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

Abstract and Applied Analysis ◽

10.1155/2013/242941 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 2

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.

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

Abstract and Applied Analysis ◽

10.1155/2012/394382 ◽

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.

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Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

Journal of Marine Science and Engineering ◽

10.3390/jmse8100766 ◽

2020 ◽

Vol 8 (10) ◽

pp. 766

Author(s):

Dohan Oh ◽

Julia Race ◽

Selda Oterkus ◽

Bonguk Koo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Machine Learning Techniques ◽

Burst Pressure ◽

Comparison Results ◽

Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

Journal of Applied Mathematics ◽

10.1155/2013/953548 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Jinxing Shen ◽

Wenquan Li

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Back Propagation ◽

Traffic Volume ◽

Wavelet Neural Network ◽

Time Interval ◽

Short Term ◽

Comparison Results ◽

Hidden Neurons

In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM) is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN). The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.

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