en.html
Abstract:   (15146 Views)
Direct drilling method and the use of microtremor studies are among the most commonly used available methods utilized to estimate dynamic parameters for a site. One of the most important parameters is the dominant period of the site whose estimation plays a pivotal role in seismic hazard mitigation. The conventional models obtained are not capable of estimating the parameters that govern the seismic response of a site. Therefore, Artificial Neural Networks (ANNs) are reliable and practical estimation methods that can be used to analyze comprehensive measurements such as dominant period of a site, and improve the data. In this paper, the performance of ANNs has been investigated on calculation of the dominant period for a site. Three different models, namely BP, RBF and ANFIS, have been compared to determine the best model that provides the most accurate estimation for the dominant period. The input parameters have been chosen to be alluvial layer thickness, grain size, specific gravity, effective stress, shear wave velocity, standard penetration number, Atterberg limits. Each of the three models has been trained and tested for these input parameters and a unique output which is the dominant period of the site. The results showed that ANNs successfully model complex relationships between soil parameters and seismic parameters of the site, and provide a robust tool to accurately estimate the dominant period of a site. The accurate estimations can be then used for engineering applications including damage assessment and structural health monitoring. In addition, The obtained emulator of RBF model shows the least model error in estimation of dominant period and has been found to be superior to the other evaluated methods.
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Type of Study: Research | Subject: Applications
Received: 2019/02/18 | Accepted: 2019/02/18 | Published: 2019/02/18

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