An artificial intelligence approach for concrete hardened property estimation

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.

The paper investigated the effect of locally sourced fly ash and ground granulated blast-furnace slag (GGBS) on the compressive strength and chloride resistance of concrete. The mix proportion was cementitious material (total of ordinary Portland cement (OPC), fly ash and GGBS): sand: coarse aggregate: water of 1:2:3:0.6 in which 20% by mass of total cementitious materials was replaced by class F fly ash and GGBS. Compressive strength and rapid chloride penetration tests were conducted at 28, 56 and 120 days. The results shows that fly ash and GGBS reduce slightly the compressive strength but improve significantly the choloride resistance of concrete. Within the range of investigation, 10% of fly ash and 10% of GGBS are recommended to replace OPC as they improve the chloride resistance and maintain the compressive strength of concrete.

In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

In this paper, a hybrid meta-heuristic artificial intelligence (AI) method, named “ANFIS–PSOGA,” is developed for modeling the discharge coefficient of circular side orifices for the first time. For this purpose, the adaptive neuro fuzzy inference system (ANFIS) network is optimized by means of two robust optimization algorithms: the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. Furthermore, the Monte Carlo simulations are utilized to enhance the performance of the AI hybrid model. Besides, the k-fold cross-validation method is employed in order to verify the numerical results. Initially, the performance of three fuzzy inference system (FIS) generations of the ANFIS model including grid partitioning, subtractive clustering and fuzzy C-means clustering are evaluated. After that, using the parameters affecting the discharge coefficient, eleven ANFIS models are defined and then the best ANFIS model and the most influencing input parameters are identified through the conduction of a sensitivity analysis. For the superior ANFIS model, mean absolute percentage error, root-mean-square error and the correlation coefficient (R) are obtained 0.021, 0.020 and 0.871, respectively. Also, the Froude number (Fr) is detected as the most effective input parameter. Moreover, the discharge coefficient of the circular side orifices and the flow field within main channels along the side orifices are simulated through the adoption of a computational fluid dynamics (CFD) model. Additionally, the flow field at three different cross sections (beginning, middle and end of orifice) along the side weir is evaluated. For instance, by advancing the flow toward the side orifice, the Froude number is reduced. Besides, at each cross section, the maximum shear stress is estimated to occur in the vicinity of the crest of the circular side orifice. According to the CFD results, the maximum lateral velocity is estimated near the circular side orifice. In addition, the results yielded by the ANFIS, ANFIS–PSO, ANFIS–PSOGA and CFD models are compared and an uncertainty analysis is carried out for the models. Based on the comparison, ANFIS–PSOGA has the best performance in simulating the discharge coefficient. Ultimately, a simple and practical computer code for the best model (ANFIS–PSOGA) is proposed which can be easily used by scholars and engineers in practical usages.

In this paper, we compare and analyze the prediction performance of existing ANFIS (Adaptive Neuro Fuzzy Inference System) models. The ANFIS model is a fusion of neural networks with learning ability, adaptability, and computational ability, and a fuzzy system with human-like thinking ability and reasoning ability. In this paper, we analyze the prediction performance of ANFIS models using different input space partitioning methods. ANFIS 1 uses SC (Subtractive Clustering), a clustering method based on the density of data, to calculate the center of the cluster by calculating potential values inversely proportional to the distances of the different inputs. ANFIS 2 uses Fuzzy C-Means (FCM) clustering, which is a clustering method based on degree of belonging to data, and classifies each data belonging to each cluster according to degree of affiliation. ANFIS 3 is a method of using Context based Fuzzy C-Means (CFCM) clustering, which is a clustering method considering characteristics of input and output space based on FCM clustering. It creates clusters for each context generated according to characteristics of output space, to classify the data. To evaluate these ANFIS models, we use short term electricity price forecasting data and Boston housing data. The prediction performance is based on the Root Mean Squared Error (RMSE) method. Experimental results show that the prediction performance of the ANFIS model is different depending on the characteristics of each data. Furthermore, we confirmed that the prediction performance of ANFIS model using CFCM clustering method is constant.

The integration of sustainable materials such as fly ash, blast-furnace slag, recycled aggregates, and seawater into concrete mixes offers significant potential for reducing the environmental impact of construction. However, traditional experimental methods for determining the compressive strength of these concrete mixes are time-consuming and resource-intensive. This study leverages Artificial Neural Networks (ANNs) and Machine Learning (ML) to develop a predictive model for the compressive strength of sustainable concrete, using a dataset of 768 concrete mix samples. Input variables include the concrete age as well as concrete composition, including cement, water, fine and coarse aggregates, seawater, fly ash, blast-furnace slag, and superplasticizer contents, while the output is the compressive strength. The developed model captures the non-linear relationships among these variables to predict compressive strength efficiently. The best ANN model achieved a test loss of 0.051, demonstrating its ability to accurately predict compressive strength and reduce reliance on traditional testing methods. Moreover, the model’s results were compared with those of alternative algorithms to ensure its validity. These findings highlight the potential of machine learning in advancing sustainable construction practices. A relevant future research direction is to analyze feature importance in machine learning models to identify key variables and guide more effective optimization and decision-making, in addition to extending their application to other material properties and advanced concrete mixes.

Electrochemical-kinetics, MD-simulation and multi-input single-output (MISO) modeling using adaptive neuro-fuzzy inference system (ANFIS) prediction for dexamethasone drug as eco-friendly corrosion inhibitor for mild steel in 2 M HCl electrolyte

This study, third in the series, following from ground limestone and Class F fly ash, evaluates, as a cement constituent, the effect of using ground granulated blast furnace slag (GGBS) on the strength development of concrete, and consequently its embodied carbon dioxide (CO2e). The paper has been built from systematically analysing, evaluating and modelling the extensive data-matrix developed, having 85,099 data points, from the information sourced from 663 studies published in English, during 1974 to 2020, by 1,672 authors, working in 718 institutions in 49 countries, globally. It is shown that, at a given water/cement ratio, in comparison to Portland cement (PC), the use of GGBS results in a reduction in 28-day concrete strength, which increases with GGBS content, at a rate determined by the strength of concrete, GGBS fineness, and curing of concrete. It is also shown that, as to achieve a 28-day design strength, a lower water/cement ratio is required with a PC/GGBS blended cement than PC, this will reduce the actual CO2e savings that can be realised with the use of GGBS as cement constituent in manufacturing concrete. Finally, it is shown that GGBS is more effective in lowering CO2e of concrete than FA and GLS.

Comparison of ANFIS and NN models—With a study in critical buckling load estimation

As financial markets worldwide continue to evolve, the importance of predictive models for investors and analysts is growing. This paper presents a comparative analysis of metaheuristic-based Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models for predicting stock prices in the Borsa Istanbul 100 (BIST 100) index. Using a comprehensive dataset of weekly data (2010–2023), researchers tested different model configurations, and the ANFIS model was improved with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Model performance was evaluated using performance criteria such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The results showed that, in particular, the ANFIS-PSO and ANN-based approaches exhibited higher accuracy compared to traditional regression methods. The study also analyzed the impact of various macroeconomic and financial indicators, including the gold/TL exchange rate, VIX index, and government bond interest rates, on the BIST 100. The findings reveal that artificial intelligence-based modeling significantly improves stock price forecasts thanks to its adaptability to dynamic market conditions. In this respect, the study makes theoretical contributions and highlights practical implications for investment strategies and risk management. Future research can further enrich the financial modeling literature by testing the validity of similar approaches in other emerging or developed markets, as well as by incorporating additional macroeconomic factors and different meta-heuristic algorithms into the model.

In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model is proposed for the determination of alpha radioactivity of Hazar Lake waters and for the prediction of its unknown values. The model parameters of the lake water are pH, total hardness (TH), depth, electrical conductivity (EC), and alpha radioactivity. ANFIS model is performed using the back-propagation algorithm, which has the five layers. Average relative error between measurements predicted by theoretical (ANFIS) and experimental data is approximately 0.7043%. The relative error between the test data and the radioactivity data change between 0.06% and 14%. Additionally, validity of the model is also tested with a regression model. The predicted results with the ANFIS model is better as statistically than the regression model.

The shear strength (Su) of soils is one of the most widely used parameters for designing structures safely, where Su is found with the unconfined compression test (UCS). Although UCS can be acquired by performing uniaxial compression test it would be extremely helpful to predict the UCS without performing any compression test, namely, using computational methods considering different parameters of soils such as consistency limits, fine grain ratio, liquidity index, and void ratio. The goal of present work is predicting UCS taking into account these soil parameters with the aid of developed Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the Multiple Linear Regression (MLR) analyses. On the other hand, the effect of the size of the training set of designed models on the results is examined, also. For this aim, four different models composed of different training and test set ratios have been constructed and analyzed using ANFIS and MLR. It is concluded that UCS can be predicted using MLR analysis and ANFIS model with best 0.76 and 0.91 values of determination coefficient (R2) around the x = y line respectively, and the effect of the size of the training set of models on ANFIS is more pronounced than MLR models.

ANFIS for analysis friction factor and Nusselt number of pulsating nanofluids flow in the fluted tube under magnetic field

The efficacy of two machine learning algorithms to predict the concrete compressive strength is investigated in this research. For this objective, a vast amount of experimental data from numerous academic research articles was statically evaluated and modelled. In all, 265 observations were considered in this investigation 70% of the data was used for training, while the remaining 30% was used for testing. The data used in this paper was divided randomly. Cement, ground granulated blast furnace slag, limestone powder, fly ash, rice husk ash, fine aggregate, coarse aggregate, silica fume, superplasticizer, water, viscosity modifying admixture, and coarse aggregate were among the 11 input parameters employed in the study. The concrete compressive strength was taken as the output parameter of the model. To assess the models’ prediction abilities, correlation coefficient (CC), the mean absolute error (MAE) and root mean square error (RMSE) values were employed. From the results it was concluded that RF-based model outperforms the M5P Model. The CC, RMSE and MAE values for RF are 0.9867, 3.7887 and 2.9244 for training and 0.8668, 9.1723 and 5.8552 for testing stage respectively.

Crocus sativus L. (Iridaceae), commonly known as saffron, is a highly valued medicinal plant often termed ‘red gold’ for its economic importance and vibrant flower colour. Among its bioactive constituents, safranal is notable for its antioxidant, anti-inflammatory, and neuroprotective properties. However, standardising its extraction remains challenging due to variable processing conditions. This study optimised safranal extraction using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). Initial screening showed that microwave-assisted extraction (MAE) provided the highest yield (0.950% in 30 min), while ultrasound-assisted extraction (UAE) achieved 0.808% in only 10 min. Extraction parameters including temperature, irradiation time, solvent concentration, particle size, solvent-to-solute ratio, pH, and extraction steps were systematically evaluated. Box-Behnken design (BBD) optimised conditions (60 °C, 10 min, 50% ethanol, 7.5 g/mL solid-to-solvent ratio) yielded a maximum of 0.605% safranal. Box-Behnken design (BBD) showing an excellent fit with the quadratic model (R 2 = 99.38%). The adaptive neuro-fuzzy inference system (ANFIS) model further refined these predictions. High-performance thin-layer chromatography (HPTLC) quantified the yield of safranal (0.605%) under optimised conditions. The ANFIS model with 81 fuzzy rules successfully modelled non-linear interactions. The HPTLC method was validated with a Limit of Detection (LOD) of 120 µg/spot and a Limit of Quantitation (LOQ) of 360 µg/spot. The results demonstrate a rapid, eco-friendly, and energy-efficient extraction process, which is a sustainable approach for safranal production to support its growing pharmaceutical and fragrance applications.