A review study of predictive model blast vibration attenuation equation by using neural network as an evaluator

2011 ◽  
Vol 25 (1) ◽  
pp. 69-85 ◽  
Author(s):  
Sugeng Wahyudi ◽  
Hideki Shimada ◽  
Ganda Marihot Simangunsong ◽  
Takashi Sasaoka ◽  
Kikuo Matsui ◽  
...  
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Blast Vibration ◽  
Vibration Attenuation ◽  
Review Study

scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

scholarly journals The effect of Kurtosis on the accuracy of artificial neural network predictive model

2018 ◽  
Vol 204 ◽  
pp. 02018
Author(s):  
Aisyah Larasati ◽  
Anik Dwiastutik ◽  
Darin Ramadhanti ◽  
Aal Mahardika
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Predictive Models ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Misclassification Rates ◽  
Hidden Layer ◽  
Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

A new predictive model for the minimum strength requirement of steel moment frames using artificial neural network

2019 ◽  
Vol 116 ◽  
pp. 69-81 ◽  
Author(s):  
Amir Ahmad Hedayat ◽  
Ehsan Ahmadi Afzadi ◽  
Hossein Kalantaripour ◽  
Esmaeil Morshedi ◽  
Amin Iranpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Moment Frames ◽  
Steel Moment Frames ◽  
Artificial Neural

Predictive analytics of the technical condition of diesel locomotive systems using neural network predictive models

2021 ◽  
pp. 102-114
Author(s):  
Mikhail V. FEDOTOV ◽  
◽  
Vladimir V. GRACHEV ◽  
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Predictive Models ◽  
Short Term Memory ◽  
Predictive Analytics ◽  
Practical Importance ◽  
Technical Condition ◽  
Feedforward Neural Network ◽  
Diesel Locomotive ◽  
Short Term

Objective: Study of the possibility of carrying out predictive analysis of the technical condition of locomotive equipment using neural network predictive models enabling to plan the scope of equipment maintenance for routine types of maintenance and repair. Methods: A comparative assessment of the accuracy of forecasts made using a feedforward neural network and a recurrent network with an LSTM layer (Long Short-Term Memory) has been carried out. For training and test-ing of predictive models, we used the results of monitoring the parameters of the lubrication sys-tem of the 2TE116 (2ТЭ116) diesel locomotive by means of on-board diagnostics. Results: The aver-age interval for preventive inspections (TO-3) of locomotives in the existing locomotive mainte-nance system is 25–30 days, and therefore it is this interval that determines the minimum duration of the lead-in period, which the predictive model should provide. We have established that a mod-el based on a feedforward neural network provides sufficient accuracy only for short-term fore-casts with a lead period of no more than 1–3 days. With a further increase in the lead-in period, the error of the model res¬ponse increases to 10–15 %, which prevents it from being effectively used for solving practical problems associated with planning the operation of service locomotive depots. At the same time, the ave¬rage response error of the predictive model based on a recurrent net-work with an LSTM layer does not exceed 3,5–5 % over a 30-day lead-in period, so it can be used to plan the scope and timing of locomotive maintenance procedures. Practical importance: The possi-bility of using time-series analysis methods for predictive analytics of the technical condition of units and systems of a locomotive is shown. Predictive models based on recurrent neural networks with LSTM layers provide prediction accuracy and lead-in period sufficient for solving practical prob-lems that are associated with planning the scope and timing of locomotive maintenance.

Production Blast-Induced Vibrations in Longhole Open Stoping

2012 ◽  
pp. 160-170
Author(s):  
John Henning ◽  
Hani Mitri
Keyword(s):  
Mining Operation ◽  
Metal Mining ◽  
Blast Vibration ◽  
Zone Width ◽  
Blast Design ◽  
Design Practices ◽  
Powder Factor ◽  
Vibration Attenuation ◽  
Stope Design

This paper examines stope design approaches employed at a metal mining operation in Canada for extraction of transverse primary, transverse secondary, and longitudinal stopes. Variations in stope and slot design, blast design, and blast vibration attenuation are presented in detail. It is shown that the type of blasthole stoping technique employed varies according to stope sequence and ore zone width. Within this range of stopes, blasting design practices have been standardized in terms of drillhole diameter, powder factor, and the type and pattern of the explosives used.

scholarly journals A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

2020 ◽  
Vol 12 (10) ◽  
pp. 4001
Author(s):  
Sung-Sik Park ◽  
Peter D. Ogunjinmi ◽  
Seung-Wook Woo ◽  
Dong-Eun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Model Equation ◽  
Soil Layer ◽  
Unit Weight ◽  
Ann Model ◽  
Matrix Formulation ◽  
Input Parameters ◽  
Artificial Neural

Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.

The neural network predictive model for heat island intensity in Seoul

2016 ◽  
Vol 110 ◽  
pp. 353-361 ◽  
Author(s):  
Ye Young Lee ◽  
Jeong Tai Kim ◽  
Geun Young Yun
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Heat Island ◽  
The Neural Network
Sign in / Sign up

Export Citation Format

Share Document