scholarly journals Development of Neural Network Model for Predicting Peak Ground Acceleration Based on Microtremor Measurement and Soil Boring Test Data

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
T. Kerh ◽  
J. S. Lin ◽  
D. Gunaratnam
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Test Data ◽  
Neural Network Model ◽  
Peak Ground Acceleration ◽  
Soil Depth ◽  
Standard Penetration Test ◽  
Ground Acceleration ◽  
Short Period ◽  
Microtremor Measurement

It may not be possible to collect adequate records of strong ground motions in a short period of time; hence microtremor survey is frequently conducted to reveal the stratum structure and earthquake characteristics at a specified construction site. This paper is therefore aimed at developing a neural network model, based on available microtremor measurement and on-site soil boring test data, for predicting peak ground acceleration at a site, in a science park of Taiwan. The four key parameters used as inputs for the model are soil values of the standard penetration test, the medium grain size, the safety factor against liquefaction, and the distance between soil depth and measuring station. The results show that a neural network model with four neurons in the hidden layer can achieve better performance than other models presently available. Also, a weight-based neural network model is developed to provide reliable prediction of peak ground acceleration at an unmeasured site based on data at three nearby measuring stations. The method employed in this paper provides a new way to treat this type of seismic-related problem, and it may be applicable to other areas of interest around the world.

scholarly journals Seismic Design Value Evaluation Based on Checking Records and Site Geological Conditions Using Artificial Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Tienfuan Kerh ◽  
Yutang Lin ◽  
Rob Saunders
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Seismic Design ◽  
Neural Network Model ◽  
Peak Ground Acceleration ◽  
Epicentral Distance ◽  
Ground Acceleration ◽  
Comparison Results ◽  
Geological Conditions ◽  
Design Value

This study proposes an improved computational neural network model that uses three seismic parameters (i.e., local magnitude, epicentral distance, and epicenter depth) and two geological conditions (i.e., shear wave velocity and standard penetration test value) as the inputs for predicting peak ground acceleration—the key element for evaluating earthquake response. Initial comparison results show that a neural network model with three neurons in the hidden layer can achieve relatively better performance based on the evaluation index of correlation coefficient or mean square error. This study further develops a new weight-based neural network model for estimating peak ground acceleration at unchecked sites. Four locations identified to have higher estimated peak ground accelerations than that of the seismic design value in the 24 subdivision zones are investigated in Taiwan. Finally, this study develops a new equation for the relationship of horizontal peak ground acceleration and focal distance by the curve fitting method. This equation represents seismic characteristics in Taiwan region more reliably and reasonably. The results of this study provide an insight into this type of nonlinear problem, and the proposed method may be applicable to other areas of interest around the world.

The Network Prediction of Hot Temperature Flow Stress and Dynamic Recrystallization of AZ61 Alloy

2014 ◽  
Vol 989-994 ◽  
pp. 544-547
Author(s):  
Quan Li ◽  
Wen Jun Liu ◽  
Ren Ju Cheng ◽  
Cheng Li ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Stress ◽  
Magnesium Alloy ◽  
Constitutive Model ◽  
Dynamic Recrystallization ◽  
Network Model ◽  
Test Data ◽  
Neural Network Model ◽  
Az61 Magnesium Alloy ◽  
The Neural Network

In this paper, the Back-Propagation neural network (BP network) and the establishment of the AZ61 magnesium alloy high temperature constitutive model and test data obtained for training the neural network, after training the neural network to become a knowledge-based constitutive model formed AZ61 magnesium alloy flow stress and dynamic recrystallization of the neural network model tested by the neural network model with traditional regression methods predict contrast, results showed that the higher the accuracy of the neural network model for dealing with a large number of test data to establish the constitutive relations of materials with high stress and promotion of value.

Artificial neural network model for estimating the soil temperature

2011 ◽  
Vol 91 (4) ◽  
pp. 551-562 ◽  
Author(s):  
Murat Ozturk ◽  
Ozlem Salman ◽  
Murat Koc
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Soil Depth ◽  
Sunshine Duration ◽  
Artificial Neural ◽  
Monitoring Stations

Ozturk, M., Salman, O. and Koc, M. 2011. Artificial neural network model for estimating the soil temperature. Can. J. Soil Sci. 91: 551–562. Although soil temperature is a critically important agricultural and environmental factor, it is typically monitored with low spatial resolution and, as a result, methods are required to estimate soil temperature at locations remote from monitoring stations. In this study, cost-effective, feed-forward artificial neural network (ANN) models are developed and tested for estimating soil temperature at 5-, 10-, 20-, 50- and 100-cm depths using standard geographical and meteorological data (i.e., altitude, latitude, longitude, month, year, monthly solar radiation, monthly sunshine duration and monthly mean air temperature). These data plus measured monthly mean soil temperature were collected for 2006–2008 from 66 monitoring stations distributed throughout Turkey to obtain a total of 2376 data records (36 months×66 monitoring stations) for each of the five soil depths. At each soil depth, 1800 randomly selected data records were used to develop and train a separate ANN model, and the remaining 576 records at each depth were used to test and validate the resulting models. Good agreement was obtained between ANN-estimated soil temperature and measured soil temperature, as evidenced by correlation coefficients of 98.91, 97.99, 99.03, 98.26 and 95.37% for the 5-, 10-, 20-, 50- and 100-cm soil depths, respectively. It was concluded that ANN modeling is a reliable method for predicting monthly mean soil temperature in regions of Turkey where soil temperature monitoring stations are not present.

scholarly journals Prediction of COVID-19 Using Recurrent Neural Network Model

2021 ◽  
Vol 8 (1) ◽  
pp. 98-103
Author(s):  
Alamsyah Alamsyah ◽  
Budi Prasetiyo ◽  
M. Faris Al Hakim ◽  
Fadli Dony Pradana
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Test Data ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Training Data ◽  
Elman Network ◽  
The World ◽  
Almost All ◽  
Hidden Nodes

The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN). In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. The research results show the percentage of accuracy is 88.

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