Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

2009 ◽  
Vol 46 (8) ◽  
pp. 955-968 ◽  
Author(s):  
Yusuf Erzin ◽  
S. D. Gumaste ◽  
A. K. Gupta ◽  
D. N. Singh
Keyword(s):  
Hydraulic Conductivity ◽  
Absolute Error ◽  
Silty Sand ◽  
Coefficient Of Determination ◽  
Performance Indices ◽  
Fine Grained ◽  
Centrifuge Tests ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-à-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.

scholarly journals Predicting the Limit Void Ratios of Coarse-Grained Soils Using Artificial Neural Networks

2021 ◽  
Author(s):  
Yusuf Erzin ◽  
Yeşim Tuskan ◽  
Selin Erzin
Keyword(s):  
Void Ratio ◽  
Back Propagation ◽  
Absolute Error ◽  
Coarse Grained ◽  
Back Propagation Algorithm ◽  
Performance Indices ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values

Abstract In this study, the prediction performance of the artificial neural network (ANN) and multiple regression (MR) models in predicting the limit void ratios of coarse-grained soils was investigated and compared. The data available in the literature were collected and used to construct both two distinct ANN-1 and ANN-2 models and two distinct MR-1 and MR-2 models: ANN-1 and MR-1 for the prediction of minimum void ratio (emin) and ANN-2 and MR-2 for the prediction of maximum void ratio (emax) of coarse-grained soils. Two basic soil graining properties such as coefficient of uniformity (Cu) and mean grain size (D50) are utilized in the simulation of the feed forward ANN models with back propagation algorithm and the MR models.The emax and emin values predicted from both ANN and MR models were compared with the experimental values taken from the literature. Moreover, five performance indices i.e. the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were calculated to examine the prediction capacity of the ANN and MR models developed in this study. The performance indices calculated indicated that both ANN models showed better performance than both MR models. It has been demonstrated that both ANN models can be used satisfactorily to predict limit void ratio values of coarse-grained soils as a rapid inexpensive substitute for laboratory techniques.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

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 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.

Estimating bearing capacity of shallow foundations by artificial neural networks

2017 ◽  
Vol 3 (4) ◽  
pp. 151
Author(s):  
Mustafa Aytekin
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Large Set ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Capacity Data

In this study, the Artificial Neural Network, ANN is applied to data extracted from a large set of random data created by using Terzaghi and Meyerhof formulae. By using MS Excel, 3750 sets of data for Terzaghi's equation, 4000 for Meyerhof's equation were generated. A simulated ANN was trained on a subset of bearing capacity data, and the performance was tested on the remaining data. The performances of the ANN models were compared to Terzaghi and Meyerhof results. ANN models were as accurate as the other techniques in estimating the ultimate bearing capacity. The models estimated the ultimate bearing capacity with an average error of around 1% of the value obtained from Terzaghi and Meyerhof equations, and the coefficient of determination (r2) was almost equal to 1. Their sensitivity and specificity is dependent on the function and the algorithm used in the training process. Validation subset is crucial in preventing the over-fitting of the ANN models to the training data. ANN models are potentially useful technique for estimating the bearing capacity of the soil. Large training data sets are needed to improve the performance of data-derived algorithms, in particular ANN models.

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 Modeling yield and backscatter using satellite derived biophysical variables of rice crop based on Artificial Neural Networks

2021 ◽  
Vol 22 (1) ◽  
pp. 41-47
Author(s):  
MAHESH PALAKURU ◽  
SIRISHA ADAMALA ◽  
HARISH BABU BACHINA
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mean Squared Error ◽  
Rice Yield ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Yield Model ◽  
Area Index ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, ‘observed rice yield (ton acre-1)’ and ‘remotely sensed backscatter’are modelled using artificial neural network (ANN) and multiple linear regression (MLR) methods for East and West Godavari districts of Andhra Pradesh in India. The biophysical variables viz. backscatter (bs), normalized difference vegetation index (NDVI), Chlorophyll (chfl), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), canopy water content (CWC), and fraction of vegetation cover (Fcover) were derived from Scatterometer Satellite-1 (SCATSAT-1), Moderate Imaging Spectrometer (MODIS) and Sentinel-2 satellite data.Inputs selected are bs, NDVI, chfl, FAPAR, LAI, CWC, and Fcover for rice yield model, whereas NDVI, chfl, FAPAR, LAI, CWC, and Fcover are inputs for backscatter models. The performance of ANN and MLR models was evaluated using three indices such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results concluded that the ANN models achieved R2 of 0.908 and 0.884 which are 42.73% and 28.85% higher than that of the MLR method for rice yield and backscatter, respectively.

scholarly journals Estimating Inside Air Temperature of a Glasshouse Using Statistical Models

2017 ◽  
Vol 12 (1) ◽  
pp. 01-05 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Konstantinos Chronopoulos
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Predictor Variable ◽  
Absolute Error ◽  
Meteorological Station ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Better Than

The efficiency of applying linear regression (LR) and artificial neural network (ANN) models to estimate inside air temperature (T) of a glasshouse (37o48΄20΄΄N, 23o57΄48΄΄E), Lavreotiki, was investigated in the present work. The T data from an urban meteorological station (MS) at 37058΄55΄΄N, 23o32΄14΄΄E, Athens, Attica, Greece, about 30 Km away from the glasshouse, were used as predictor variable, taking into account the actual time of measurement (ATM) and two hours earlier (ATM-2), depending on the case. Air temperature data were monitored in each examined area (glasshouse and MS) for four successive months (July-October) and averages on a two-hour basis were used for the aforementioned estimation. Results showed that ANN were better than LR models, considering their better performance as shown in the scatterplots of the distribution of observed versus estimated inside T data of the glasshouse, in terms of both higher coefficient of determination (R2) and lower mean absolute error (MAE). The best ANN model (highest R2 and lowest MAE) was achieved by using as predictor variables the T at ATM and the T at ATM-2 from MS. The findings of our study may be a first step towards the estimation of inside T of a glasshouse in Greece, from outside T data of a remote MS. Thus, the operation of the glasshouse could be improved noticeably.

scholarly journals Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks

2017 ◽  
Vol 14 (2) ◽  
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Narx Models ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive ◽  
Yearly Data

In this study, three Artificial Neural Network (ANN) models (Feedforward network, Elman, and Nonlinear Autoregressive Exogenous (NARX)) were used to predict hourly solar radiation in Amman, Jordan. The three models were constructed and tested by using MATLAB software. Meteorological data for the years from 2000 to 2010 were used to train the ANN while the yearly data of 2011 was used to test it. It was found that ANN technique may be used to estimate the hourly solar radiation with an excellent accuracy, and the coefficient of determination of Elman, feedforward and NARX models were found to be 0.97353, 0.97376, and 0.99017, respectively. The obtained results showed that NARX model has the best ability to predict the required solar data, while Elman and feedforward models have the lowest ability to predict it.

Sign in / Sign up

Export Citation Format

Share Document