Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R ² = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model.     

Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

This study compares the daily potato crop evapotranspiration (ET_C) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ET_C was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ET_C at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R ² > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.     

Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling

In this paper, an artificial neural network (ANN) model is proposed to predict the first lactation 305-day milk yield (FLMY305) using partial lactation records pertaining to the Karan Fries (KF) crossbred dairy cattle. A scientifically determined optimum dataset of representative breeding traits of the cattle is used to develop the model.Several training algorithms, viz., (i) gradient descent algorithm with adaptive learning rate; (ii) Fletcher–Reeves conjugate gradient algorithm; (iii) Polak–Ribiére conjugate gradient algorithm; (iv) Powell–Beale conjugate gradient algorithm; (v) Quasi-Newton algorithm with Broyden, Fletcher, Goldfarb, and Shanno (BFGS) update; and (vi) Levenberg–Marquardt algorithm with Bayesian regularization; along with various network architectural parameters, i.e., data partitioning strategy, initial synaptic weights, number of hidden layers, number of neurons in each hidden layer, activation functions, regularization factor, etc., are experimentally investigated to arrive at the best model for predicting the FLMY305.Also, a multiple linear regression (MLR) model is developed for the milk-yield prediction. The performances of ANN and MLR models are compared to assess the relative prediction capability of the former model.It emerges from this study that the performance of ANN model seems to be slightly superior to that of the conventional regression model. Hence, it is recommended that the ANNs can potentially be used as an alternative technique to predict FLMY305 in the KF cattle.     

An artificial neural-network model for impact properties in X70 pipeline steels

An artificial neural-network (ANN) model has been developed for the analysis and simulation of the correlation between the mechanical properties and composition and thermomechanical treatment parameters of high strength, low alloy steels. The input parameters of the model consist of alloy compositions (C, Si, Mn, P, S, Cu, Ni, Cr, Mo, Ti, V, Nb, Ca, Al, B) and tensile test results (yield strength, ultimate tensile strength, percentage elongation). The outputs of the ANN model include impact energy (?10 °C). The model can be used to calculate the properties of low alloy steels as a function of alloy composition and thermomechanical treatment variables. The current study achieved a good performance of the ANN model, and the results are in agreement with experimental knowledge.     

Backbreak prediction in the Chadormalu iron mine using artificial neural network

Backbreak is one of the unfavorable blasting results, which can be defined as the unwanted rock breakage behind the last row of blast holes. Blast pattern parameters, like stemming, burden, delay timing, stiffness ratio (bench height/burden) and rock mass conditions (e.g., geo-mechanical properties and joints), are effective in backbreak intensity. Till date, with the exception of some qualitative guidelines, no specific method has been developed for predicting the phenomenon. In this paper, an effort has been made to apply artificial neural networks (ANNs) for predicting backbreak in the blasting operation of the Chadormalu iron mine (Iran). Number of ANN models with different hidden layers and neurons were tried, and it was found that a network with architecture 10-7-7-1 is the optimum model. A comparative study also approved the superiority of the ANN modeling over the conventional regression analysis. Mean square error (MSE), variance account for (VAF) and coefficient of determination (R ²) between measured and predicted backbreak for the ANN model were calculated and found 89.46 %, 0.714 and 90.02 %, respectively. Also, for the regression model, MSE, VAF and R ² were computed and found 66.93 %, 1.46 and 68.10 %, respectively. Sensitivity analysis was also carried out to find out the influence of each input parameter on backbreak results, and it was revealed that burden is the most influencing parameter on the backbreak, whereas water content is the least effective parameter in this regard.     

Determination of color changes of inks on the uncoated paper with the offset printing during drying using artificial neural networks

This study attempts to determinate color changes based on time in inks applied on the surface of wood-free uncoated paper with offset printing during drying. This study consists of two main cases: (1) Experimental analysis: By preparing a test page according to the 12647-2 principle with an offset printing system, test prints were applied to 120 g/m² wood-free uncoated paper using an ECI 2002 CMYK test chart. Each press was measured being subject to process every 15 min in the first 2 h, then hour by hour between 2 and 12 h, then 4–4 h between 12 and 24 h, and then 6–6 h between 24 and 48 h. CIELAB and reflectance values between 380 and 720 nm of the target, 1,485 colors of the test chart were obtained. To see the drying and color changes of the ink on paper, changes were determined by printing on the paper and applying artificial neural network (ANN) to spectrophotometer data at the stated time intervals. (2) Empirical analysis: The use of the ANN has been proposed as numerical approach to get of empirical equations of color changes in inks applied on the surface of wood-free uncoated paper with offset printing during drying. Based on the outputs of the study, ANN model can be used to estimate the effects of digital proofing systems used in color management on print quality with high confidence with the use of the acquired equations without experimental study. In the study, as colors are defined in terms of wave length, in case, all wave lengths are taken into consideration, certain wave length changes have been taken into consideration.     

Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the ultimate tensile strength of the API X70 steels after thermomechanical treatment. The independent variables in the model are chemical compositions (carbon equivalent), based upon the International Institute of Welding equation (CE_IIW), the carbon equivalent, based upon the chemical portion of the Ito-Bessyo carbon equivalent equation (CE_Pcm), the sum of the niobium, vanadium and titanium concentrations (VTiNb), the sum of the niobium and vanadium concentrations (NbV), the sum of the chromium, molybdenum, nickel and copper concentrations (CrMoNiCu), Charpy impact energy at ?10 °C (CVN) and yield strength at 0.005 offset (YS). For purpose of constructing these models, 104 different data were gathered from the experimental results. The data used in the ANN model is arranged in a format of seven input parameters that cover the chemical compositions, yield stress and Charpy impact energy, and output parameter which is ultimate tensile strength. In this model, the training, validation and testing results in the ANN have shown strong potential for prediction of relations between chemical compositions and mechanical properties of API X70 steels.     

Comparison of individual and combined ANN models for prediction of air and dew point temperature

Predicted air and dew point temperatures can be valuable in decision making in many areas including protecting crops from damage, avoiding heat stress on animals and humans, and in planning related to energy management. Current web-based artificial neural network (ANN) models on the Automated Environment Monitoring Network (AEMN) in Georgia predict hourly air and dew point temperature for twelve prediction horizons, using 24 models. The observed air temperature may approach the observed dew point temperature, but never goes below it. Current web based ANN models have prediction errors which, when the air and dew point temperatures are close, may cause air temperature to be predicted below the dew point temperature. Herein this error is referred to as a prediction anomaly. The goal of this research was to improve the prediction accuracy of existing air and dew point temperature ANN models by combining the two weather variables into a single ANN model for each prediction horizon. The objectives of this study were to reduce the mean absolute error (MAE) of prediction and to reduce the number of prediction anomalies. The combined models produced a reduction in the air temperature MAE for ten of twelve prediction horizons with an average reduction in MAE of 1.93 %. The combined models produced a reduction in the dew point temperature MAE for only six of twelve prediction horizons with essentially no average decrease in MAE. However, the combined models showed a marked reduction in prediction anomalies for all twelve prediction horizons with an average reduction of 34.1 %. The reduction in prediction anomalies ranged from 4.6 % at the one-hour horizon to 60.5 % at the eleven-hour horizon.     

Regional-scale rice-yield estimation using stacked auto-encoder with climatic and MODIS data: a case study of South Korea

In South Korea, rice is the most important grain crop that it is crucial to develop yield estimation model for supporting sustainable agriculture and national food security. The main objectives of this paper are (1) to propose a Deep Learning (DL) algorithm, the Stacked Sparse Auto-encoder (SSAE), for rice-yield estimation using climatic and Moderate Resolution Imaging Spectroradiometer (MODIS) data; (2) to choose scenarios showing the best combined performance in terms of length of crop season (before and after harvest) and aggregation periods (7, 10, 15, and 30 days) of climatic data; and (3) to analyse the results in both the temporal and spatial perspectives. In this procedure, the SSAE model was built around the study objectives and compared with the artificial neural network (ANN) model for evaluation of its performance. According to the results, the combined 15-day-aggregated climatic data (between June and August) and MODIS data were selected as the optimal feature set for rice-yield estimation in the study area, Jeolla-do; the SSAE model outperformed ANN, showing root mean square error (RMSE) and RMSE% of 33.09 kg(10a)^?1 (5.21 kg(10a)^?1lower than ANN) and 6.89% (1.14% lower than ANN). Throughout the experiments, the rice-yield-estimation potentiality of a DL algorithm, namely the SSAE, was substantiated.     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

Inter-relationship between preparation methods,nickel loading,characteristics and performance in the reforming of crude ethanol over Ni/Al2O3 catalysts: A neural network approach

Artificial neural network (ANN) approach was used to design an optimum Ni/Al₂O₃ catalyst for the production of hydrogen by the catalytic reforming of crude ethanol based on determining the inter-relationships between catalyst-preparation methods, nickel loading, catalyst characteristics and catalyst performance. ANN could predict hydrogen production performance of various Ni/Al₂O₃ catalysts of various elemental compositions and methods of preparation in the production of hydrogen by the catalytic reforming of crude ethanol in terms of crude-ethanol conversion, hydrogen selectivity and hydrogen yield. Specifically on catalyst design, ANN was used to determine the optimum catalyst conditions for obtaining maximum hydrogen production performance of a Ni/Al₂O₃ catalyst for the production of hydrogen by the catalytic reforming of crude ethanol. The optimal hydrogen yield was 4.4 mol %, and the associated crude-ethanol conversion and H₂ selectivity for the optimal hydrogen yield were 79.6 and 91.4 mol%, respectively. The optimal catalyst was the one prepared by the coprecipitation method with the optimal nickel loading of 12.4 wt% and an optimal aluminum composition of 42.5 wt%.     

ANN modeling of the bremsstrahlung photon flux in tantalum target

Bremsstrahlung photons produced by 15 MeV electron beam are simulated using the Monte Carlo code of FLUKA. Tantalum foils have been chosen as a target material in the simulation, and the obtained photon spectrum has been analyzed with artificial neural network (ANN) technique. In the training ANN model, the thicknesses and energy values of bremsstrahlung photons for the Ta target have been used as input. In this study, we observed that the trained ANN model is consistent with simulation results.     

Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model

The paper presents an integrated model of artificial neural networks (ANNs) and non-dominated sorting genetic algorithm (NSGAII) for prediction and optimization of quality characteristics during pulsed Nd:YAG laser cutting of aluminium alloy. A full factorial experiment has been conducted where cutting speed, pulse energy and pulse width are considered as controllable input parameters with surface roughness and material removal rate as output to generate the dataset for the model. In ANN–NSGAII model, back propagation ANN trained with Bayesian regularization algorithm is used for prediction and computation of fitness value during NSGAII optimization. NSGAII generates complete set of optimal solution with pareto-optimal front for outputs. Prediction accuracy of ANN module is indicated by around 1.5 % low mean absolute % error. Experimental validation of optimized output results less than 1 % error only. Characterization of the process parameters in pareto-optimal region has been explained in detail. Significance of controllable parameters of laser on outputs is also discussed.     

Mapping urban land surface temperature using remote sensing techniques and artificial neural network modelling

The objective of the present study is to monitor and predict the changes in land surface temperature (LST) in the North of Jordan during the Period 2000 to 2016. Due to political instability in the nearby countries Syria and Iraq, Jordan has witnessed increased influxes of refugees, starting from the year 2003, which has been led to the urban expansion in the area that reflected on the climatic conditions and affected the LST values. Satellite images were used for providing LST, the acquired images represented two seasons of each year, namely summer and winter. Simulation and prediction of LST values for the next 10 years were carried out using nonlinear autoregressive exogenous (NARX) artificial neural network (ANN) model. The inputs to the model consist of meteorological data collected from eight stations in the study area, population, and land use and land cover (LULC). In fact, LULC was expressed in terms of normalized difference building index (NDBI) and normalized difference vegetation index (NDVI) that were obtained from satellite images. The model showed a high correlation between these parameters and the values of simulated LST, where the correlation coefficient for the training set, validation set, testing set and for the entire data ranged from 0.91 to 0.92. Based on the predicted LST values, LST maps for the next 10 years were developed and compared with the present actual LST maps for the year 2016. The comparison has shown an average increase of 1.1 ^°C in the average LST values, which is considered a significant increase compared with previous studies.     

Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks

Design of rectangular concrete-filled steel tubular (CFT) columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network (ANN) models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network model1 (ANN1) and artificial neural network model2 (ANN2) with a 20-neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs: the length (D) and width (B) of cross section, the thickness of steel (t), the yield strength of steel (f _y), the cylinder strength of concrete (f′_c). ANN2 has ten inputs: D, B, t, f _y, f′_c, the length to width ratio (D/B), the length to thickness ratio (D/t), the width to thickness ratio (B/t), restraint coefficient (ξ), the steel ratio (α). The axial bearing capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.     

Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks

This study presents forecast of highway casualties in Turkey using nonlinear multiple regression (NLMR) and artificial neural network (ANN) approaches. Also, the effect of railway development on highway safety using ANN models was evaluated. Two separate NLMR and ANN models for forecasting the number of accidents (A) and injuries (I) were developed using 27 years of historical data (1980–2006). The first 23 years data were used for training, while the remaining data were utilized for testing. The model parameters include gross national product per capita (GNP-C), numbers of vehicles per thousand people (V-TP), and percentage of highways, railways, and airways usages (TSUP-H, TSUP-R, and TSUP-A, respectively). In the ANN models development, the sigmoid and linear activation functions were employed with feed-forward back propagation algorithm. The performances of the developed NLMR and ANN models were evaluated by means of error measurements including mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R ²). ANN models were used for future estimates because NLMR models produced unreasonably decreasing projections. The number of road accidents and as well as injuries was forecasted until 2020 via different possible scenarios based on (1) taking TSUPs at their current trends with no change in the national transport policy at present, and (2) shifting passenger traffic from highway to railway at given percentages but leaving airway traffic with its current trend. The model results indicate that shifting passenger traffic from the highway system to railway system resulted in a significant decrease on highway casualties in Turkey.     

A genetic algorithm to refine input data selection for air temperature prediction using artificial neural networks

The accurate prediction of air temperature is important in many areas of decision-making including agricultural management, transportation and energy management. Previous research has focused on the development of artificial neural network (ANN) models to predict air temperature from one to twelve hours in advance. The inputs to these models included a constant duration of prior data with a fixed resolution for all environmental variables for all prediction horizons. The overall goal of this research was to develop more accurate ANN models that could predict air temperature for each prediction horizon. The specific objective was to determine if the ANN model accuracy could be improved by applying a genetic algorithm (GA) for each prediction horizon to determine the preferred duration and resolution of input prior data for each environmental variable. The ANN models created based on this GA based approach provided smaller errors than the models created based on the existing constant duration and fixed data resolution approach for all twelve prediction horizons. Except for a few cases, the GA generally included a longer duration for prior air temperature data and shorter durations for other environmental variables. The mean absolute errors (MAEs) for the evaluation input patterns of the one-, four-, eight-, and twelve-hour prediction models that were based on this GA approach were 0.564 °C, 1.264 °C, 1.766 °C and 2.018 °C, respectively. These MAEs were improvements of 3.98%, 4.59%, 2.55% and 1.70% compared to the models that were created based on the existing approach for the same corresponding prediction horizons. Thus, the GA based approach to determine the duration and resolution of prior input data resulted in more accurate ANN models than the existing ones for air temperature prediction. Future work could examine the effects of various GA and fitness evaluation parameters that were part of the approach used in this study.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks

In this study, the application of artificial neural networks (ANN) to predict the ultimate moment capacity of reinforced concrete (RC) slabs in fire is investigated. An ANN model is built, trained and tested using 294 data for slabs exposed to fire. The data used in the ANN model consists of seven input parameters, which are the distance from the extreme fiber in tension to the centroid of the steel on the tension side of the slab (d′), the effective depth (d), the ratio of previous parameters (d′/d), the area of reinforcement on the tension face of the slab (A_s), the fire exposure time (t), the compressive strength of the concrete (f_cd), and the yield strength of the reinforcement (f_yd). It is shown that ANN model predicts the ultimate moment capacity (M_u) of RC slabs in fire with high degree of accuracy within the range of input parameters considered. The moment capacities predicted by ANN are in line with the results provided by the ultimate moment capacity equation. These results are important as ANN model alleviates the problem of computational complexity in determining M_u.     

Trust region‐based optimization of PKI neural models for RF/microwave devices

This article proposes a new trust region‐based optimization technique for Radio Frequency (RF)/microwave devices. The proposed approach is apt for modeling scenarios, where standard ANN multilayer perceptron (MLP) and Prior Knowledge Input (PKI) models fail to deliver a satisfactory model. This approach feeds output of standard ANN model as knowledge input to PKI model. The ANN model and the PKI model form a symbiotic pair to yield accurate results. In this paper, the dogleg routine is exploited in the process of optimization to obtain valid trust region steps. The proposed method is compared with sensitivity technique via several RF/microwave components. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.