Using Artificial Neural Network to Predict Blast-Induced Ground Vibration

2012 ◽  
Vol 170-173 ◽  
pp. 1013-1016
Author(s):  
Fu Qiang Gao ◽  
Xiao Qiang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Monitoring Point ◽  
Blasting Operation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of peak particle velocity (PPV) is very complicated due to the number of influencing parameters affecting seism wave propagation. In this paper, artificial neural network (ANN) is implemented to develop a model to predict PPV in a blasting operation. Based on the measured parameters of maximum explosive charge used per delay and distance between blast face to monitoring point, a three-layer ANN was found to be optimum with architecture 2-5-1. Through the analysis of coefficient of determination (CoD) and mean absolute error (MAE) between monitored and predicted values of PPV, it indicates that the forecast data by the ANN model is close to the actua1 values.

scholarly journals Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Thai Binh Pham ◽  
Sushant K. Singh ◽  
Hai-Bang Ly
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Coefficient Of Consolidation ◽  
Experimental Approaches ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Accuracy Performance

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

scholarly journals Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation

2020 ◽  
Vol 42 (4) ◽  
Author(s):  
Thai Binh Pham ◽  
Sushant K. Singh ◽  
Hai-Bang Ly
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Coefficient Of Consolidation ◽  
Experimental Approaches ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Accuracy Performance

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

scholarly journals The Use of an Artificial Neural Network for Predicting the Gloss of Thermally Densified Wood Veneers

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Şükrü Özşahin ◽  
Hilal Singer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Densified Wood ◽  
Percentage Error ◽  
Angle Of Incidence ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

scholarly journals An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation – Ahdeb Oil Field

2020 ◽  
pp. 1051-1062
Author(s):  
Zaher JabbarAttwan AL Zirej ◽  
Hassan Abdul Hadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Field ◽  
Coefficient Of Determination ◽  
Drilling Mud ◽  
Ann Model ◽  
Rate Of Penetration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The main objective of this study is to develop a rate of penetration (ROP) model for Khasib formation in Ahdab oil field and determine the drilling parameters controlling the prediction of ROP values by using artificial neural network (ANN).      An Interactive Petrophysical software was used to convert the raw dataset of transit time (LAS Readings) from parts of meter-to-meter reading with depth. The IBM SPSS statistics software version 22 was used to create an interconnection between the drilling variables and the rate of penetration, detection of outliers of input parameters, and regression modeling. While a JMP Version 11 software from SAS Institute Inc. was used for artificial neural modeling.      The proposed artificial neural network method depends on obtaining the input data from drilling mud logging data and wireline logging data. The data then analyzes it to create an interconnection between the drilling variables and the rate of penetration.      The proposed ANN model consists of an input layer, hidden layer and outputs layer, while it applies the tangent function (TanH) as a learning and training algorithm in the hidden layer. Finally, the predicted values of ROP are compared with the measured values. The proposed ANN model is more efficient than the multiple regression analysis in predicting ROP. The obtained coefficient of determination (R2) values using the ANN technique are 0.93 and 0.91 for training and validation sets, respectively. This study presents a new model for predicting ROP values in comparison with other conventional drilling measurements.

scholarly journals Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

2020 ◽  
Vol 58 (1) ◽  
pp. 25-38
Author(s):  
Sandi Baressi Šegota ◽  
Daniel Štifanić ◽  
Kazuhiro Ohkura ◽  
Zlatan Car
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Activation Function ◽  
Propulsion System ◽  
Coefficient Of Determination ◽  
Decay State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Torque Values

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99.

Failure pressure prediction of pipeline with single corrosion defect using artificial neural network

2021 ◽  
pp. 166-171
Author(s):  
К. Т. Чин ◽  
Т. Арумугам ◽  
С. Каруппанан ◽  
М. Овинис
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Failure Pressure ◽  
Corrosion Defect ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Описываются разработка и применение искусственной нейронной сети (ИНС) для прогнозирования предельного давления трубопровода с точечным коррозионным дефектом, подверженного воздействию только внутреннего давления. Модель ИНС разработана на основе данных, полученных по результатам множественных полномасштабных испытаний на разрыв труб API 5L (класс от X42 до X100). Качество работы модели ИНС проверено в сравнении с данными для обучения, получен коэффициент детерминации R = 0,99. Модель дополнительно протестирована с учетом данных о предельном давлении корродированных труб API 5L X52 и X80. Установлено, что разработанная модель ИНС позволяет прогнозировать предельное давление с приемлемой погрешностью. С использованием данной модели проведена оценка влияния длины и глубины коррозионных дефектов на предельное давление. Выявлено, что глубина коррозии является более значимым фактором разрушения корродированного трубопровода. This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination ( R ) of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error. Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.

Development of an Artificial Neural Network-Based Procedure for the Verification of Traffic Speed Deflectometer Measurements

2021 ◽  
pp. 036119812110057
Author(s):  
Hossam Abohamer ◽  
Mostafa A. Elseifi ◽  
Zia U. A. Zihan ◽  
Zhong Wu ◽  
Nathan Kebede ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The United States ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Measuring Device ◽  
Spatial Coverage ◽  
Artificial Neural ◽  
Traffic Speed ◽  
Artificial Neural Network Ann

Since the 1980s, the falling weight deflectometer (FWD) has been the primary deflection-measuring device in the United States to evaluate the structural conditions of in-service pavements. However, the stop and go nature of the FWD limits its application at the network level. In the early 2000s, the traffic speed deflectometer (TSD) was introduced as an alternate deflection-measuring device for network-level applications. TSD collects deflection measurements while traveling at traffic speed, which provides improved spatial coverage and no traffic disturbance. The verification of TSD measurements is of great interest as many agencies move toward widespread implementation. This study aims at developing a reliable and straightforward procedure for the verification of TSD measurements using limited FWD measured deflection measurements. The verification procedure employs a trained artificial neural network (ANN) model to shift TSD deflections to their corresponding FWD deflections. The ANN model was trained and verified based on FWD and TSD measurements from two deflection-testing programs. The developed model accurately predicted FWD measurements with a coefficient of determination (R2) of 0.994. The suitability of the proposed verification procedure was evaluated using statistical and engineering-based measures and showed acceptable accuracy. Results also validated that the proposed method could be used to verify TSD measurements before its use for conducting deflection measurements at the network level.

Prediction of Vibration Velocity due to Blasting Using Artificial Neural Network

2012 ◽  
Vol 170-173 ◽  
pp. 3063-3067
Author(s):  
Fu Qiang Gao ◽  
Ai Jun Hou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Velocity ◽  
Structural Damage ◽  
Mining Industry ◽  
Coefficient Of Determination ◽  
Vibration Velocity ◽  
Monitoring Point ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Vibration induced by blasting is one of the most hazardous events in the mining industry and may cause structural damage in country areas. Therefore, mitigating the possible hazard and predicting the vibration velocity is important. In this paper, an attempt has been made to predict the peak particle velocity using artificial neural network (ANN) by taking into consideration of maximum explosive charge used per delay and distance between blast face to monitoring point. To achieve the classic framework of this approach, the prediction results by artificial neural network were compared with measured values by coefficient of determination (CoD) and sum of squares due to error (SSE).

scholarly journals Modeling of Evapotranspiration (ETo) in a Medium Urban Park within a Megacity by Using Artificial Neural Network (ANN) Model

2021 ◽  
Author(s):  
Hayder Algretawee ◽  
Ghofran Alshama
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrological Cycle ◽  
Reference Evapotranspiration ◽  
Coefficient Of Determination ◽  
Medium Size ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Main Component

Evapotranspiration (ETo) is considered a main component of the hydrological cycle. This study was carried out on a medium-size park within a highly urbanized area, close to the center of Melbourne city. The purpose of the study is to calculate the reference evapotranspiration (ETo), particularly at a specified spot in a corner of the park. The hand-held device used to collect data gave consistent results and reduced the need for assumptions. The Penman-Montieth equation was used to calculate the reserved ETo. To build an ETo model, Artificial Neural Network (ANN) was adopted to predict ETo. Three models were built to select the best model, based on the least Root Mean Square Error (RMSE) and the highest coefficient of determination (R2). Results showed a contrast between the observed and predicted magnitudes of ETo. Both of the observed and predicted magnitudes for ETo are higher than most recent studies. Data from the specified location shows a difference in ETo magnitudes relative to the fixed meteorological stations. This study supports that climate change causes increasing magnitudes of reference evapotranspiration ETo.

scholarly journals Failure pressure prediction of pipeline with single corrosion defect using artificial neural network

2020 ◽  
Vol 4 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Kiu Toh Chin ◽  
◽  
Thibankumar Arumugam ◽  
Saravanan Karuppanan ◽  
Mark Ovinis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Failure Pressure ◽  
Corrosion Defect ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of 71 sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error (within 15%). Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.

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