Adaptive Neuro-fuzzy Inference System Model Research Articles

Computational and intelligence modeling analysis of pharmaceutical freeze drying for prediction of temperature in the process

Accurate temperature prediction is crucial for various scientific and engineering applications, yet it remains challenging due to the complex relationships between spatial coordinates and temperature variations. This study addresses this challenge by exploring advanced machine learning models to improve prediction accuracy, filling the research gap in optimizing predictive models for temperature estimation in freeze drying of pharmaceuticals. We employed Support Vector Regression (SVR), Poisson Regression (POR), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, each enhanced using the Cheetah Optimizer (CO), to estimate temperature based on spatial coordinates (X, Y, Z) in the domain of process. The methodology involved training and testing these models on a dataset comprising spatial and temperature data, with a focus on improving accuracy through optimization techniques. Validation of the models showed that the CO-SVR model outperformed the others, achieving an R2 score of 0.953, Mean Squared Error (MSE) of 9.893, and Mean Absolute Error (MAE) of 2.549. This represents a significant improvement over the CO-ANFIS model, which obtained an R2 of 0.863, MSE of 23.583, and MAE of 3.912. The CO-POR model showed the lowest predictive capability, with an R2 score of 0.753, MSE of 57.608, and MAE of 6.756. These findings underscore the effectiveness of the Cheetah Optimizer in enhancing the accuracy of SVR models for temperature prediction, suggesting its potential for broader applications in predictive modeling where accuracy is paramount.

Hybrid intelligent framework for designing band gap-rich 2D metamaterials

An artificial intelligence machine learning-based design framework is proposed to design lattice-based metamaterials with hexagonal symmetry that deliver wide band gaps at user-desired frequency ranges between 0 and 1000 kHz. The design approach starts by selecting a traditional, easy-to-manufacture parent lattice-based material that does not necessarily exhibit wide or functional band gaps. Subsequently, the parent lattice is transformed into a band-gap-rich lattice by superposing periodic triangular-shaped perturbations (i.e., zigzag-sine-based curvatures) with controllable frequencies and magnitudes on its ligaments. Finally, the frequency and magnitude parameters needed to deliver a specific band gap between 0 and 1000 kHz are determined using a hybrid intelligent framework, developed based on an Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The ANFIS network integrates fuzzy logic expert models and artificial neural networks’ machine learning capabilities. Such a hybrid network is known for its ability to model strongly nonlinear and complex data. The data used in training the ANFIS models is generated using parametric finite element-based simulations where band gaps corresponding to a wide range of perturbation frequencies and magnitudes are computationally determined. The parametric study showed a nonlinear and complex topology-band gap characteristic relation; however, the Adaptive Neuro-Fuzzy Inference System (ANFIS) proved capable of modeling the observed complex topology-band gap behavior efficiently. The accuracy of the ANFIS models exceeded 99 % in several design ranges (i.e., perturbation parameters ranges). These were designated as high-accuracy design regions and were highlighted in the proposed design approach. Using multiple case studies with different band gap requirements, the ANFIS-based design framework proved effective in delivering customized lattice-based metamaterials with user-defined band gap frequencies.

Analysis of Metaverse Knowledge Levels of Prospective Mathematics: ANFIS Approach

Education is a field that is affected by technological development and requires rapid adaptation. Metaverse is one of these technologies and it is predicted that it will take its place widely in the world of the future, including education in research. However, it is seen that there are few studies on metaverse and the studies are generally analysed using statistical methods. From this point of view, the aim of this study was to predict the metaverse knowledge levels of pre-service mathematics teachers by using Adaptive Neuro-Fuzzy Inference System (ANFIS) and to create models. The use of fuzzy logic has spread to the field of education with the development of science and technology. ANFIS combines neural network research and fuzzy logic to utilise the relevant capabilities. Considering this important advantage, ANFIS model was established to predict the metaverse knowledge levels of pre-service teachers. The research was conducted with the participation of 192 pre-service teachers. Personal information form and metaverse scale were used as data collection tools. As a result of the study, the scores of the pre-service teachers obtained from the metaverse scale were found to be at a moderate level and the real and artificial scores of the pre-service teachers' metaverse knowledge levels were found to be quite close to each other.

Ensemble of artificial intelligence and physically based models for rainfall–runoff modeling in the upper Blue Nile Basin

ABSTRACT This study investigated the performance of the adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FFNN), Soil and Water Analysis Tool (SWAT), Hydrologic Engineering Center's Hydraulic Modeling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), and support vector regression (SVR) models for rainfall–runoff modeling using gauged and satellite rainfall, and their fusions in the Gilgel Abay watershed, Ethiopia. Afterward, simple average ensemble (SAE), weighted average ensemble (WAE), and neural network ensemble (NNE) techniques were applied to combine the outputs of individual models under three scenarios. The performance of the models was evaluated using Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE). The results demonstrated that the ANFIS model outperformed all the other single models with validation stage NSE values of 0.864 and 0.875, and RMSE values of 23.58 and 21.84 m3/s for gauge and fusion rainfall data, respectively. Among the physical-based models, SWAT gave better modeling performance with the validation stage NSE values of 0.81 and 0.821 for gauge and fusion rainfall data, respectively. Moreover, an ensemble of artificial intelligence and physical-based models greatly improved the overall modeling performance. The NNE improved the performance of single models up to 15.7 and 21.2 5% for fusion and satellite-based rainfall modeling, respectively.

Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network Models for Predicting Time-Dependent Moisture Levels in Hazelnut Shells (Corylus avellana L.) and Prina (Oleae europaeae L.)

Nowadays, in parallel with the rapid increase in industrialization and human population, a significant increase in all types of waste, especially domestic, industrial, and agricultural waste, can be observed. In this study, microwave drying, one of the disposal methods for agricultural waste, such as prina and hazelnut shell, was performed. To reduce the time, energy, and cost spent on drying processes, two recently prominent machine learning prediction methods (Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)) were applied. In this study, our aim is to model the disposal of waste using artificial intelligence techniques, especially considering the importance of environmental pollution in today’s context. Microwave power values of 120, 350, and 460 W were used for 100 g of hazelnut shell, and 90 W, 360 W, and 600 W were used for 7 mm thickness of prina. Both ANN and ANFIS approaches were applied to a dataset obtained from the calculation of moisture content and drying rate values. It was observed that the ANFIS and ANN models were applicable for predicting moisture levels, but not applicable for predicting drying rates. When the coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for moisture level are examined both for ANN and ANFIS models’ predictions, it is seen that the R2 value is between 0.981340 and 0.999999, the RMSE value is between 0.000012 and 0.015010 and the MAPE value is between 0.034268 and 23.833481.

Adaptive neuro-fuzzy inference system approach for tensile properties prediction of LPDC A357 aluminum alloy

This study is based on the desire of aluminum casting foundries to understand the influence of minor changes, within the specification limits, in the alloy chemistry. In order to ensure the casting of A357 Al alloys within the framework of the casting standards and to minimize the quality problems that may arise during casting; the estimation of ultimate tensile strength (UTS), yield strength (YS) and elongation (ε) due to very small changes among the alloying elements, although they are in the standard range, by using machine learning method (ML), were studied. The dataset of chemical composition and tensile properties of Low-Pressure Die Cast (LPDC) A357 Al alloy were experimentally established. The relationship between five input variables in the A357 alloy, namely the main alloying elements Si and Mg together with the most common impurity contents Fe, Ti and Cu were selected and three outputs (i.e UTS, YS and ε) were linked by Adaptive Neuro Fuzzy Inference System (ANFIS). The ANFIS model predicted that the most detrimental element affecting tensile properties was Fe content. According to this model, the order of the relative importance on UTS, YS and ε revealed as Si, Mg and Ti content respectively after the Fe content of the alloy.

Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut Shells

AbstractThe study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters’ influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil’s U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil’s U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil’s U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks. Graphical Abstract

An adaptive neuro-fuzzy inference system-based irrigation sprinkler system for dry season farming

In recent years, the management of irrigation systems has emerged as one of the most pressing concerns in the agricultural industry, especially in areas that experience dry seasons. In this research, an adaptive neuro-fuzzy inference system (ANFIS)-based irrigation system that uses a hot and cold sprinkler mechanism is presented. The goal of the system is to reduce the amount of water needed for farming and increase crop output during dry seasons. Adaptive control of water release is achieved via the use of MATLAB and the ANFIS model. This is done in response to changes in soil moisture, ambient temperature, and crop water demand. According to the findings, the suggested system performs noticeably better than conventional irrigation methods in terms of both the amount of water used and the number of crops produced.

Data-Driven Price Trends Prediction of Ethereum: A Hybrid Machine Learning and Signal Processing Approach

Due to the recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) resulted in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.

Adaptive Neuro-Fuzzy Inference System-Based Predictive Modeling of Mechanical Properties in Additive Manufacturing

Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process.

Seismic Damage Prediction and Classification tool: Utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS) and Geographic Information System (GIS)

Seismic damage prediction of wide range of buildings is an important prioritization and classification tool for implementation of seismic risk and vulnerability assessment. This study has developed a rapid visual screening (RVS) tool using Adaptive Neuro-Fuzzy Inference System (ANFIS) model and Geographic Information System (GIS). Hospital an d public-school buildings located in districts 5 and 6 have been selected for assessment. Technical calculation basis in the development of FEMA P- 154 was followed in formulating the model using the neuro-fuzzy tool package in MATLAB. Six (6) building para meters and their resulting output (final score) were inputted for training. Sub-clustering FIS model showed the lowest RMSE and R2 result out of the five (5) FIS models compared. The model showed effective performance in classifying the buildings’ damage grades having 0.2908, 0.8073 and 0.948 for RMSE, and for testing and overall coefficient of determination (R2) values, respectively. Performance me trics from the developed confusion matrix showed high range of values for sensitivity, specificity, precision, and accuracy, with maximum error rate and false positive rate of 0.11 and 0.25, respectively, in classifying the buildings’ damage grade. These validate the model’s performance and ability in classifying the damage grade of the buildings observed. The results were used to develop a digital mapping of the damage grade distribution of the selected buildings in the area using ArcGIS.

Performance Evaluation of Benzyl Alcohol Oxidation with tert-Butyl Hydroperoxide to Benzaldehyde Using the Response Surface Methodology, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Model.

The adaptive neuro-fuzzy inference system (ANFIS), central composite experimental design (CCD)-response surface methodology (RSM), and artificial neural network (ANN) are used to model the oxidation of benzyl alcohol using the tert-butyl hydroperoxide (TBHP) oxidant to selectively yield benzaldehyde over a mesoporous ceria-zirconia catalyst. Characterization reveals that the produced catalyst has hysteresis loops, a sponge-like structure, and structurally induced reactivity. Three independent variables were taken into consideration while analyzing the ANN, RSM, and ANFIS models: the amount of catalyst (A), reaction temperature (B), and reaction time (C). With the application of optimum conditions, along with a constant (45 mmol) TBHP oxidant amount, (30 mmol) benzyl alcohol amount, and rigorous refluxing of 450 rpm, a maximum optimal benzaldehyde yield of 98.4% was obtained. To examine the acceptability of the models, further sensitivity studies including statistical error functions, analysis of variance (ANOVA) results, and the lack-of-fit test, among others, were employed. The obtained results show that the ANFIS model is the most suited to predicting benzaldehyde yield, followed by RSM. Green chemistry matrix calculations for the reaction reveal lower values of the E-factor (1.57), mass intensity (MI, 2.57), and mass productivity (MP, 38%), which are highly desirable for green and sustainable reactions. Therefore, utilizing a ceria-zirconia catalyst synthesized via the inverse micelle method for the oxidation of benzyl alcohol provides a green and sustainable methodology for the synthesis of benzaldehyde under mild conditions.

Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study.

Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R2 = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.

Efficiency increment of CFD modeling by using ANFIS artificial intelligence for thermal-based separation modeling

This study presents a comprehensive analysis of predicting the temperature in a vacuum membrane distillation (VMD) process. The simulation was carried out via computational fluid dynamics (CFD) as well as machine learning. CFD was performed for obtaining temperature distribution in the feed solution, and the calculated temperature was used for training several machine learning models. The dataset comprises over 13,000 observations, which provide a rich source for modeling complex relationships. Three sophisticated regression models were employed: Adaptive Neuro-Fuzzy Inference System (ANFIS), Kernel Ridge Regression (KRR), and Multi-Layer Perceptron (MLP). ANFIS was chosen for its hybrid nature, combining neural networks and fuzzy logic, effectively capturing intricate non-linear relationships in data. ANFIS performance in fitting the data was compared with the other models. Hyper-parameter optimization for these models was conducted using the Tabu Search algorithm to ensure optimal performance. The ANFIS model demonstrated superior performance with an R2 score of 0.9964513 on the training set and 0.9964507 on the test set, alongside a MSE of 0.037655 and a MAE of 0.168272. The robustness of ANFIS was further confirmed by a 3-fold cross-validation mean R2 score of 0.9964579 and a standard deviation of 3.3619616e−05.

Method for Predicting Internal Defect Areas of Timber Components Based on an Adaptive Neuro-Fuzzy Inference System

ABSTRACT Internal defects in the timber components of ancient architecture can reduce their effective cross-sectional areas, deteriorate their mechanical performance, and increase the risk of structural failure. To accurately and rapidly determine the extent of internal damage in components of ancient architecture, this study performed damage detection tests on Larix gmelinii (Larch) column specimens using stress wave nondestructive testing technology. An adaptive neuro-fuzzy inference system (ANFIS) model was developed using the cross-sectional wave velocity ratio after investigating the laws of defect identification through stress-wave testing. Stress-wave detection could identify the relative positional correlation of defect areas. However, the accuracy of the defect area detection was inconsistent (8%–98%). The accuracy of the ANFIS model exhibits a diminishing trend as both parameters rejection rate and influence range increase. However, the ANFIS outperformed the stress-wave detection technique and linear regression model with correlation coefficient and prediction accuracy of 0.99 and 88%, respectively. The root mean square error for the ANFIS was reduced by 79% and 70% compared to the stress wave detection method and linear regression model, respectively. Thus, the ANFIS model can precisely forecast areas of internal damage in timber components of historical edifices and can be used to evaluate the safety of ancient timber buildings.

Artificial intelligence‐based modeling of novel non‐thermal milk pasteurization to achieve desirable color and predict quality parameters during storage

AbstractThis study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (p > 0.05) for lightness (L*), redness‐greenness (a*), yellowness‐blueness (b*), total color differences (∆E), hue angle (h), chroma (C), whiteness (WI), yellowness (YI), and browning index (BI). ANFIS well‐predicted milk fat and moisture content using quadratic and two‐factor interaction models with mean errors of .00858–.01260 and correlation coefficient of .8051–.8205. Stability tests showed L* and WI reduced while a*, b*, ΔE, h, C, YI, and BI increased during the storage. NP milk had 77.21% higher half‐life than CP, as predicted by ANFIS modeling. Findings indicated milk quality characteristics could be estimated based on physical parameters (e.g., color components), contributing to sustainable food production.Practical applicationsThe findings offer practical applications of artificial intelligence (AI) as an innovative monitoring and prediction technique to enhance food quality and sustainability. The proposed methodology makes the real‐time prediction of milk quality feasible by leveraging AI and physical parameters. An adaptive neuro‐fuzzy inference system (ANFIS) accurately predicts moisture and fat contents according to color values, facilitating quality assessment. Stability tests during cold storage provide insights into milk quality changes over time, aiding in determining key parameters in predictive modeling. The proposed approach was found to be applicable to both conventional and non‐thermal pasteurized milk. This study also provides a step‐by‐step protocol, facilitating the implementation of emerging technologies in the food industry.

A hybrid model of artificial intelligence integrated into GIS for predicting accidents in water supply networks

The search for an effective and reliable model for predicting accidents on water supply networks by determining their exact locations has always been important for effectively managing water distribution systems. This study, based on the adaptive neuro-fuzzy logical inference system (ANFIS) model, was developed to predict accidents in the city of Kyiv (Ukraine) water supply network. The ANFIS model was combined with genetic algorithms and swarm optimization (ACO) methods and integrated into a GIS to visualize results and determine locations. Forecasts were evaluated according to the following criteria: mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Depending on the amount and type of input data, ANFIS optimization with genetic algorithms and swarm optimization (ACO) can, on average, increase the accuracy of ANFIS predictions by 10.1% to 11%. The obtained results indicate that the developed hybrid model may be successfully applied to predict accidents on water supply networks.

Exploring AI approaches for predicting groundwater levels in coastal agro-climatic zones: a case study in Cuttack District, Odisha

Groundwater level (GWL) prediction across various time scales is essential for efficient management and governance of water resources especially in regions characterized by arid and semi-arid climates, and it holds great significance. Within certain coastal regions, agro-climatic zones give rise to challenges like water scarcity in summer and waterlogging during the rainy season, resulting in reduced GWL during scarcity periods and saltwater intrusion that contaminates groundwater. This study emphasizes on application of diverse AI methodologies, encompassing Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Wavelet Transform-based ANN (W-ANN), ANFIS (W-ANFIS), SVR (W-SVR), and LSTM (W-LSTM) models for quantitative assessment of groundwater in Odisha's Cuttack District, aiming to comprehend GWL fluctuations across the region. The investigation leverages historical groundwater data from monitoring wells, incorporating monthly datasets of rainfall, temperature, relative humidity, and GWLs. Through comparative assessment using statistical methods namely Pearson’s R (R), co-efficient of determination (R2), Root Mean Squared Error (RMSE), and Sum of Squared Error (SSE), the most precise and robust AI approach for groundwater estimation in the area is identified. The W-LSTM (R2-0.78196, RMSE- 0.09254, R-0.88428 and SSE-2.66357) and W-ANFIS (R2-0.74068, RMSE-0.08229, R-0.86063 and SSE-2.10596) hybrid algorithms consistently achieved the most accurate predictions for GWLs followed by W-SVR, W-ANN hybrid models and LSTM and ANN for all stations. Overall, this study demonstrated promising outcomes, offering a dependable foundation for water resources planners to guide future investigations into groundwater resources.

A neuro-fuzzy security risk assessment system for software development life cycle

This study aims to protect software development by creating a Software Risk Assessment (SRA) model for each phase of the Software Development Life Cycle (SDLC) using an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Software developers discovered and validated the risk variables affecting each SDLC phase, following which relevant data about risk factors and associated SRA for each SDLC phase were collected. To create the SRA model for SDLC phases, risk factors were used as inputs, and SRA was used as an output. The formulated model was simulated using 70 % and 80 % of the data for training, while 30 % and 20 % were used for testing the model. The performance of the SRA models using the test datasets was evaluated based on accuracy. According to the study findings, many risk variables were discovered and confirmed for the requirement, design, implementation, integration, and operation phases of SDLC 11, 8, 9, 4, and 6, respectively. The SRA model was formulated using the risk factors using 2048, 256, 512, 16, and 64 inference rules for the requirement, design, implementation, integration, and operation phases, respectively. The study concluded that using the SRA model to assess security risk at each SDLC phase provided a secured software development process.

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.