Modeling the acentric factor of binary and ternary mixtures of ionic liquids using advanced intelligent systems

Olayiwola, Teslim; Ogolo, Oghenerume; Falola, Yusuf

doi:10.1016/j.fluid.2020.112587

Cited by 11 publications

(3 citation statements)

References 83 publications

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“…Vapnik et al [46] created SVM, a supervised machine learning model for solving high-dimensional problems. Depending on the data, this approach may be used to solve regression or classification issues [47,48]. "A hyperplane is used in SVM to map a set of training samples representing points in space to a multidimensional feature space".…”

Section: Support Vector Machines Regressionmentioning

confidence: 99%

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

et al. 2022

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Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem due to the design code, which typically requires specific characteristics. The emergence of a new category of technology has motivated researchers to develop mechanical strength prediction models using Artificial Intelligence (AI). Empirical and statistical models have been extensively used. These models require a huge amount of laboratory data and still provide inaccurate results. Sometimes, these models cannot predict the properties of concrete due to complexity in the concrete mix design and curing conditions. To conquer such issues, AI models have been introduced as another approach for predicting the compressive strength and other properties of concrete. This article discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC.

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Section: Support Vector Machines Regressionmentioning

confidence: 99%

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

et al. 2022

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“…This is a prominent ML technique rooted in statistical learning principles. This method can be applied to solve classification and regression-based problems 51 , 52 . The method aims to minimize generalized error and is rooted in the principle of structural risk minimization.…”

Section: Methodology Of Studymentioning

confidence: 99%

Prediction of axial capacity of corrosion-affected RC columns strengthened with inclusive FRP

Kumar,

Arora,

Kumar

et al. 2024

Sci Rep

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The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.

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“…where N denotes the number of data samples, while subscriptions cal and exp represent the calculated and experimental quantities, respectively [74]. Also, H exp denotes the experimental relative viscosity of nanoparticles.…”

Section: Models' Evaluationmentioning

confidence: 99%

Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach

Chen

Hammid²,

Akbarov

et al. 2022

International Journal of Chemical Engineering

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This work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al2O3, TiO2, SiO2, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer, and intensive properties of NPs. In order to develop a comprehensive artificial intelligence model in this study, sixty-nine data samples were collected. To this end, the Gaussian process regression approach with four basic function kernels (Matern, squared exponential, exponential, and rational quadratic) was exploited. It was found that Matern outperformed other models with R2 = 0.987, MARE (%) = 6.048, RMSE = 0.0577, and STD = 0.0574. This precise yet simple model can be a good alternative to the complex thermodynamic, mathematical-analytical models of the past.

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Modeling the acentric factor of binary and ternary mixtures of ionic liquids using advanced intelligent systems

Cited by 11 publications

References 83 publications

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

Prediction of axial capacity of corrosion-affected RC columns strengthened with inclusive FRP

Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach

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