Application Of Artificial Neural Networks Research Articles

Ann Prediction of Mechanical Properties of GGBFS and Alccofine Based High Strenth Self-Compacting Concrete

Abstract In this study, we use Artificial Neural Networks (ANN) and Multiple Regression Analysis to evaluate the prediction of two crucial self-compacting concrete properties: compressive strength and split tensile strength. It was possible to create four different datasets, each of which had different concrete mix proportions along with their respective ages in days, compressive strengths (MPa), and split tensile strengths (MPa). Separate ANN models and Regression models were trained and tested using these datasets. As a gauge of prediction accuracy, Mean Squared Error (MSE) was used to assess the performance of the models. This study offers insightful information on the application of multiple regression analysis and artificial neural networks to forecast the characteristics of self-compacting concrete using GGBS and Alccofine. Here Alccofine functions as an additive and GGBS acts as a partial substitute for cement at 0 to 60% with a fluctuation of 10%. The outcomes highlight the potential of neural networks as a tool for concrete mix design optimization and quality control since they can capture complex correlations between input variables and concrete strength.

Serving Up Success: Unveiling the Power of Machine Learning for Volleyball League Prediction

This study investigates the efficacy of Artificial Neural Networks (ANN) in predicting volleyball league standings, focusing on the Turkish Volleyball Federation's Sultanlar and Efeler leagues over five seasons (2018-19 to 2022-23). Given the complexity and volume of performance data in volleyball, traditional analysis methods often face challenges such as data overload and high operational costs. ANN models, known for their ability to learn from and generalize data, present a promising solution to these challenges. By analyzing 23 input variables related to match performance, including points scored, services, attacks, and blocks, this study aims to identify the most influential factors on final league standings and provide a more objective, rapid, and economical analysis method. The results indicate significant potential for ANN in sports analytics, demonstrating high accuracy rates in predictions, especially for the Sultanlar League. However, the study also acknowledges limitations such as data quality and model complexity, suggesting areas for future research to enhance predictive accuracy and applicability of ANN in volleyball and other sports analytics.

Modulating Neuromorphic Behavior of Organic Synaptic Electrolyte-Gated Transistors Through Microstructure Engineering and Potential Applications.

Organic synaptic transistors are a promising technology for advanced electronic devices with simultaneous computing and memory functions and for the application of artificial neural networks. In this study, the neuromorphic electrical characteristics of organic synaptic electrolyte-gated transistors are correlated with the microstructural and interfacial properties of the active layers. This is accomplished by utilizing a semiconducting/insulating polyblend-based pseudobilayer with embedded source and drain electrodes, referred to as PB-ESD architecture. Three variations of poly(3-hexylthiophene) (P3HT)/poly(methyl methacrylate) (PMMA) PB-ESD-based organic synaptic transistors are fabricated, each exhibiting distinct microstructures and electrical characteristics, thus serving excellent samples for exploring the critical factors influencing neuro-electrical properties. Poor microstructures of P3HT within the active layer and a flat active layer/ion-gel interface correspond to typical neuromorphic behaviors such as potentiated excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-term potentiation (STP). Conversely, superior microstructures of P3HT and a rough active layer/ion-gel interface correspond to significantly higher channel conductance and enhanced EPSC and PPF characteristics as well as long-term potentiation behavior. Such devices were further applied to the simulation of neural networks, which produced a good recognition accuracy. However, excessive PMMA penetration into the P3HT conducting channel leads to features of a depressed EPSC and paired-pulse depression, which are uncommon in organic synaptic transistors. The inclusion of a second gate electrode enables the as-prepared organic synaptic transistors to function as two-input synaptic logic gates, performing various logical operations and effectively mimicking neural modulation functions. Microstructure and interface engineering is an effective method to modulate the neuromorphic behavior of organic synaptic transistors and advance the development of bionic artificial neural networks.

Predictive Insight into Tailings Flowability at Their Disposal Using Operating Data-Driven Artificial Neural Network (ANN) Technique

This study investigates the application of artificial neural networks (ANNs) in predicting the flowability of mining tailings based on operational variables. As the mining industry seeks to enhance operations with complex ores, the constant improvement and optimization of mineral waste management are crucial. The flowability of tailings was investigated with data driven by properties such as particle-size distribution, water content, compaction capacity, and viscoelastic characteristics that can directly affect stacking, water recovery capabilities, and stability at disposal, influencing storage capacity, operational continuity, and work safety. There was a strong correlation between water content and tailings flowability, emphasising its importance in operational transport and deposition. Three ANN models were evaluated to predict tailings flowability across three and five categories, where a model based on thickening operational variables, including yield stress and turbidity, demonstrated the highest accuracy, achieving up to 94.4% in three categories and 88.9% in five categories. Key variables such as flocculant dosage, water content, yield stress, and solid concentration were identified as crucial for prediction accuracy The findings suggest that ANN models, even with limited datasets, can provide reliable flowability predictions, supporting tailings management and operational decision-making.

Preparation of expandable polystyrene/acrylonitrile-butadiene-styrene by multi-stage initiator dosing with the application of artificial neural networks

By increasing usage of polymers, environmental problems for them increase. The polymers that after usage, contribute to these issues, their preparation process may generate wastage, that affects production. In the production of expandable polystyrene (EPS), several batches on batch were discharged due to disruption in suspension, and the presence of these wastages in the production place can cause problems. This study tries to introduce a new polymer with special features by mixing different percentages of this polymer (10, 20, 30) with ABS polymer. According to the fact that disarray can occur in 60, 63, 66, and 69% of the polymerization, the polymerization continued until those percentages and then mixed with ABS. Different tests (like: Elongation at break, yellow index, Modular melt flow, Impact test, and Vicat softening temperature) was performed for mixed polymer. The results show that in some materials the quality of the mixed polymer may decrease but it is still in an acceptable range. The Multi-Layer Perceptron method of Artificial Neural Networks has been used in order to simulate data that has not been tested.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

Appling machine learning for estimating total suspended solids in BFT aquaculture system

Biofloc Technology (BFT) systems are used to improve water quality and the production of aquatic organisms, and they influence dissolved oxygen, alkalinity, and pH, directly affecting the efficiency and success of this production system. Measuring total suspended solids (TSS) in water demands substantial investments and involves a time-consuming process to obtain results. This delay in obtaining results poses a significant challenge to the operations of these farms. In this study, we applied Artificial Neural Networks (ANN) and Support Vector Machine (SVM) methods based on artificial intelligence and water quality parameters (easy to measure, low cost, and quick response) to identify the most accurate method for measuring TSS. The best TSS estimate was achieved with SVM using nitrite and turbidity as predictive variables, which tended to overestimate the real value by 19 %, presenting a potential for application in estimating TSS in the BFT aquaculture system.

Application of Artificial Neural Networks for Recovery of Cu from Electronic Waste by Dynamic Acid Leaching: A Sustainable Approach

Application of Artificial Neural Networks for Recovery of Cu from Electronic Waste by Dynamic Acid Leaching: A Sustainable Approach

On the choice of physical constraints in artificial neural networks for predicting flow fields

The application of Artificial Neural Networks (ANNs) has been extensively investigated for fluid dynamic problems. A specific form of ANNs are Physics-Informed Neural Networks (PINNs). They incorporate physical laws in the training and have increasingly been explored in the last few years. In this work, the prediction accuracy of PINNs is compared with that of conventional Deep Neural Networks (DNNs). The accuracy of a DNN depends on the amount of data provided for training. The change in prediction accuracy of PINNs and DNNs is assessed using a varying amount of training data. To ensure the correctness of the training data, they are obtained from analytical and numerical solutions of classical problems in fluid mechanics. The objective of this work is to quantify the fraction of training data relative to the maximum number of data points available in the computational domain, such that the accuracy gained with PINNs justifies the increased computational cost. Furthermore, the effects of the location of sampling points in the computational domain and noise in training data are analyzed. In the considered problems, it is found that PINNs outperform DNNs when the sampling points are positioned in the Regions of Interest. PINNs for predicting potential flow around a Rankine oval have shown a better robustness against noise in training data compared to DNNs. Both models show higher prediction accuracy when sampling points are randomly positioned in the flow domain as compared to a prescribed distribution of sampling points. The findings reveal new insights on the strategies to massively improve the prediction capabilities of PINNs with respect to DNNs.

Intelligent computing for the electro-osmotically modulated peristaltic pumping of blood-based nanofluid

The study delves into a novel application of artificial neural networks within the biomedical realm, specifically focusing on the peristaltic pumping of ionized blood-infused nanofluid using the Casson model. The investigation takes into account the impacts of electrokinetic forces, chemical reaction, thermal radiation and variable thermal conductivity. The endorsed nanofluid model encompasses both thermophoretic and Brownian diffusions. The Nernst-Planck and Poisson equations are used to characterize electroosmotic process. The mathematical model is then numerically solved using NDSolve in Mathematica. The variation in relevant hemodynamic characteristics concerning key flow parameters is explored through numerous graphical representations. Two distinct artificial neural networks have been developed to estimate values for rates of mass and heat transfer. The Levenberg-Marquardt algorithm is used as the training algorithm in network models with multi-layer perceptron architecture. Graphical inspection of the results demonstrates a decrease in blood flow with an elevated Helmholtz-Smoluchowski velocity. A lower pressure gradient is recorded with a higher electroosmotic parameter. Furthermore, an augmentation in the thermal conductivity parameter leads to a reduction in heat transfer rate at the wall. The concentration profile demonstrates an increase with a rise in the chemical reaction parameter. The results obtained from the neural network models exhibit an ideal harmony with the target values. The developed neural network models demonstrated the ability to predict rates of heat and mass transfer values with average deviations of −0.01% and 0.004%, respectively.

Network properties determine neural network performance

Machine learning influences numerous aspects of modern society, empowers new technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products such as smartphones and self-driving cars. Despite the vital role and broad applications of artificial neural networks, we lack systematic approaches, such as network science, to understand their underlying mechanism. The difficulty is rooted in many possible model configurations, each with different hyper-parameters and weighted architectures determined by noisy data. We bridge the gap by developing a mathematical framework that maps the neural network’s performance to the network characters of the line graph governed by the edge dynamics of stochastic gradient descent differential equations. This framework enables us to derive a neural capacitance metric to universally capture a model’s generalization capability on a downstream task and predict model performance using only early training results. The numerical results on 17 pre-trained ImageNet models across five benchmark datasets and one NAS benchmark indicate that our neural capacitance metric is a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods.

USING ARTIFICIAL NEURAL NETWORKS TO IMPROVE THE EFFICIENCY OF TRANSFORMERS USED IN WIRELESS POWER TRANSMISSION SYSTEMS FOR DIFFERENT COIL POSITIONS

This study uses magnetic resonance-based coupling theory to study the various placements of transmitter and receiver coils in wireless power transfer (WPT) systems. Various coil placements are examined to show where high efficiency can be achieved within the air gap. Basic characteristics such as self-inductance, mutual inductance, and coupling coefficient were calculated. Artificial neural networks (ANNs) in WPT are a powerful technique for predicting performance characteristics. Using ANNs provides an excellent method for streamlining the design process and reducing time-consuming calculations. To quickly determine and optimize coil design, this study compares recent research on ANN applications in WPT and the performance of different types of ANNs in WPT systems. An artificial neural network (ANN) was trained to predict the magnetic properties of a wireless power transfer (WPT) device. Appropriate cost functions have been implemented to train the ANN properly. It was shown that the trained ANN can effectively reproduce the data obtained by the finite element method (FEM). The results show an effective power transmission at different coil placements, with decreased efficiency observed after a certain distance. These data will help determine the proposed WPT system's air gap and angular limits.

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg–Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e−05 for the hydrogen mole fraction and 4.88e−05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

A study on the application of artificial neural network to predict k-eff and peaking factor of a small modular PWR

Machine learning (ML) using artificial neural network (ANN) methods is being applied to predict required parameters for nuclear reactors based on learning from big data sets. The ML models usually give faster calculation speed while the accuracy is good agreement with physical simulators. In this work, a multi-layer perceptron network was built and trained to predict k-eff and peaking factor of a small modular pressurized water reactor (PWR). The results are compared with those obtained by using a reactor physics code system, i.e. SRAC2006. The comparison shows good agreement accuracy and higher performance of the ML models.

Application of artificial neural networks for indirect load measurements on bridges from vibration measurements

The German Research Foundation (DFG) has launched in September 2022 the priority program SPP 2388 100+ to develop new methods for digital representation, SHM and lifetime management of complex structures, due to the continuously increasing amount of old infrastructure buildings. The present contribution is prepared within the LEMOTRA project as a part of SPP 2388 100+. The data assimilation techniques present a potential SHM approach and presume a permanent update of a computational model by use of continuous measurements. System changes resulting from damage or aging processes can be detected and localized, provided the measurement and model prediction share the same cause. Thus, the load identification is a necessary prerequisite for data assimilation methods. In this paper an approach using artificial neural networks is developed to determine the main parameters of the traffic load associated with vehicles crossing a bridge. Simple feed forward neural networks have already been used for predicting structural displacements or discovering system anomalies. However, a more sophisticated network structure is necessary for the purpose of extracting features from time history measurement data. Therefore, a cluster structure of convolutional neural networks (CNN) was developed to gain knowledge about load characteristics such as load magnitudes, load velocities or the number of vehicles on the bridge. The developed approach has been tested on a numerical example. For this purpose, a finite element model of a bridge is created and loaded by various moving loads with variable relevant parameters. Simulated acceleration data at multiple locations on the bridge are considered as artificial measurements and then used in the training process. The impact of different measurement noise levels on the quality of the CNNs is being investigated as well. The obtained results allow for some conclusions on advantages and drawbacks which should be a matter of discussion during the conference.

A Study on XANNs for Analyzing Failures in Guided Ultrasonic Wave-based Damage Localization

In the field of Structural Health Monitoring (SHM) using Guided Ultrasonic Waves (GUW), machine learning (ML) approaches for data analysis are becoming increasingly popular. Especially, deep learning with artificial neural networks (ANN) has proven effective due to its ability to process a large amount of data. ANN are commonly employed in damage localization tasks, where, with appropriate training, they deliver good results. However, a significant challenge persists in the application of ANN, namely, their "black-box" nature, which hinders the investigation of decision processes within the ANN and makes it difficult to understand why an ANN model fails under certain inputs. In the present work, an already introduced explainable artificial neural network (XANN) approach will be utilized to investigate when and why a trained XANN model fails with given inputs. The Open Guided Wave (GUW) platform's data is employed to train and test a XANN model designed to predict the location of artificial damage. Subsequently, individual inputs are modified (simulating malfunctions in specific sensor paths), and the impact on decision processes within the XANN architecture is examined. The aim of this study is to investigate and enhance the resilience and explainability of ANN approaches in the SHM domain.

MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA

Roundabouts, as an unsignalized intersection, have an effective preventative measure designed to control straight-line crashes. Efficient traffic flow in cities depends upon appropriate capacity estimation of roundabouts. This study attempts to develop models for roundabout entry capacity by applying Artificial Neural Network (ANN) analysis for mixed traffic flow conditions. Data was gathered from 27 roundabouts spread across India. The influence area for gap acceptance (INAGA) concept was used as a graphical method to identify critical gap (Tc) of entry flow at roundabouts. This study indicated that the Bayesian Regularisation Neural Network (BRNN) based model has the best R2 and RMSE of 0.97 and 167.8. The connection weight approach and Garson algorithm evaluate the significance of each explanatory variable and identify follow-up time (Tf) as a critical parameter with values of 11.10 and 21.15%, respectively.

Quality Enhancement of MIROS Wave Radar Data at Ieodo Ocean Research Station Using ANN

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

Optimized artificial neural network application for estimating oil recovery factor of solution gas drive sandstone reservoirs

The most crucial aspect in determining field development plans is the oil recovery factor (RF). However, RF has a complex relationship with the reservoir rock and fluid properties. The application of artificial neural networks is able to produce complex correlations between reservoir parameters that affect the recovery factor. This research provides a new approach to improve the accuracy of the ANN model in the form of steps including removing outlier data, selecting input parameters, selecting transferring functions, selecting the number of neurons, and determining hidden layers. By applying these steps, an ANN model was selected with nine input parameters consisting of oil viscosity, water saturation, initial oil formation volume factor, formation thickness, initial pressure, permeability, specific gravity of oil, porosity, and original oil in place. Furthermore, based on the correlation coefficient, a tangent sigmoid transferring function, 30 neurons, and two hidden layers were determined. The proposed ANN correlation gives the best accuracy compared to the previous correlations. This is proved by the highest correlation coefficient of 0.91657.

Neural networks for critical load factors of unstiffened plates according to the reduced stresses method of EN 1993-1-5

The critical load factor resulting from the complete stress field is the main input parameter for the verification against plate buckling according to the reduced stresses method (RSM) from Eurocode EN 1993-1-5. Currently, the code provides just a simple and often conservative interaction formula with a limited application range. Realistic values can only be obtained by numerical methods, like the FEM, which is tedious for many load cases. To enhance the ease of use of the RSM, simple-to-use but accurate methods are needed, which can be automated and implemented into EN 1993-1-5. The current research deals with this problem by applying artificial neural networks (ANN) to predict the critical load factors of unstiffened plates under longitudinal, shear, and patch loading stresses, including their interaction behaviour. ANNs were used because approximated solutions with polynomials (as actual in the standard) are not suitable due to the complexity of the problem. The developed ANNs are highly accurate (as shown by an independent verification), can be implemented into the Eurocode, and can easily be used, even without in-depth programming skills. Moreover, a simple Python code is provided to demonstrate the ease of use. Still, the code can also be implemented into other software packages (like Excel), as only basic matrix operations are needed. The interaction surfaces are visualized, gaining a deeper understanding of plates under multiaxial loading situations. Finally, recommendations are given for generating ANNs for plate buckling problems.