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The reliability of thimble tubes plays a critical role for maintaining the safety of a nuclear power plant. The defect depth needs to be quantified and predicted to support the operational decision-making. This paper presents a method to quantify the defects on thimble tube wall based on the analyzation of eddy current testing (ECT) data. Then, a method using artificial neural network (ANN) to predict the detect depth is studied. The tubes are divided into 2 shapes and four regions according to their positions and the data of each region and each shape is expanded by mean interpolation. A prediction model based on ANN is constructed for each shape in each region. The experimental results show that the model can predict the signal of the next year according to the signal of the previous three years with mean absolute percentage error less than 16%. [ABSTRACT FROM AUTHOR]

Aims: The purpose of this study was to identify risk factors for cognitive impairment in advanced cancer patients and to develop predictive models based on these risk factors. Background: Cancer‐related cognitive impairment seriously affects the quality of life of advanced cancer patients. However, neural network models of cognitive impairment in patients with advanced cancer have not yet been identified. Design: A cross‐sectional design was used. Methods: This study collected 494 questionnaires between January and June 2022. Statistically significant clinical indicators were selected by univariate analysis, and the artificial neural network model and logistic regression model were used for multivariate analysis. The predicted value of the model was estimated using the area under the subject's working characteristic curve. Result: The artificial neural network and the logistic regression models suggested that cancer course, anxiety and age were the major risk factors for cognitive impairment in advanced cancer patients. All the indexes of artificial neural network model constructed in this study are better than those of the logistic model. Conclusion: The artificial neural network model can better predict the risk factors of cognitive impairment in patients with advanced cancer. Better prediction will enable nurses and other healthcare professionals to provide better targeted and timely support. Summary statement: What is already known about this topic?: Cancer‐related cognitive impairment has significantly affected the quality of life of patients with advanced cancer.Risk factors associated with cognitive impairment in patients with advanced cancer have not been adequately documented, and neural network models have not been identified. What this paper adds?: This study found that long a cancer course, anxiety and ageing were the most important risk factors for cognitive impairment in patients with advanced cancer.The artificial neural network showed good predictive indicators, indicating that it can be used to predict cognitive impairment in patients with advanced cancer. The implications of this paper: Neural network models can help nurses and other clinicians predict cognitive impairment in advanced cancer patients early which will allow provision of timely intervention. [ABSTRACT FROM AUTHOR]

BACKGROUND: Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure. OBJECTIVE: In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery. METHODS: A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature T c and bone surface temperature T b for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data. RESULTS: Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R = 0.99661, validation set R = 0.85003, testing set R = 0.90421, all temperature data set R = 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements. CONCLUSION: This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety. [ABSTRACT FROM AUTHOR]

Rock mechanical properties are complex but essential for reservoir stimulation. Both experimental and empirical equation method have inevitable defects, and artificial neural network model (ANN) shows great potential in rock mechanics. This paper established a preliminary rock database from the Keshen block in Tarim Basin and improved clustering algorithm to assess the similarity between wells, creating sub-databases and realizing data sharing among similar wells. 18 parameters are used to predict ten rock mechanical properties and corresponding model inputs are optimized by correlation and sensitivity analysis separately. The results show that more accurate predictions can be obtained using optimized sample data and model inputs. The average predicted loss value using all 31 wells (221 samples) was 7.28%, while the value was reduced to 5.4% after clustering analysis using seven wells (95 samples). The average loss value can be further reduced to 4.3% using the optimized model inputs. As for rock peak stress, the predicted loss value is 2.5%, the determination coefficient (R2) is 0.983, and the root mean square error (RMSE) is 0.03. By optimizing training method, improving training sample quality, and optimizing model inputs, the problem of overfitting can be alleviated and a more reliable ANN model can be obtained. Highlights: Defined homogeneity coefficient based on computed tomography and image recognition, which is important for predicting rock mechanical properties. A preliminary database was established based on the rock samples from 31 wells for the training of the artificial neural network model. Improved clustering algorithm and performed similarity analysis between wells, created sub-databases, and improved data quantity and quality. 18 parameters are used to predict 10 rock mechanical properties, and model inputs are optimized by correlation and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R2 = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature. [ABSTRACT FROM AUTHOR]

Time-space four-dimensional motion target localization is a fundamental and challenging task in the field of intelligent driving, and an important part of achieving the upgrade in existing target localization technologies. In order to solve the problem of the lack of localization of moving targets in a spatio-temporal four-dimensional environment in the existing spatio-temporal data model, this paper proposes an optical imaging model in the four-dimensional time-space system and a mathematical model of the object-image point mapping relationship in the four-dimensional time-space system based on the central perspective projection model, combined with the one-dimensional "time" and three-dimensional "space". After adding the temporal dimension, the imaging system parameters are extended. In order to solve the nonlinear mapping problem of complex systems, this paper proposes to construct a time-space four-dimensional object-image mapping relationship model based on a BP artificial neural network and demonstrates the feasibility of the joint time-space four-dimensional imaging model theory. In addition, indoor time-space four-dimensional localization prediction experiments verify the performance of the model in this paper. The maximum relative error rates of the predicted motion depth values, time values, and velocity values of this localization method compared with the real values do not exceed 0.23%, 2.03%, and 1.51%, respectively [ABSTRACT FROM AUTHOR]

This paper investigates the possibility for implementing machine learning-aided prediction in analyzing the buckling phenomena of ballasted railway tracks induced by extreme temperature. In this study, artificial neural networks (ANNs) have been developed to identify the relationship between various ballasted track conditions and outputs, namely safe temperature and buckling temperature. The variables included in the objective function of the optimization problems are the lateral resistance of ballasted track provided by ballast-sleeper interaction, torsional resistance provided by fastening systems, and misalignment of the track. Due to its complexity in parameter combinations, the objective of this study is to create predictive models with the aim of minimizing the usage of scarce resources. Thus, this paper is the first to develop a novel machine learning-aided prediction of railway track buckling due to extreme temperature. Comprehensively, all 353 datasets of the safe and buckling temperatures derived from previous finite element (FE) simulation results have been collected and trained. Note that the mean squared error (MSE) and the coefficient of determination ( R 2) are considered to quantify the performance of the ANN architectures. The optimal ANN architecture with a very high rate of accuracy has been determined and highlighted. Thus, the suggested neural network model can be applied conveniently to help estimate safe and buckling temperatures of the complex track models in order to improve track conditions and thus prevent track buckling in summer. [ABSTRACT FROM AUTHOR]

• Developing optimized neural network-based models for trapdoor stability prediction. • New FELA solutions for 3D rectangular trapdoors in anisotropic clays. • Developing hybrid machine learning models based on ANNs with various optimization algorithms. • Examining several performance metrics, regression plots, and rank analysis. The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges. [ABSTRACT FROM AUTHOR]

Wind power, as a potential new energy generation technology, is gradually developing towards to the mainstream energy in the world. However, the inherent random volatility of wind brings severe challenges to the safe operation of the grid and the reliability of power supply, one of the effective ways to solve the problem is to improve the accuracy of wind speed prediction. However, most of wind speed prediction model cannot well mine the inherent regularity of wind speed data. Therefore, this paper introduces variational mode decomposition (VMD) algorithm. And the Short-term Wind Speed Prediction Model based on GA-ANN improved by VMD is proposed, which can effectively improve the accuracy of wind speed prediction. Firstly, hierarchical cluster method in this paper is employed to extract the historical data with high similarity to the predicted day. And then the appropriate number of decompositions K is selected by judging the value of sample entropy, so that the extracted historical data is decomposed into K subsequences by the variational mode decomposition. Next, with the global optimization ability of genetic algorithm, the artificial neural network is optimized to improve the forecasting performance. Finally, the short-term wind speed forecasting model based on GA-ANN improved by VMD is employed to predict the wind speed of each subsequence and superimposed them to obtain the final wind speed prediction sequence. The results in this paper show that (1) the model can find the periodic fluctuation of wind speed through historical data by hierarchical cluster method, so that significantly improving the accuracy of short-term wind speed prediction; (2) for the wind speed prediction, the error value of GA-ANN model is smaller than that of BP neural network; (3) in view of the inherent nature of the wind, the model proposed in this paper can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results. The research work can help the relevant departments of the power system to accurately assess the risk of power grid operation, make a reasonable generation plan, effectively reduce the cost of power operation, and then greatly promote the development of green energy. • Proposing a new windspeedprediction model named HC-VMD-GA-BPmodel. • Using hierarchical cluster methodtoselect the historical wind speed data. • VMD is introduced to decomposethe original wind speed sequence. • Artificial neural networkisoptimized by GA to improve the prediction performance. • The HC-VMD-GA-BP model is comparedwith other models to verify itseffectiveness and high prediction accuracy. [ABSTRACT FROM AUTHOR]

Viscoelastic characterization of asphalt binders is critical for selecting suitable binders for a given climatic region and for improving long-term performance prediction using mechanistic-empirical pavement analysis and design methods, such as the Mechanistic-Empirical-Pavement-Design-Guide (MEPDG). A-VTS information (viscosity data) was used in the past and existing databases have this type of information. Nowadays, Dynamic Shear Modulus (|G*|) and Phase Angle (δ) determination using the Dynamic Shear Rheometer (DSR) is common practice. This resulted in the existence of old and new databases in highway agencies. The goal is to find a way to combine these separate data sets and merge them into a unified national database based on |G*| and δ. The work discussed in this paper is critical to make this merger possible and to use this in future binder to mixture predictive model developments and in MEPDG. In this study, |G*| and δ prediction models were developed. A machine learning technique named Artificial Neural Network (ANN) was employed in this process. 8940 data points from 41 binders were utilized in the model development process. After robust training, validation, and testing process, a |G*| prediction model with seven neurons and a δ prediction model with three neurons were reported. The models were compared with previous Bari-Witczak and Onifade-Birgisson models. ANN-based models performed better in every statistical parameter. Two equations were also extracted from the ANN models, which can be used reliably to predict |G*| and δ from A-VTS data. These two models with closed-form equations can be incorporated into the MEPDG for |G*| and δ predictions. • New models for Binder dynamic shear modulus and phase angle using binder viscosity data with temperature and frequency. • Artificial Neural Network (ANN) is utilized in the model development process. • An equation is extracted from each ANN-based model. • The prediction performance of new models is better than existing models in every statistical parameter. [ABSTRACT FROM AUTHOR]

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance. [ABSTRACT FROM AUTHOR]

Due to a lack of solar radiation measurement instruments in various regions across the world, particularly in Ethiopia, many empirical models have been developed to estimate global solar radiation. Reliable global solar radiation infor- mation is essential for the design and development of solar energy systems.Thus, the aim of this paper is to investigate the feasibility of using Artificial Neural Network (ANN) to predict mean daily global solar radiation (GSR) and to compare the performance of ANN and empirical models based on sunshine in estimating mean daily global solar irradiation on a horizontal surface.For the ANN model, network inputs were daily data for a number of days, average daily data (sunshine hours, maximum temperature, minimum temperature, wind speed, relative humidity, and pressure), and while daily averaged global solar radiation was the network output.After training, the model had R values of 0.891 for training, 0.932 for validation, 0.920 for testing, and 0.903 for all, with validation performance of 0.0797.The results of the ANN model were compared to the empirical equations using statistical error evaluation (MBEANN = 0.0005, RMSEANN = 0.3310, and R2ANN = 0.7998).The agreement between the values of NASA and estimated values reveals that the ANN exhibits good performance in studying daily GSR. The result also shows that the mean daily GSR is varying between 3.675KWh/m² (August-1) and 6.935 KWh/m² (April-19) in the study site. [ABSTRACT FROM AUTHOR]

This paper is motivated by searching for the perfect pattern for the spectroscopy spectra using artificial neural networks (ANN) using python programming coding. The pattern from the spectroscopy is based on the absorption and emission of light and other radiation by materials in relation to the wavelength dependence of these processes. Spectral equipment such as spectrometers, spectral analysers, spectrographs, or spectrophotometers is utilised to determine spectrum values. The problem in this spectroscopy is to identify the sample or analyte, which can be solved by a prediction model for spectroscopy using Python. These problems occur when finding the best algorithm of pre-processing techniques that can predict any model accurately into an understandable format for prediction models. Various types of pre-processing techniques have been used, such as Multiplicative Scatter Correction (MSC), Inverse MSC, Extended MSC (EMSC), Extended Inverse MSC, de-trending, Standard Normal Variate (SNV) and normalisation in order to get a better r2 value. In this project, we find the r2 and the root mean square error (RMSE) to evaluate the prediction values and the actual values. First, choosing pre-processing techniques and then finding the best statistical method for constructing predictive models that produce high accuracy. We used ANN in this project as a prediction model. Based on the results, we managed to achieve our objective, which is that the prediction model has more than 90% of accuracy. Furthermore, the results show that our prediction model has 1.0 accuracy at 100 Epoch with a 0.3 learning rate. Finally, we can conclude that our prediction model can be used to predict the spectroscopybased data format. [ABSTRACT FROM AUTHOR]

This paper aimed to explore the non‐destructive detection of apple firmness based on portable acoustic signals for consumers. The acoustic eigenvalues processed by Time‐domain, Fourier Transform (FT), and Hilbert Huang Transform (HHT) are used to analyse the correlations with apple firmness. The apple firmness prediction models are established through Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). The results proved that the acoustic eigenvalues of wave index form and total energy of 8–9 kHz correlated with apple firmness (P < 0.05), the significant acoustic eigenvalues of sound intensity, amplitude difference, max frame energy, maximum power spectral density, power spectral density characteristics and total energy of 0–1 kHz significantly correlated with apple firmness (P < 0.01). The average relative error of ANN model was significantly lower than that of MLR model (P < 0.05). Compared with Time‐FT, the acoustic eigenvalues processed by a combination of Time‐FT‐HHT possessed a superior effect on the prediction model established by ANN of apple firmness. [ABSTRACT FROM AUTHOR]

This paper presents a new artificial neural network (ANN)-based system model that concatenates an optimized artificial neural network (OANN) and a neural network compensator (NNC) in series to capture temporally varying system dynamics caused by slow-paced degradation/anomaly. The OANN comprises a complex, fully connected multilayer perceptron, trained offline using nominal, anomaly free data, and remains unchanged during online operation. Its hyperparameters are selected using genetic algorithm-based meta-optimization. The compact NNC is updated continuously online using collected sensor data to capture the variations in system dynamics, rectify the OANN prediction, and eventually minimize the discrepancy between the OANN-predicted and actual response. The combined OANN-NNC model then reconfigures the model predictive control (MPC) online to alleviate disturbances. Through numerical simulation using an unmanned quadrotor as an example, the proposed model demonstrates salient capabilities to mitigate anomalies introduced to the system while maintaining control performance. We compare the OANN-NNC with other online modeling techniques (adaptive ANN and multinetwork model), showing it outperforms them in reference tracking of altitude control by at least 0.5m and yaw control by 1 deg. Moreover, its robustness is confirmed by the MPC consistency regardless of anomaly presence, eliminating the need for additional model management during online operation. [ABSTRACT FROM AUTHOR]

This paper proposes an artificial neural network (ANN) to predict the solar energy generation produced by photovoltaic generators. The intermittent nature of solar power creates two main issues. Firstly, power production and demand have to be balanced to ensure the control of the whole system, and the inherent variability of clean energies makes this difficult. Secondly, energy generation companies need a highly accurate day-ahead or intra-day estimation of the energy to be sold in the electricity pool. For the tool developed in this paper, we address the issue of the complexity of control in systems that are based on solar energies. The tool's ability to predict the parameters that are involved in solar energy production will allow us to estimate the future power production in order to optimise grid control. Our tool uses an ANN which we developed using MATLAB ® software. The results were validated by analysing the root mean square error of the prediction for days outside the database used for training the ANN. The difference between the actually produced and predicted energy is about 0.5–9%, meaning that the accuracy of our tool is sufficient enough to be installed in systems which have integrated solar generators. [ABSTRACT FROM AUTHOR]

The Newmark-type predictive models are extensively used to estimate earthquake-induced sliding displacements (D) of slopes. Most existing models are designed for shallow slope failure using polynomial regression. Based on a large amount of decoupled sliding-block analyses, this paper proposes an artificial neural network (ANN)-aided model to predict D for both shallow and deep slope failures using peak ground acceleration and spectral acceleration at the 2 s period (SA (2 s)). Three sub-models are included for estimating shallow sliding displacement, representing dynamic response of sliding mass, and modifying the displacement for deep failure, respectively. The key features achieved are as follows: (1) the inputted SA is more easily accessible than the mean period (T m) required by the existing models; (2) impedance ratio (IR) is utilized to account for stiffness conditions of geo-materials underlying slip surfaces; (3) powerful ANN is introduced for the first time as a surrogate of the decoupled analysis. The SA -based model generally yields lower biases and uncertainty than the existing models. Since larger D is produced for smaller IR , the existing models with a single IR value would be unconservative for relatively stiff underlying soils and/or soft overlying soils. Coefficients of the proposed model are provided for practical applications. [ABSTRACT FROM AUTHOR]

• An artificial neural network-parallel connected (ANN-PL) model is proposed to predict vehicle running attitude under the single-vehicle impact condition. • The 3D multibody dynamics model of the single-vehicle impact on the rigid wall is established to generate the datasets and validated by the experimental full-scale test. • The predictive capacity of the proposed network model is validated in the test datasets. • The key factors affecting vehicle running attitude are studied by the change of MSE method (COM) in the ANN-PL. Artificial neural networks have drawn growing attention for their outstanding predictive capability combined with traditional research methods. This paper aims to propose a vehicle running attitude prediction model based on Artificial Neural Network-Parallel Connected (ANN-PL), predicting the longitudinal displacement (S vx) and vertical displacement (S vz) of the vehicle body, the vehicle head-up angle (α), and the overriding risk (C d). The 3D multibody dynamics model (MBD) of the single-vehicle impact on the rigid wall, namely 3D-MBD-SV, was established and validated by the experimental full-scale vehicle collision test. Based on the reliable 3D-MBD-SV, the design of experiment (DOE) approach was carried out to obtain the datasets for training the ANN-PL. The ANN-PL exhibited excellent computational efficiency and satisfactory prediction accuracy compared to the multibody dynamics and finite element simulation calculation methods. However, the different network hyperparameters of the ANN-PL network are essential to prediction accuracy, considering the number of hidden layers and neurons in this paper. In terms of the variables factor analysis, the change of Mean Square Error (MSE) method (COM) in the ANN-PL was used to explore the relationship between the eleven essential input variables and vehicle running attitude. It was found that the maximum relative contribution in ANN-PL (S vx , S vz , α, C d) is vehicle body mass (M c) at 70.65%, impact velocity (V x) at 43.39%, vertical offset of the vehicle body center mass (CM z) at 30.14%, and primary suspension axle box spring vertical travel (D pz) at 13.63%, respectively. The outcome of this study is expected to provide a research method to solve the complicated engineering issue by building a new artificial neural network algorithmic framework combined with the multibody dynamics and finite element simulation calculation methods. [ABSTRACT FROM AUTHOR]

Abstract The prediction of interval-valued data is a challenging task as the predicted lower bounds of intervals should not cross over the corresponding upper bounds. In this paper, a regularized artificial neural network (RANN) is proposed to address this difficult problem. It provides a flexible trade-off between prediction accuracy and interval crossing. Compared to existing hard-constrained methods, the RANN has the advantage that it does not necessarily reduce the prediction accuracy while preventing interval crossing. Extensive experiments are conducted based on both simulation and real-life datasets, with comparison to multiple traditional models, including the linear constrained center and range method, the least absolute shrinkage and selection operator-based interval-valued regression, the nonlinear interval kernel regression, the interval multi-layer perceptron and the multi-output support vector regression. Experimental results show that the proposed RANN model is an effective tool for interval-valued data prediction tasks with high prediction accuracy. [ABSTRACT FROM AUTHOR]

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic. [ABSTRACT FROM AUTHOR]

This paper proposes a machining performance prediction approach on multiple performances of wire electrical discharge machining (WEDM) on Inconel 718. Artificial neural network (ANN) is emphasized to predict the machining performances. Many efforts have been made to model the performances of WEDM using ANN. However, to obtain the best ANN model, generally the network parameters are not consistent and so far, the selection has been made in a random manner and resulted in an excessive experimental trial. Taguchi design orthogonal array L256 is implemented in the process of network parameter selection to search for the potential machining model. This approach, prescribed as OrthoANN, is simplified to avoid as much as unnecessary experimentations. Material removal rate, surface roughness (Ra), cutting speed (Vc) and sparking gap (Sg) are the machining performances considered in this study. Five machining parameters considered; pulse on time, pulse off time, peak current, servo voltage and flushing pressure. Cascade forward back-propagation neural network (CFNN) is found to be the best network type of the selected data set. One hidden layer 5-14-4 CFNN showed the most precise and generalized network architecture with very good prediction accuracy. An average of 5.16% error is generated which seems to be in superior concurrence with the actual experimental results. Confirmation test is carried out to verify the machining performances suggested by this approach. [ABSTRACT FROM AUTHOR]

The energy production analysis of a system based on renewable technology depends on the inputs estimation accuracy. The solar energy is a free resource characterized by high variability; hence, its correct evaluation is a strategic factor for the feasibility of a solar system. In this paper a new methodological approach is presented in order to evaluate more accurately the electric and thermal energy production of a point-focus concentrating photovoltaic and thermal system (CPV/T). Two Artificial Neural Network (ANN) models for predicting solar global radiation and direct normal solar irradiance (DNI) are developed adopting different parameters such as climatic, astronomic and radiometric variables. In particular, a new combination of parameters is proposed in this paper and adopted first of all for the global radiation evaluation whose ANN model can be easily compared with the literature; the data are trained and tested by a multi layer perceptron (MLP). Hence, the results validation for the global solar radiation evaluation has encouraged to design an ANN model for the DNI by means of a similar variables set. The MLP network is trained, tested and validated for the hourly DNI estimation obtaining the MAPE, RMSE and R 2 statistical indexes values respectively equal to 5.72%, 3.15% and 0.992. Finally, the electric and thermal outputs of a point-focus CPV/T system are evaluated varying the concentration factor and cells number, and adopting as input the DNI evaluation results obtained by the ANN model presented in this paper. The CPV/T system outputs are estimated referring to the city of Salerno (Italy) under different meteorological conditions. [ABSTRACT FROM AUTHOR]

Building cooling load prediction is critical to the success of energy-saving measures. While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model's parameters. This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model's mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model's performance according to other ANN models. It is also successfully applied to predicting the cooling load of a commercial building in Hong Kong. The occupancy areas and concentration of carbon dioxide inside the building are successfully adopted to mimic the building's internal cooling load. Training data was adopted from actual measurements taken inside the building. Its results show reasonable agreement with actual cooling loads. [ABSTRACT FROM AUTHOR]

In this paper, group contribution (GC) property models for the estimation of acid dissociation constants ( K a ) of organic compounds are presented. Three GC models are developed to predict the negative logarithm of the acid dissociation constant pK a : (a) a linear GC model for amino acids using 180 data-points with average absolute error of 0.23; (b) a non-linear GC model for organic compounds using 1622 data-points with average absolute error of 1.18; (c) an artificial neural network (ANN) based GC model for the organic compounds with average absolute error of 0.17. For each of the developed model, uncertainty estimates for the predicted pK a values are also provided. The model details, regressed parameters and application examples are highlighted. [ABSTRACT FROM AUTHOR]

The Baltic Dry Index (BDI) is a robust indicator in the shipping sector in terms of global economic activities, future world trade, transport capacity, freight rates, ship demand, ship orders, etc. It is hard to forecast the BDI because of its high volatility and complexity. This paper proposes an artificial neural network (ANN) approach for BDI forecasting. Data from January 2010 to December 2016 are used to forecast the BDI. Three different ANN models are developed: (i) the past weekly observation of the BDI, (ii) the past two weekly observations of the BDI, and (iii) the past weekly observation of the BDI with crude oil price. While the performance parameters of these three models are close to each other, the most consistent model is found to be the second one. Results show that the ANN approach is a significant method for modeling and forecasting the BDI and proving its applicability. [ABSTRACT FROM AUTHOR]

The functional design of submerged breakwaters is still developing, particularly with respect to modelling of the nearshore wave field behind the structure. This paper describes a method for predicting the wave transmission coefficients behind submerged breakwaters using machine learning algorithms. An artificial neural network using the radial-basis function approach has been designed and trained using laboratory experimental data expressed in terms of non-dimensional parameters. A wave transmission coefficient calculator is presented, based on the proposed radial-basis function model. Predictions obtained by the radial-basis function model were verified by experimental measurements for a two dimensional breakwater. Comparisons reveal good agreement with the experimental results and encouraging performance from the proposed model. Applying the proposed neural network model for predictions, guidance is given to appropriately calculate wave transmission coefficient behind two dimensional submerged breakwaters. It is concluded that the proposed predictive model offers potential as a design tool to predict wave transmission coefficients behind submerged breakwaters. A step-by-step procedure for practical applications is outlined in a user-friendly form with the intention of providing a simplified tool for preliminary design purposes. Results demonstrate the model’s potential to be extended to three dimensional, rough, permeable structures. [ABSTRACT FROM AUTHOR]

Knowing the pressure coefficient on building surfaces is important for the evaluation of wind loads and natural ventilation. The main objective of this paper is to present and to validate a computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings. This approach makes use of artificial neural network (ANN) to estimate the surface-average pressure coefficient for each wall and roof according to the building geometry and the wind angle. Three separate ANN models were developed, one for each roof type, and trained using an experimental database. Applied to a wide variety of buildings, the current ANN models were proved to be considerably more accurate than the commonly used parametric equations for the estimation of pressure coefficients. The proposed ANN-based methodology is as general and versatile as to be easily expanded to buildings with different shapes as well as to be coupled to building performance simulation and airflow network programs. [ABSTRACT FROM AUTHOR]

Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm. [ABSTRACT FROM AUTHOR]

In photovoltaic (PV) modules manufacturer provides rating under standard test conditions (STC). But STC hardly occur under outdoor conditions so it is important to investigate PV power by experimental analysis. In this study extensive literature survey of PV module electrical characteristics by conventional methods and ANN techniques are carried out. It is found that experimental analysis of PV modules maximum power under outdoor conditions remains a major research area. For this measurement of 75 Wp PV module are performed under outdoor conditions at Centre for Energy and Environmental Engineering, National Institute of Technology, Hamirpur, India. To find most influencing variables for PV power prediction, five different sets of parameters are served as inputs to establish five Artificial Neural Network (ANN) models and Multiple Linear Regression (MLR) Models which is novelty of this paper. The results shows that solar radiation and air temperature are found to be most influencing input variables for ANN based prediction of maximum power produced by PV module with mean absolute percentage (MAPE) of 2.15 %. The mean absolute percentage (MAPE) errors for ANN models are found to vary between 2.15 % to 2.55 % whereas for MLR models it varies from 13.04 % to 19.34 %, showing better prediction of ANN models. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the development of an Artificial Neural Network for the prediction of the wave reflection coefficient from a wide range of coastal and harbor structures. The Artificial Neural Network is trained and validated against an extensive database of about 6000 data, including smooth, rock and armor unit slopes, berm breakwaters, vertical walls, low crested structures, oblique wave attacks. The structure and data included in this database, as well as the approach used in this paper, follow the work done on wave overtopping within the CLASH project. In this new Artificial Neural Network 13 input elements are used to represent the physics of the reflection process taking into account the structure geometry (height, submergence, straight or non-straight slope, with or without berm or toe), the structure type (smooth or covered by an armor layer, with permeable or impermeable core) and the wave attack (water depth, wave height, wave length, wave obliquity, directional spreading). The selection of the input elements and of the algorithms used in the network is described based on an in-depth sensitivity analysis of the network performance. The accuracy of the network is quite satisfactory, being the average root mean squared error lower than 0.04. This value is consistent between the Artificial Neural Network calibrated on the original dataset and the one calibrated on boot-strapped datasets in which data reliability and structure complexity are considered. The performance of the network is compared for limited datasets with selected available literature formulae proving that this approach is able to estimate the experimental reflection coefficients with greater accuracy than the empirical formulae calibrated on these same datasets. [Copyright &y& Elsevier]

Abstract: Accurate predictions of oil production from wells are important for cost-effective operations in the petroleum industry. Such a prediction can assist petroleum engineers in project design, facilities construction scheduling, economic forecasting, and environment management. However, it is difficult to obtain accurate predictions of oil production due to the complex subsurface conditions of reservoirs. Reservoir engineers typically employ curve fitting techniques for predicting primary production of wells based on existing production data. Instead of using this approach, application of some artificial intelligence techniques for production prediction is explored in this paper. The artificial neural network (ANN) approach is a mathematical modeling technique inspired by biological neural networks. The ANN consists of an interconnected group of artificial neurons, which process information via a learning phase between inputs and outputs so as to find patterns in data. A decision tree learning algorithm such as C4.5 usually considers one variable at a time and ignores interdependencies among input attributes, which reduces its model accuracy. On the other hand, an enhanced decision tree learning approach called Neural Decision Tree (NDT) takes interdependencies among input attributes into consideration and generates a decision tree for prediction of petroleum production. This paper presents a comparison of prediction results produced from the three machine intelligence approaches of C4.5 model, NDT model and the ANN model. The results show that the NDT model can significantly improve upon the classification accuracy of the C4.5 algorithm. When compared to the ANN approach, the NDT model has a lower classification rate in general but is better able to describe classes with low number of instances. [Copyright &y& Elsevier]

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation. [ABSTRACT FROM AUTHOR]

This work presents the approach of a mobile dose prediction system for NPP emergencies with nuclear material release. The objective is to provide extra support to field teams decisions when plant information systems are not available. However, predicting doses due to atmospheric dispersion of radionuclide generally requires execution of complex and computationally intensive physical models. In order to allow such predictions to be made by using limited computational resources such as mobile phones, it is proposed the use of artificial neural networks (ANN) previously trained (offline) with data generated by precise simulations using the NPP atmospheric dispersion system. Typical situations for each postulated accident and respective source terms, as well as a wide range of meteorological conditions have been considered. As a first step, several ANN architectures have been investigated in order to evaluate their ability for dose prediction in hypothetical scenarios in the vicinity of CNAAA Brazilian NPP, in Angra dos Reis, Brazil. As a result, good generalization and a correlation coefficient of 0.99 was achieved for a validation data set (untrained patterns). Then, selected ANNs have been coded in Java programming language to run as an Android application aimed to plot the spatial dose distribution into a map. In this paper, the general architecture of the proposed system is described; numerical results and comparisons between investigated ANN architectures are discussed; performance and limitations of running the Application into a commercial mobile phone are evaluated and possible improvements and future works are pointed. [ABSTRACT FROM AUTHOR]

This paper reports the refinement of a published empirical model of salinity in the San Francisco Bay-Delta estuary by integration with a Bayesian artificial neural network (ANN) model and incorporation of additional inputs. Performance goals established for the resulting hybrid model are based on the quality of fit to observed data (replicative and predictive validation) as well as sensitivity when compared with a priori knowledge of system behavior (structural validation). ANN model parameters were constrained to provide plausible sensitivity to coastal water level, a key input introduced in the hybrid formulation. In addition to representing observed data better than the underlying empirical model while meeting structural validation goals, the hybrid model allows for characterization of prediction uncertainty. This work demonstrates a real-world application of a general approach— integration of a pre-existing model with a Bayesian ANN constrained by knowledge of system behavior—that has broad application for environmental modeling. [ABSTRACT FROM AUTHOR]

Road traffic noise is the most prevalent form of environmental noise pollution, and it can either be measured in the field or predicted through verified mathematical models. While many road traffic noise models are available around the world, these models cannot be simply generalized because local conditions affecting such noise (e.g., vehicle type and weather) vary from one locality to another. In this paper, the Artificial Neural Network (ANN) technique was employed to model road traffic noise in a city with known hot climate, namely Sharjah City in United Arab Emirates. Toward this end, field data were collected from three different road sites which resulted in more than 420 hourly measurements of noise level, traffic volume and classification, average speed, and roadway temperature. Overall, a total of 16 feed-forward back-propagation ANN models with one and two hidden layers were undertaken in this research. The best-performing models were compared with two conventional models: The Basic Statistical Traffic Noise model (BSTN) and the Ontario Ministry of Transport Road Traffic Noise model (ORNAMENT). In general, results showed that ANN models outperformed the conventional models. Furthermore, the performance of both ANN and conventional road noise models was further improved by including roadway temperature. While the reported work emphasizes the importance of adapting models to different local conditions, it also encourages others to consider the roadway temperature when modeling traffic noise, especially in areas of hot climate. Given that ANN models are often designated as “black-box,” a few techniques were utilized to expand the capabilities of ANN models to offer explanatory insights in modeling the inter-relations between the variables considered in this study. Generally, it was found that the ANN model was capable of identifying these relations as expected. In-depth analysis showed that the distance from the edge of road was found to be the most significant contributing factor whereas heavy-vehicle volume was surprisingly found to be the least. [ABSTRACT FROM AUTHOR]

Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles' initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville's formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset. [ABSTRACT FROM AUTHOR]

This paper presents the state of the art report on available approaches to predicting the ultimate bearing capacity of two-layered soils. The article discusses three most popular methods, including the classical method, application of the finite element method and artificial neural network. Various approaches based on these three powerful tools are studied and their methodologies are discussed. [ABSTRACT FROM PUBLISHER]

An accurate solar potential estimation of a specific location is basic for the solar systems evaluation. Generally, the global solar radiation is determined without considering its different contributes, but systems as those concentrating solar require an accurate direct normal irradiance (DNI) evaluation. Solar radiation variability and measurement stations non-availability for each location require accurate prediction models. In this paper two Artificial Neural Network (ANN) models are developed to predict daily global radiation (GR) and hourly direct normal irradiance (DNI). Two heterogeneous set of parameters as climatic, astronomic and radiometric variables are introduced and the data are obtained by databases and experimental measurements. For each ANN model a multi layer perceptron (MLP) is trained and validated investigating nine topological network configurations. The best ANN configurations for predicting GR and DNI are tested on different new dataset. MAPE, RMSE and R 2 for the GR model are respectively equal to 4.57%, 160.3 Wh/m 2 and 0.9918, while for the DNI they are equal to 5.57%, 17.7 W/m 2 and 0.994. Hence, the proposed models show a good correlation both between measured and predicted data and with the literature. The main results obtained are the DNI and the GR models predicting which have allowed the evaluation of the electric energy production by means of two different photovoltaic systems used for a residential building. Hence, the developed ANN models represent a good tool to support the assessment of the green energy production evaluation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to achieve an accurate prediction of heat transfer deterioration and Nu of supercritical CO 2 upward flowing in vertical channels using the artificial neural network method. Firstly, based on extensive experiments of round tubes and the square annular channel, a database with 11,589 data in 431 sets of experimental conditions was established. Then ANN models were trained with the training set which only contains the round tube data, and the optimal model structures were determined. The effects of input features on the prediction performance, including the experimental parameters, the dimensionless parameters, and the thermophysical property ratios, were compared, and the results showed that the model with all features in the input parameters performed best. The test set contains the experimental data of the round tubes and the square annular channel, the prediction results of the models on the test set showed that the model for HTD only has high precision on the round tube data, while the model for Nu has precise prediction performance both on the data of round tubes and the square annular channel. Simultaneously, the model for Nu shows a higher prediction performance than the correlations. • Effects of mass flux, heat flux, inlet temperature, and tube diameter on the heat transfer characteristics are evaluated. • Two models for the prediction of HTD and Nu were proposed based on the artificial neural network. • The effects of different input features on the ANN model performance are compared. • The model for predicting Nu outperforms the correlations. [ABSTRACT FROM AUTHOR]

When using the Taguchi method, an L18 or L27 orthogonal array is usually adopted. However, this requires many experiments (18 or 27 runs, respectively), consuming time, and resources. This study proposes a progressive Taguchi neural network model, which combines the Taguchi method with the artificial neural network to construct a prediction model for a CO laser cutting experiment. During CO laser cutting, energy from the moving laser is accumulative. The paper develops an integral equation of energy density during laser beam movement and lets it determine the sliding level of control factor. Meanwhile, the paper proposes that in Stage 1, only less number of experiments is required to be conducted by L9 orthogonal array. After the crucial supplementary experimental training samples proposed in Stage 2 are also included, high-accuracy prediction of artificial neural network can be completed. Based on analysis from the progressive Taguchi neural network, the Stage 1 preliminary network-with only a few available experimental examples-has achieved good predictive ability from regions near the Taguchi control points. For regions further out, the predictions have been increasingly unreliable. Nevertheless, the high precision of Stage 2 Taguchi network has good predictive results for all regions. [ABSTRACT FROM AUTHOR]

Abstract: Artificial neural network (ANN) was developed to predict the morphology of TiO2 nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of TiO2 nanotube was considered as three parameters including the degree of order, diameter and length. Applying radial basis function neural network to predict TiO2 nanotube degree of order and back propagation artificial neural network to predict the nanotube diameter and length were emphasized in this paper. Some important problems such as the selection of training data, the structure and parameters of the networks were discussed in detail. It was proved in this paper that ANN technique was effective in the prediction work of TiO2nanotube fabrication process. [Copyright &y& Elsevier]

Abstract: The suitability of the project delivery system (PDS) selected for a project greatly influences the efficiency to conduct a project. It is not an easy task to select an appropriate PDS as a large amount of ambiguous information exists. The paper aims to develop a PDS selection model to help owners to make a decision. The similar projects are identified through the similarity metrics between the target project to be predicted and those in the database. In addition, some of the indicator values are examined and modified through DEA-BND model, and then they are trained by ANN model to predict an appropriate PDS for the target project. A survey was conducted by postal questionnaire to empirically validate the reliability of the model in China. The indicator system for the selection of an appropriate PDS is established. Through the comparison of results predicted by different models, it is found that the PDS selection model developed in this paper can predict PDS precisely, and shows higher reliability than the ANN model. A PDS selection model is developed by inputting project-specific data, which proves to be more accurate and less dependent on experts’ judgment. Its practical application will benefit the owner’s decision making in the selection of the PDS. [ABSTRACT FROM AUTHOR]

The aim of this study is to present a new empirical model to predict thermal conductivity, based on experimental databases that might be particularly applicable to unsaturated weathered granite soils. To establish the experimental databases, a number of thermal conductivity tests were conducted using a new probe system combined with a volumetric pressure extractor. Thereby, 162 data were collected and applied to the prediction model. The prediction model has two empirical coefficients that determine the shape of a graph, and it is not easy to determine these coefficients unless experiments are conducted. Thus, in this paper, we propose using an artificial neural network model to obtain these empirical coefficients without experiments, given only information on the soil properties. Moreover, to evaluate the applicability of the trained network model, it was tested for data sets that had not been introduced during the training stage. According to the verification results, the trained network model presents reasonable prediction results (R 2 = 0.9046), even for the new testing data. In addition, the model traces the measured data curve with fairly good agreement, depending on a sample's porosity, regional characteristics, and degree of saturation. [ABSTRACT FROM AUTHOR]

This paper presents a 5-input artificial neural network (ANN) model for the prediction of the thermal conductivity ratio of nanofluids to the base fluid ( k nf / k f ) of various nanofluids based on water and ethylene glycol (EG) and a type of transformer oil. The studied nanofluids are Al 2 O 3 –Water, Al–Water, TiO 2 –Water, Cu–Water, Cuo–Water, ZrO 2 –Water, Al 2 O 3 –EG, Al–EG, Cu–EG, Cuo–EG, Mg(OH) 2 –EG, Al 2 O 3 –Oil, Al–Oil, Cuo–Oil and Cu–Oil (15 nanofluids). The network is designed and trained using a total of 776 experimental data points collected from 21 sources of experimental data available in the literature. Average diameter, volume fraction, thermal conductivity of nanoparticles and temperature as well as some appropriated numbers for both nanoparticle and base fluid are chosen as input variables of the network, whereas the corresponding value of ( k nf / k f ) is selected as its target. The developed optimal ANN model shows a reasonable agreement in predicting experimental data with mean absolute percent error of 1.26% and 1.44% and correlation coefficient of 0.995 and 0.993 for training and testing data sets, respectively. [ABSTRACT FROM AUTHOR]

Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers. [ABSTRACT FROM AUTHOR]

Solar energy generated by sunlight has a non-schedulable nature due to the stochastic environment of meteorological conditions. Hence, power system control and the energy business require the prediction of solar energy (radiation) from a few seconds up to one week in advance. To deal with prediction shortcomings, various solar radiation prediction methods have been used. Predictive data mining offers variety of methods for solar radiation predictions where artificial neural network is one of the reliable and accurate methods. A systematic review of literature was conducted and identified 24 papers that discuss artificial neural network for solar systems design and solar radiation prediction. The artificial neural network techniques were employed for designing solar systems and predicting solar radiations to assess current literature on the basis of prediction accuracy and inadequacies. Specific inclusion and exclusion criteria in two distinct rounds were applied to determine the most relevant studies for our research goal. Further, it is observed from the result of this study that artificial neural network gives good accuracy in terms of prediction error less than 20%. The accuracy of solar radiation prediction models is found to be dependent on input parameters and architecture type algorithms utilized. Therefore, artificial neural network as compared to other empirical models is capable to deal with many input meteorological parameters, which make it more accurate and reliable. [ABSTRACT FROM AUTHOR]

The increasing demand and growing pressure on natural aggregates necessitated recycling and reusing recycled concrete aggregate (RCA). The proper implementation of RCA for concrete production lags, possibly because there is currently no adequate stand-alone data to predict its quality. Consequently, many tons of RCA are accumulated as landfills and are a threat to the public. In an attempt to stop the recurrence of experimental studies and the wasting of scarce resources, the present study proposed statistical models for the evaluation of the compressive and split tensile strengths of the recycled aggregate concrete (RAC) comprising Lathe Waste Steel Fibre (LWSF), utilizing the Artificial Neural Network (ANN). Crushed granite (CG) was partially replaced with RCA from 0% to 100% in increments of 25% and LWSF as reinforcement at a constant amount of 1.5% by volume fraction. The fresh and hardened concrete's properties, such as workability, compressive and splitting tensile strengths, were studied. The results showed that 25% RCA with 1.5% LWSF (RACS1) increased the compressive strength and workability, while the split tensile strength reduced substantially. The ANN model was developed based on six input variables; namely: ordinary Portland cement (OPC), river sand (RS), CG, RCA, water-cement ratio (WC) and concrete age (CA), whereas the compressive and split tensile strengths were the response variables. The input data was learned, verified and validated using the feed-forward back-proportion approach for ANN. The most probable model architecture, comprising a six-input layer, twelve-hidden layer and two-output layer neurons, was selected based on acceptable results in terms of mean square error, MSE, after several trials. As a result, the selected ANN model was found to be capable of reproducing experimental results. [ABSTRACT FROM AUTHOR]

[Display omitted] • Adaptive model predictive control (AMPC) is proposed for household building control. • Energy flow models use artificial network and weighted moving average methodology. • A genetic algorithm performs the heat pump comfort and cost optimization. • MILP optimization is applied to achieve a cost-optimal energy strategy. • The building management performance is validated through a set of scenarios. • The electricity bill is reduced and the elasticity of demand is increased. This paper focuses on the optimisation of electricity consumption in residential buildings. To deal with the increase in electricity consumption, the intermittency of renewable energy generation and grid contingencies, a greater effort is required towards residential management optimisation. A novel adaptive model predictive control algorithm is proposed to achieve this objective. The challenges for this research included recognising and modelling the economic and technical constraints of the sources and appliances and addressing the uncertainties concerning the weather and user behaviour. Data-driven models are developed and trained to predict the user behaviour and buildings. Artificial neural networks and statistical models based on the weighted moving average are proposed to capture the patterns of deferrable and non-deferrable appliances, battery storage, electric vehicles, photovoltaic modules, buildings and grid connections. A dual optimisation method is devised to minimise the electricity bill and achieve thermal comfort. The proposed optimisation solver is a two-step optimisation method based on genetic algorithm and mixed integer linear programming. A comprehensive simulation study was carried out to reveal the effectiveness of the proposed method through a set of simulation scenarios. The results of the quantitative analysis undertaken as part of this study show the effectiveness of the proposed algorithm towards reducing electricity charges and improving grid elasticity. [ABSTRACT FROM AUTHOR]

The forecasting of drought based on cumulative influence of rainfall, temperature and evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture, ecosystems, wildlife, tourism, recreation, crop health and hydrologic engineering. Predictive models of drought indices help in assessing water scarcity situations, drought identification and severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network (ANN) as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index ( SPEI ) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006–2012. The predictive variables were: monthly rainfall totals, mean temperature, minimum temperature, maximum temperature and evapotranspiration, which were supplemented by large-scale climate indices (Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and Indian Ocean Dipole) and the Sea Surface Temperatures (Nino 3.0, 3.4 and 4.0). A total of 30 ANN models were developed with 3-layer ANN networks. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms were utilized to train the network, tangent and logarithmic sigmoid equations used as the activation functions and the linear, logarithmic and tangent sigmoid equations used as the output function. The best ANN architecture had 18 input neurons, 43 hidden neurons and 1 output neuron, trained using the Levenberg–Marquardt learning algorithm using tangent sigmoid equation as the activation and output functions. An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute Error ranging from 0.9945–0.9990, 0.0466–0.1117, and 0.0013–0.0130, respectively for individual stations. Also, the Willmott's Index of Agreement and the Nash–Sutcliffe Coefficient of Efficiency were between 0.932–0.959 and 0.977–0.998, respectively. When checked for the severity ( S ), duration ( D ) and peak intensity ( I ) of drought events determined from the simulated and observed SPEI , differences in drought parameters ranged from − 1.41–0.64%, − 2.17–1.92% and − 3.21–1.21%, respectively. Based on performance evaluation measures, we aver that the Artificial Neural Network model is a useful data-driven tool for forecasting monthly SPEI and its drought-related properties in the region of study. [ABSTRACT FROM AUTHOR]