scholarly journals Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay

2021 ◽  
Vol 11 (20) ◽  
pp. 9760
Author(s):  
Zhongkai Huang ◽  
Dongmei Zhang ◽  
Dongming Zhang
Keyword(s):  
Pressure Coefficient ◽  
Earth Pressure ◽  
Numerical Data ◽  
Absolute Error ◽  
Ann Model ◽  
Soil Stiffness ◽  
Wall Deflection ◽  
Proposed Model ◽  
Ann Models ◽  
Artificial Neural Network Ann

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.

scholarly journals Neural network application in forecasting maximum wall deflection in homogenous clay

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Khalid R. Aljanabi ◽  
Osamah M. AL-Azzawi
Keyword(s):  
Neural Network ◽  
Earth Pressure ◽  
Width Ratio ◽  
Maximum Deflection ◽  
Ann Model ◽  
Relative Significance ◽  
Wall Deflection ◽  
Ann Models ◽  
Network Application ◽  
Input Variables

AbstractAn attempt was carried out by using a neural network to predict the maximum deflection and its position caused by braced excavation in homogeneous clay. Six input variables, including excavation depth, Ratio of EI wall/EI of brace, the vertical distance between bracing, Length to width ratio of an excavation, shear strength, and the coefficient of lateral earth pressure, were adopted. Two models were developed, one is to estimate the maximum deflection and the other one to estimate the position of maximum deflection. The ANN models were developed and verified using a database of (169) cases of actual measured and presumptive cases using the analysis with the Finite element of maximum deflection. A sensitivity analysis was accomplished, to examine the relative significance of the parameters that influence the maximum deflection of the wall and its position; it indicates that the Ratio of EI wall/EI of brace has the most significant effect on the maximum wall deflection, while the properties of the soil have the most considerable effects on the position. The results show that the ANN can reasonably forecast the magnitude of the maximum deflection of the wall, as well as its position. Design charts are developed based on the ANN model.

scholarly journals A NEW HYBRID ANN MODEL FOR EVALUATING THE EFFICIENCY OF Π-TYPE FLOATING BREAKWATER

2017 ◽  
pp. 25 ◽  
Author(s):  
Hadi Ghasemi ◽  
Morteza Kolahdoozan ◽  
Enrique Pena ◽  
Javier Ferreras ◽  
Andrés Figuero
Keyword(s):  
Experimental Data ◽  
Wave Energy ◽  
Model Performance ◽  
Accurate Estimate ◽  
Ann Model ◽  
Proposed Model ◽  
Wave Basin ◽  
Short Period ◽  
Ann Models ◽  
Artificial Neural Network Ann

Floating breakwaters (FBs) have been specially regarded in recent years as a means to protect small harbors and marine structures against affection of short period waves. FBs have different types in terms of geometric shapes; one of the most common of which is the π-type FB. Generally, FBs are designed to reduce wave energy. The parameter used to evaluate the efficiency of FBs in the wave energy reduction is the wave transmission coefficient (Kt). Thus, accurate estimate of Kt is an important aspect in FBs design. In the present study, new hybrid artificial neural network (ANN) models are developed for predicting Kt of π-type FBs. Actually, a new algorithm that combines particle swarm optimization (PSO) and Levenberg-Marquardt (LM) is used for learning ANN models. These models are developed by the use of experimental data sets obtained from the Kt of π-type FBs using a wave basin of the University of a Coruña, Spain. A proposed model performance was evaluated and results show that this model can be successfully applied for the prediction of the Kt. Also, results of proposed model show that the efficiency of this model is improved in compare with the introduced formulas cited in the literature. After assuring the acceptability of the prediction results, this model as one of an efficient tool was used for extending the experimental data and selection of the optimal design of π-type FBs in terms of the geometric characteristics.

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 Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds

2020 ◽  
Vol 51 (3) ◽  
pp. 423-442
Author(s):  
Naser Dehghanian ◽  
S. Saeid Mousavi Nadoushani ◽  
Bahram Saghafian ◽  
Morteza Rayati Damavandi
Keyword(s):  
Flood Control ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ann Model ◽  
Runoff Generation ◽  
Source Areas ◽  
Walnut Gulch ◽  
Artificial Neural Network Ann ◽  
Semi Arid ◽  
Flood Source

Abstract An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds.

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

2022 ◽  
pp. 1287-1300
Author(s):  
Balaji Prabhu B. V. ◽  
M. Dakshayini
Keyword(s):  
Neural Network ◽  
Crop Production ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Ann Model ◽  
Demand Prediction ◽  
Computational Performance ◽  
Societal Needs ◽  
Ann Models ◽  
Artificial Neural Network Ann

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

2020 ◽  
Vol 12 (4) ◽  
pp. 35-47
Author(s):  
Balaji Prabhu B. V. ◽  
M. Dakshayini
Keyword(s):  
Neural Network ◽  
Crop Production ◽  
Demand Forecasting ◽  
Ann Model ◽  
Demand Prediction ◽  
Computational Performance ◽  
Societal Needs ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Agricultural Food

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

GA-ANN Forecast Model for Typhoon Gale in South China Sea Based on MDS

2014 ◽  
Vol 556-562 ◽  
pp. 5618-5622 ◽  
Author(s):  
Kai Ping Lin ◽  
Yan Dong ◽  
Xiao Yan Huang
Keyword(s):  
South China Sea ◽  
South China ◽  
Forecast Model ◽  
Absolute Error ◽  
Reanalysis Data ◽  
Ann Model ◽  
Prediction Ability ◽  
China Sea ◽  
Ann Models ◽  
Grid Points

Based on 33-year typhoon information of South China Sea (SCS) in 1980-2012 and NCEP/NCAR reanalysis data, taking Climatology and Persistence (CLIPER) and earlier physical quantities predictors selected by Stepwise Regression (SWR) and Multidimensional Scaling (MDS) methods as model inputs, the Genetic Algorithm-Artificial Neural Network (GA-ANN) forecast model was built for typhoon gale. The forecast verification results for independent samples in MDS-GA-ANN model show that mean absolute error of 24h forecast for wind velocities at 36 grid points around typhoon centers from July to September is 1.6m/s. Using the same samples, the prediction results of MDS-GA-ANN models for independent samples were compared with that of traditional SWR models. Taking July as example, prediction abilities for 29 MDS-GA-ANN models (81%) among 36 grid points around typhoon centers are superior to that of SWR models; only 2 grid points of MDS-GA-ANN models are worse than that of SWR models (6%). Therefore, prediction ability for most of 36 grid points using MDS-GA-ANN models is superior to that of SWR models and can meet business requirements of meteorological stations at present.

Evaluation and ANN-Based Prediction on Functional Parameters of Surface Roughness in Precision Grinding of Cast Iron

2014 ◽  
Vol 1017 ◽  
pp. 166-171
Author(s):  
Bin Zhao ◽  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Surface Roughness ◽  
Cast Iron ◽  
Three Dimensional ◽  
Ann Model ◽  
Linear Speed ◽  
Functional Parameters ◽  
Grinding Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Dimensional Surface

Three-dimensional surface roughness parameters are widely applied to characterize frictional and lubricating properties, corrosion resistance, fatigue strength of surfaces. Among them, the functional parameters of surface roughness, such as Sbi, Sci, and Svi, are used to evaluate bearing and fluid retention properties of surfaces. In this study, the effects of grinding parameters, including wheel linear speed (Vs), workpiece linear speed (Vw), grinding depth (ap), longitudinal feed rate (fa), and dressing rate (F), on functional parameters were studied in grinding of cast iron. An artificial neural network (ANN) model was developed for predicting the functional parameters of three-dimensional surface roughness. The inputs of the ANN models were grinding parameters (Vs, Vw, ap, fa, F), and the output parameters of the models were functional parameters of surface roughness (Sbi, Sci, Svi). With small errors (e.g MSE = 0.09%, 0.61%, and 0.0014%. ), the ANN-based models are considered sufficiently accurate to predict functional parameters of surface roughness in grinding of cast iron.

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.

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