Artificial Neural Network Approach Research Articles

Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches

Accurately predicting the Modulus of Resilience (MR) of subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques for determining MR are often costly and time-consuming. This study explores the efficacy of Genetic Programming (GEP), Multi-Expression Programming (MEP), and Artificial Neural Networks (ANN) in forecasting MR using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that the GEP model consistently outperforms MEP and ANN models, demonstrating the lowest error metrics and highest correlation indices (R2). During training, the GEP model achieved an R2 value of 0.996, surpassing the MEP (R2 = 0.97) and ANN (R2 = 0.95) models. Sensitivity and SHAP (SHapley Additive exPlanations) analysis were also performed to gain insights into input parameter significance. Sensitivity analysis revealed that confining stress (21.6%) and dry density (26.89%) are the most influential parameters in predicting MR. SHAP analysis corroborated these findings, highlighting the critical impact of these parameters on model predictions. This study underscores the reliability of GEP as a robust tool for precise MR prediction in subgrade soil applications, providing valuable insights into model performance and parameter significance across various machine-learning (ML) approaches.

Advancing software reliability with time series insights: A non‐autoregressive ANN approach

AbstractSoftware reliability is a critical factor in assessing the health of software and identifying defects. Software reliability growth models (SRGM) are used to estimate the occurrence of software faults. There are various parameterized and non‐parameterized models of SRGM. These models effectively predict fault occurrence for limited testing conditions. To resolve this problem various neural and artificial neural network (ANN) models are proposed. A problem while using ANN is over‐fitting and under‐fitting. Non‐autoregressive time series models, including ANN variants, offer promising solutions to address under‐fitting issues in SRGM, providing enhanced predictive capabilities for fault occurrence across diverse testing conditions. This study proposes a modified version with a Bayesian regularization technique to address over‐fitting. This modification aims to enhance the suitability of the Bayesian regularization framework for nonlinear autoregressive (NAR) models by carefully adjusting regularization parameters. Comprehensive testing with real‐world software failure datasets is conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that our modified approach improved generalization capabilities and increased prediction accuracy. The NAR‐ANN model exhibits a lower mean squared error of 0.12935 and a higher value of 0.99853.

Direct and inverse simulations of hydrodynamic and thermal characteristics in a room with random boundary conditions by feedforward neural network modelling

Artificial Intelligence (AI) or Neural Network (NN) approaches are starting to be used in built environment areas. This study demonstrates that feedforward neural network (FNN) modelling is capable of simulating the hydrodynamic and thermal characteristics of a room with a radiant heating and cooling system. The FNN model was trained and tested to simulate air velocity and temperature profiles based on boundary conditions as direct simulation and to restore boundary conditions as inverse simulation, using a scaled and normalised database generated by a Computational Fluid Dynamics (CFD) model. In the CFD model, the non-dimensional form of the governing equations, including the radiative transfer equation (RTE) and vorticity equation, were solved using the discrete ordinate method (DOM) and finite difference method (FDM) respectively. The impacts of training database size, hidden layers, and the number of neurons in each layer on the accuracy of the FNN results in the direct and inverse simulations were evaluated in terms of the average root mean square error (RMSE) over the testing data. In the direct simulation, using a smaller CFD database with more hidden layers and neurons achieved comparable accuracy to an FNN trained with a larger CFD database and a less complex FNN. Furthermore, in the inverse simulation, where the FNN model was trained to use a subdomain of temperature profiles to simulate or restore the boundary conditions, the effect of the size of the subdomain on the accuracy in terms of RMSE was studied. It demonstrated that the FNN approach could conduct inverse simulations that are typically beyond the capability of conventional CFD modelling approaches. The results show that the boundary conditions could be accurately restored using subdomains of the temperature profile, covering approximately 60% of the main domain.

Study of factors affecting the magnetic sensing capability of shape memory alloys for non-destructive evaluation of cracks in concrete: Using response surface methodology (RSM) and artificial neural network (ANN) approaches

Currently, the field of structural health monitoring (SHM) is focused on investigating non-destructive evaluation techniques for the identification of damages in concrete structures. Magnetic sensing has particularly gained attention among the innovative non-destructive evaluation techniques. Recently, the embedded magnetic shape memory alloy (MSMA) wire has been introduced for the evaluation of cracks in concrete components through magnetic sensing techniques while providing reinforcement as well. However, the available research in this regard is very scarce. This study has focused on the analyses of parameters affecting the magnetic sensing capability of embedded MSMA wire for crack detection in concrete beams. The response surface methodology (RSM) and artificial neural network (ANN) models have been used to analyse the magnetic sensing parameters for the first time. The models were trained using the experimental data obtained through literature. The models aimed to predict the alteration in magnetic flux created by a concrete beam that has a 1 mm wide embedded MSMA wire after experiencing a fracture or crack. The results showed that the change in magnetic flux was affected by the position of the wire and the position of the crack with respect to the position of the magnet in the concrete beam. RSM optimisation results showed that maximum change in magnetic flux was obtained when the wire was placed at a depth of 17.5 mm from the top surface of the concrete beam, and a crack was present at an axial distance of 8.50 mm from the permanent magnet. The change in magnetic flux was 9.50 % considering the aforementioned parameters. However, the ANN prediction results showed that the optimal wire and crack position were 10 mm and 1.1 mm, respectively. The results suggested that a larger beam requires a larger diameter of MSMA wire or multiple sensors and magnets for crack detection in concrete beams.

Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater

The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN > RF > SVM > Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better.Highlights• In-depth analysis of various artificial intelligence optimization techniques.• Prediction efficiency of artificial intelligence and machine learning algorithms.• 98% dye removal and 100% regeneration of graphite intercalation compound.• Advanced statistical analysis of targeted responses and data fitting techniques.• Analysis of uncertainties and variability using Monte Carlo simulation.

FAULT DETECTION AND CLASSIFICATION USING DENSENET, SURF-BASED ANN, AND INFRARED THERMOGRAPHY

The reliability and efficiency of electric motors are critical in industrial applications, where unexpected faults can lead to costly downtime and safety hazards. Traditional fault detection methods often require extensive manual inspection and may not capture subtle anomalies in motor behavior. Infrared thermography has emerged as a non-invasive technique to detect temperature variations in motor components, which can indicate potential faults. However, the challenge lies in accurately classifying these faults to prevent failures. Current methods lack the precision needed to classify various motor faults accurately and quickly, especially when dealing with complex thermal patterns. The integration of advanced deep learning architectures with feature extraction techniques presents an opportunity to enhance the detection and classification of motor faults. This study proposes a hybrid model combining DenseNet, a deep learning architecture known for its high performance in image analysis, with a Speeded-Up Robust Features (SURF)-based Artificial Neural Network (ANN) for feature extraction and classification. Infrared thermography images of motors were first processed through DenseNet for initial feature extraction. The SURF algorithm further refined these features, which were then classified using ANN. The model was trained and validated on a dataset of infrared thermography images, representing various motor fault conditions, including bearing wear, misalignment, and insulation failure. The proposed model achieved an overall accuracy of 98.7% in detecting and classifying motor faults, outperforming traditional methods by 5.2%. The model also demonstrated high sensitivity (97.8%) and specificity (99.1%) in identifying subtle temperature variations indicative of early-stage faults. These results highlight the effectiveness of the DenseNet and SURF-based ANN approach in enhancing the reliability of motor fault detection using infrared thermography.

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM10 and SO2 pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg-Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis (N = 181), sinusitis (N = 83), and upper respiratory infections (N = 194). The study also found demographic differences, with females and people aged 18 to 65years having greater admission rates. The performance of the ANN model, measured using R2 values, demonstrated a high level of predictive accuracy. Specifically, the R2 value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors.

Examining Hybrid Nanofluid Flow Dynamics in the Conical Gap between a Rotating Disk and Cone Surface: An Artificial Neural Network Approach

To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg–Marquardt neural networks (LM-NNs). A unique hybrid nanoliquid comprising aluminum oxide, Al2O3, nanoparticles and copper, Cu, nanoparticles is specifically addressed. Through the application of similarity transformations, the mathematical model formulated in terms of partial differential equations (PDEs) is converted into ordinary differential equations (ODEs). The BVP4C method is employed to generate a dataset encompassing various TRHNF-DC scenarios by varying magnetic parameters and nanoparticles. Subsequently, the intelligent LM-NN solver is trained, tested, and validated to ascertain the TRHNF-DC solution under diverse conditions. The accuracy of the LM-NN approach in solving the TRHNF-DC model is verified through different analyses, demonstrating a high level of accuracy, with discrepancies ranging from 10−10 to 10−8 when compared with standard solutions. The efficacy of the framework is further underscored by the close agreement of recommended outcomes with reference solutions, thereby validating its integrity.

3D criteria to improve real-time pressure control in water distribution network system architecture

ABSTRACT Real-time control (RTC) methods have been developed for over one decade to regulate service pressure and reduce leakage using pressure control valves in water distribution networks (WDN). The present study investigates control node selection frameworks and RTC system architecture for the field-oriented control of a rural WDN. The unique topology of the case-study area, the small size of the area, and high-pressure variety increased the considerations of the RTC system. A computer code was developed based on the method of characteristics for the hydraulic simulation of the network. The code could analyze unsteady flows through sloped pipes, implement pressure-based analysis, and regulate control valves based on the target node. Leakage reduction, pressure fluctuation reduction, and cavitation prevention were used as three criteria and constraints in the selection of control nodes. It was found that the optimal strategy would reduce the real water loss from 25 to 10%. Furthermore, key parameters in remote sensing were evaluated to minimize the number of sensors in order to further simplify the control algorithm and RTC system architecture through an artificial neural network (ANN) approach. The control function with a convergence rate of 99% was introduced.

Concrete compressive strength prediction by artificial neural network approach

Concrete compressive strength prediction by artificial neural network approach

Mindfully fashioned: Sustaining style through product value retention

In the ever-evolving landscape of fashion industry, the pursuit of sustainability and mindful consumption has emerged as an imperative. This study presents an innovative and integrated framework that amalgamates the Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) approach. This comprehensive framework explores the dynamic interplay between Sustainable Mindfulness (SM), Product Value Retention (PVR), Brand Loyalty (BL) and Circular Practices (CP) in the fashion domain. The study underscores Theory of Planned Behavior (TPB) as the theoretical foundation, shedding light on how attitudes, subjective norms, and perceived behavioral control shape SM and, subsequently, PVR. This study explores into the multidimensional aspects of sustainable fashion, enabling a holistic understanding of the nuanced relationships between PVR strategies, and sustainable consumption attitudes. The study sets the stage for a more harmonious coexistence between style and sustainability, charting a course for the fashion industry that values mindfulness, longevity, and environmental responsibility. The findings offer actionable insights for fashion managers, emphasizing strategic approaches to enhance sustainability, consumer awareness, and circular initiatives in the industry. This study not only advances theoretical discourse but also offers actionable insights for fashion brands and policymakers aiming to foster SM among diverse consumer segments. It sets the stage for a more harmonious coexistence between style and sustainability, charting a course for the fashion industry that values mindfulness, longevity, and environmental responsibility.

Study on material behaviours of additively manufactured high-impact polystyrene using artificial neural networks

AbstractFused Filament Fabrication (FFF), a process parameters-dependent manufacturing method, currently dominates the additive manufacturing (AM) sector because of its prominent ability to produce parts with intricate profiles, customise products, and minimise waste. Though the effects of FFF process parameters were investigated experimentally, recent research highlighted the importance of developing numerical modelling and computational methods on optimising the FFF printing process and FFF-printed materials. This study aims to investigate the tensile strength (TS) of FFF-printed high-impact polystyrene (HIPS) via devising a systematic testing and analysis framework, which combines experimental testing, representative volume element (RVE)-finite element method (FEM), rule of mixture (ROM), and artificial neural networks (ANN). HIPS samples are fabricated using FFF considering the variations of infill density, layer thickness, nozzle temperature, raster angle, and build orientation, and tested with standard tensile testing. The rule of mixtures (ROM) and its modified version (MROM) are employed to calculate the TS of longitudinally and transversely built samples at various infill densities, respectively, while an ANN model is constructed to investigate the effect of material anisotropy precisely. The optimal ANN architecture is built with five hidden layers with the number of neurons in each layer as 44, 82, 169, 362, and 50. Although both MROM and ANN perform well on the validation set, ANN exhibits superior accuracy with only a maximum error of 0.13% for training set and 11% for validation set. The combination of the RVE-FEM, MROM, and ANN approaches can significantly improve the FFF printing process of polymers for optimisation.

Modeling of textile dye removal from aqueous solution with a Zr-MOF framework : design of experiments and artificial neural network approaches

Modeling of textile dye removal from aqueous solution with a Zr-MOF framework : design of experiments and artificial neural network approaches

An Artificial Neural Network Approach for Predicting TOC and Comprehensive Pyrolysis Parameters from Well Logs and Applications to Source Rock Evaluation

An Artificial Neural Network Approach for Predicting TOC and Comprehensive Pyrolysis Parameters from Well Logs and Applications to Source Rock Evaluation

Heat transfer evaluation of combined nanofluids in vertical annulus: An experimental and artificial neural network approach

Enhancing the thermal properties of the working fluid is a practical way to improve heat transfer performance. One of the suggested fluids for this purpose is nanofluids, which are suspensions of nanoparticles within a base fluid. Nanofluids have the potential to operate as a primary coolant or as an emergency coolant in a nuclear reactor. This paper investigates the thermal performance of a homogeneous hybrid nanofluid of Al2O3 and TiO2 in deionized water. The thermo-hydraulic performance of the nanofluid is analyzed in a high-pressure test loop capable of 25 bar pressure. The test section has an annular geometry, generating a cosine heat flux pattern. This flux pattern is representative of nuclear reactor cores. The experimental study is conducted to investigate the effects of nanoparticle concentration and Reynolds number on the heat transfer performance of nanofluids. The results show that increasing these two parameters significantly improved heat transfer. Specifically, raising the nanoparticle concentration from 0% to 1% leads to a reduction in surface temperature of 19% and 8% at Reynolds numbers of 825 and 4128, respectively. The calculated Nusselt numbers are compared with correlations from previous studies. Furthermore, an artificial neural network model is developed to predict heat transfer data for the studied system. The model consists of a single-layer feed-forward neural network trained with the stochastic gradient descent algorithm. To determine the optimal network, the number of neurons is optimized and evaluated based on the network's predictability. The neural network model demonstrates substantial improvements over previous experimental correlations (MAPE <2.5% and MSE <0.001).

Artificial Intelligence Predictions of Biomass Power of an Installed Waste Water Treatment Plant

In the current study, the power generations obtained from gas turbines of an installed waste water treatment plant were predicted, utilizing artificial intelligence method consisting of artificial neural network (ANN). In this regards, a cumulative of 445 data, found in the power generation data cluster and found in the physical and chemical data clusters has been used in the predictions based on the artificial intelligence association method. Each instant data of these total 445 data corresponds to daily average power generation (P) obtained from gas turbines of the facility and corresponds to physical and chemical parameters including the temperature (T), degree of acidity (pH), conductivity (σ), as well as the daily total volumetric flow of the waste gas to be burned at the gas generator (Q). Accordingly, the best prediction obtained by ANN approach was concluded to generate the statistical accuracy results corresponding to 6.1279% mean absolute percentage error (MAPE), 2.1540 MWh/day root mean square error (RMSE), and 0.9730 correlation coefficient (R) for power generation parameter.

Predicting project duration using a coupled artificial neural network and Taguchi method approach

Accurate project duration prediction is increasingly important for management because it defines the expected timeline for project realization. This study utilizes an integrated approach combining neural networks with the Taguchi method to forecast the time required to complete projects within predetermined deadlines. The methodology involves modelling and simulating the network of project activities to estimate the total average project duration. ration. The neural network model uses input variables such as success probability, effort, and learning factor to predict the total time necessary for project completion. The total average project duration is the output variable that is critical during the design phase. Subsequently, the Taguchi method optimizes the neural network’s outputs, incorporating mean squared error (MSE) values to enhance predictive accuracy. accuracy. This study underscores the efficacy of artificial neural networks (ANNs) as predictive tools, demonstrating their ability to meticulously estimate project duration. The proposed method’s efficiency and applicability are demonstrated by MATLAB simulation analyses, highlighting its effectiveness in precise deadline estimation. In the realm of engineering, ANNs stand tall as formidable predictive tools, their efficacy underscored by this study’s successful application. By harnessing ANNs and simulation data, this research crafts a predictive model adept at estimating the average total duration of projects. Through meticulous consideration of crucial parameters like the probability of success and effort factors, the model emerges as a beacon of accuracy within the design domain. Future research will explore the effects of additional parameters on activity networks and alternative transfer functions, as well as the potential integration of reinforcement algorithms to improve resource allocation, risk management, and project outcomes through online training data.

Predictive modeling of allowable storage time of finger millet grains using artificial neural network and support vector regression approaches

The study aimed to establish safe storage guidelines for long-term preservation of finger millet grains and to develop a model for predicting the allowable storage time. The experiment was conducted at various temperature (15, 25, 35 and 45 °C) and moisture contents (8, 11, 14, 17 and 20% wb). Changes in response variables such as germination, free fatty acid and mold growth were systematically monitored. Finger millet with a moisture content above 14% should be dried to safe moisture levels within 3–5 weeks at 30 °C or within 5–10 weeks at 15 °C to preserve grain quality. To maintain high quality and seed viability of finger millet, moisture content and storage temperature should be below 12% and 20 °C, respectively for up to 34 weeks. The study also assessed the use of artificial neural network and support vector regression models in predicting the safe storage period for finger millet grains.

Artificial neural network approach for predicting the mechanical properties of Al7475/Flyash/SiC hybrid composite

Artificial neural network approach for predicting the mechanical properties of Al7475/Flyash/SiC hybrid composite

ANN‐based analysis of the effect of SCM on recycled aggregate concrete

AbstractRising environmental awareness has prompted in‐depth studies on how the concrete industry affects the environment. Using recycled concrete aggregates (RCAs) and supplementary cementitious materials (SCMs) in concrete manufacturing provides advantages for sustainability. However, the broader chemical composition of SCMs and the inferior qualities of RCAs compared with natural aggregates (NAs) often lead to a decrease in concrete mechanical strength. The difficulty lies in foreseeing how the inclusion of SCMs and RCAs will affect the concrete compressive strength. The artificial neural network (ANN) approach presented herein can precisely forecast the recycled aggregate concrete (RAC) compressive strength, even when incorporates SCMs. The analysis employing the connection weight approach (CWA) determines how input variables influence compressive strength. Results indicate silica fume contributes most to compressive strength, followed by cement content, silica modulus, fine natural aggregate dosage, and coarse natural aggregate. Additionally, the amount of water utilized, the water/cement ratio, and the presence of RCA are all detrimental to compressive strength. The adverse effect of the cementitious materials' alumina modulus can be attributed to increased water demand during their reaction. Performance metrics of the final ANN model on the testing data subset include R2 = 0.94, and RMSE = 3.11, utilizing 834 data observations after outlier treatment for training and validation purposes. In summary, the ANN‐based approach demonstrates its efficacy in predicting concrete compressive strength when incorporating SCMs and RCAs, shedding light on the influential factors in concrete performance.