scholarly journals Towards Data-Driven Evaluation of Site Effects From Coda of Strong Motion Timeseries Using Recurrent Neural Networks

2021 ◽  
Author(s):  
Mona Izadi ◽  
Shinichi Matsushima
Keyword(s):  
Neural Networks ◽  
Site Effects ◽  
Short Term Memory ◽  
Strong Motion ◽  
Coda Waves ◽  
Machine Learning Techniques ◽  
Coda Wave ◽  
Specific Information ◽  
Borehole Data ◽  
Strong Motion Records

Abstract It is known that coda of strong motion records are products of numerous scatterings of body and surface waves within the subsurface soil structure. Several studies have successfully simulated coda wave envelopes by modelling the energy decay. However, due to limitations of quantifying soil heterogeneities, deterministic simulation of scattered wavefields is much more challenging. Therefore, the reverse problem of estimating non statistical properties of subsurface structure from coda waves remains a theoretical potential. On the other hand, machine learning techniques have proven useful in dealing with problems of similar nature, where a theoretical solution is imaginable yet hard to achieve due to a great number of unknown variables. This study utilizes artificial neural networks to propose a new approach of evaluating site effects from coda waves, with the future prospect of obtaining the similar results from microtremor records. A Long Short-Term Memory recurrent neural network is designed using Tensorflow 2 library in Python language. The study utilizes a strong motion dataset consisting of about 60000 3-component records as well as borehole data at 464 stations of Kiban-Kyoshin Network across Japan. The prediction input is coda wave timeseries of strong motion records, defined based on a parametric energy criterion, and all 3 seismograph components EW, NS and UD are used as parallel sequential features. In the first step, the prediction target is a vector of 3 site effect proxies namely, time-averaged shear-wave velocities for the upper 30-m depth Vs30 and the upper 10-m depth Vs10 and predominant frequency f0. In this step, different model parameter combinations are tested to ensure the basic model’s ability in extracting site-specific information from the input coda waves. One of the combinations is then used in the second step, in which the prediction target is surface to downhole ratio of Fourier Amplitude Spectra. For each of the 3 components EW, NS and UD, 100 identical networks are trained to each predict the desired ratio at a certain target frequency. Accuracy of test sample predictions confirms applicability of the proposed approach as well as its potential for future works on microtremor timeseries instead of coda waves.

scholarly journals COLLAPSE WARNING SYSTEM USING LSTM NEURAL NETWORKS FOR CONSTRUCTION DISASTER PREVENTION IN EXTREME WIND WEATHER

2021 ◽  
Vol 27 (4) ◽  
pp. 230-245
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Neural Networks ◽  
Prediction Models ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Warning System ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Strong Wind ◽  
Wind Force

Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk.

Improvement of Variant Adaptable LSTM Trained With Metaheuristic Algorithms for Healthcare Analysis

2021 ◽  
pp. 1031-1051
Author(s):  
Tarik A. Rashid ◽  
Mohammad K. Hassan ◽  
Mokhtar Mohammadi ◽  
Kym Fraser
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Data Sets ◽  
Patients With Diabetes

Recently, the population of the world has increased along with health problems. Diabetes mellitus disease as an example causes issues to the health of many patients globally. The task of this chapter is to develop a dynamic and intelligent decision support system for patients with different diseases, and it aims at examining machine-learning techniques supported by optimization techniques. Artificial neural networks have been used in healthcare for several decades. Most research works utilize multilayer layer perceptron (MLP) trained with back propagation (BP) learning algorithm to achieve diabetes mellitus classification. Nonetheless, MLP has some drawbacks, such as, convergence, which can be slow; local minima can affect the training process. It is hard to scale and cannot be used with time series data sets. To overcome these drawbacks, long short-term memory (LSTM) is suggested, which is a more advanced form of recurrent neural networks. In this chapter, adaptable LSTM trained with two optimizing algorithms instead of the back propagation learning algorithm is presented. The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA). Dataset is collected locally and another benchmark dataset is used as well. Finally, the datasets fed into adaptable models; LSTM with BBO (LSTMBBO) and LSTM with GA (LSTMGA) for classification purposes. The experimental and testing results are compared and they are promising. This system helps physicians and doctors to provide proper health treatment for patients with diabetes mellitus. Details of source code and implementation of our system can be obtained in the following link “https://github.com/hamakamal/LSTM.”

Sentiment Recognition in Customer Reviews Using Deep Learning

2018 ◽  
Vol 14 (2) ◽  
pp. 77-86 ◽  
Author(s):  
Vinay Kumar Jain ◽  
Shishir Kumar ◽  
Prabhat Mahanti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Customer Reviews ◽  
Analysis Question ◽  
Comparison Of The Results

Deep learning has become popular in all aspect related to human judgments. Most machine learning techniques work well which includes text classification, text sequence learning, sentiment analysis, question-answer engine, etc. This paper has been focused on two objectives, firstly is to study the applicability of deep neural networks strategies for extracting sentiment present in social media data and customer reviews with effective training solutions. The second objective is to design deep networks that can be trained with these weakly supervised strategies in order to predict meaningful inferences. This paper presents the concept and steps of using deep learning for extraction sentiments from customer reviews. The extraction pulls out the features from the customer reviews using deep learning popular methods including Convolution neural networks (CNN) and Long Short-Term Memory (LSTM) architectures. The comparison of the results with tradition text classification method such as Naive Bayes(NB) and Support Vector Machine(SVM) using two data sets IMDB reviews and Amazon customer reviews have been presented. This work mainly focused on investigating the merit of using deep models for sentiment analysis in customer reviews.

Site Effects and Microzonation in Acapulco

1998 ◽  
Vol 14 (1) ◽  
pp. 75-93 ◽  
Author(s):  
Francisco J. Chávez-García ◽  
Julio Cuenca
Keyword(s):  
Data Analysis ◽  
Seismic Risk ◽  
Site Effects ◽  
Pacific Coast ◽  
Strong Motion ◽  
Strong Motion Records ◽  
The Pacific ◽  
Dominant Period ◽  
Seismograph Network ◽  
Earthquake Data

The region around Acapulco, on the Pacific coast of Mexico, is subjected to large seismic risk. This paper presents a contribution to improve microzonation of this region. We investigated site effects using three basic sources of data: strong-motion records from all of the instruments that have operated within the area; weak-motion records obtained from the installation and operation of a temporal, digital, seismograph network; and measurements of microtremors at 35 sites. We compared and evaluated different techniques of data analysis. We show that very coherent results are obtained from different kinds of measurement, and that microtremor records are very useful to interpolate sparse earthquake data. We propose two maps that reflect the fundamental characteristics of site effects in the area: dominant period and maximum relative amplification. These maps may be used to improve current microzonation of Acapulco.

Improvement of Variant Adaptable LSTM Trained With Metaheuristic Algorithms for Healthcare Analysis

2019 ◽  
pp. 111-131 ◽  
Author(s):  
Tarik A. Rashid ◽  
Mohammad K. Hassan ◽  
Mokhtar Mohammadi ◽  
Kym Fraser
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Data Sets ◽  
Patients With Diabetes

Recently, the population of the world has increased along with health problems. Diabetes mellitus disease as an example causes issues to the health of many patients globally. The task of this chapter is to develop a dynamic and intelligent decision support system for patients with different diseases, and it aims at examining machine-learning techniques supported by optimization techniques. Artificial neural networks have been used in healthcare for several decades. Most research works utilize multilayer layer perceptron (MLP) trained with back propagation (BP) learning algorithm to achieve diabetes mellitus classification. Nonetheless, MLP has some drawbacks, such as, convergence, which can be slow; local minima can affect the training process. It is hard to scale and cannot be used with time series data sets. To overcome these drawbacks, long short-term memory (LSTM) is suggested, which is a more advanced form of recurrent neural networks. In this chapter, adaptable LSTM trained with two optimizing algorithms instead of the back propagation learning algorithm is presented. The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA). Dataset is collected locally and another benchmark dataset is used as well. Finally, the datasets fed into adaptable models; LSTM with BBO (LSTMBBO) and LSTM with GA (LSTMGA) for classification purposes. The experimental and testing results are compared and they are promising. This system helps physicians and doctors to provide proper health treatment for patients with diabetes mellitus. Details of source code and implementation of our system can be obtained in the following link “https://github.com/hamakamal/LSTM.”

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