ANN-derived equation and ITS application in the prediction of dielectric properties of pure and impure CO2

Abidoye, Luqman K.; Mahdi, Faiz M.; Idris, Musibaudeen Olatunde; Alabi, O.O.; Wahab, Abdul

doi:10.1016/j.jclepro.2017.12.013

Cited by 26 publications

(8 citation statements)

References 29 publications

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“…A multilayer perceptron neural network (MLPNN), a network-based DM computing approach, is employed in this research with individual base learner and ensemble base learner methods to model and forecast MK concrete. An ANN program mimics the structure of a biological nervous system’s neurons [ 69 ]. Parallel linkages provide the basis of ANNs.…”

Section: Methodsmentioning

confidence: 99%

In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches

et al. 2022

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In recent decades, a variety of organizational sectors have demanded and researched green structural materials. Concrete is the most extensively used manmade material. Given the adverse environmental effect of cement manufacturing, research has focused on minimizing environmental impact and cement-based product costs. Metakaolin (MK) as an additive or partial cement replacement is a key subject of concrete research. Developing predictive machine learning (ML) models is crucial as environmental challenges rise. Since cement-based materials have few ML approaches, it is important to develop strategies to enhance their mechanical properties. This article analyses ML techniques for forecasting MK concrete compressive strength (fc’). Three different individual and ensemble ML predictive models are presented in detail, namely decision tree (DT), multilayer perceptron neural network (MLPNN), and random forest (RF), along with the most effective factors, allowing for efficient investigation and prediction of the fc’ of MK concrete. The authors used a database of MK concrete mechanical features for model generalization, a key aspect of any prediction or simulation effort. The database includes 551 data points with relevant model parameters for computing MK concrete’s fc’. The database contains cement, metakaolin, coarse and fine aggregate, water, silica fume, superplasticizer, and age, which affect concrete’s fc’ but were seldom considered critical input characteristics in the past. Finally, the performance of the models is assessed to pick and deploy the best predicted model for MK concrete mechanical characteristics. K-fold cross validation was employed to avoid overfitting issues of the models. Additionally, ML approaches were utilized to combine SHapley Additive exPlanations (SHAP) data to better understand the MK mix design non-linear behaviour and how each input parameter’s weighting influences the total contribution. Results depict that DT AdaBoost and modified bagging are the best ML algorithms for predicting MK concrete fc’ with R2 = 0.92. Moreover, according to SHAP analysis, age impacts MK concrete fc’ the most, followed by coarse aggregate and superplasticizer. Silica fume affects MK concrete’s fc’ least. ML algorithms estimate MK concrete’s mechanical characteristics to promote sustainability.

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Section: Methodsmentioning

confidence: 99%

In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches

et al. 2022

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“…The configuration setting can include determining the number of layers and the number of neurons in each layer. 34…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The configuration setting can include determining the number of layers and the number of neurons in each layer. 34 Training, validation, and test are three key processes of ANNs. In a training process, the relationship between input and output is presented.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A data-driven multi-criteria decision-making approach for assessing new product conceptual designs

Arbabi

Vahedi-Nouri

Iranmanesh

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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The surge in competition among companies to acquire a more significant portion of the market as well as respecting customer preferences in high quality and diverse products result in a reduction of product life cycles. Accordingly, companies are under enormous pressure to introduce new high quality and diverse products on time. Assessing new product designs at the primary phases of new product development (NPD) is a necessary and complex activity that can considerably reduce the time and cost of introducing new products to the market. The current methods of evaluating new product conceptual designs, including employing decision-making methods based on subjective opinions of experts, utilizing simulation packages, and following trial-and-error approaches in prototyping, may be inefficient, very time-consuming, and costly. To overcome this issue, this paper develops a quantitative data-driven Multi-Criteria Decision-Making (MCDM) approach founded on the combination of an Artificial Neural Network (ANN) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess the new conceptual designs. So that the ANN method is utilized to predict the performance characteristics of new designs based on the related existed data of similar products, and TOPSIS is employed to score and rank different proposed alternatives designs. Finally, a case study of evaluating new product conceptual designs in an automotive research and development company is considered to demonstrate the performance and applicability of the proposed approach.

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“…This model has only 4 neurons in the single hidden layer. The procedure followed in these comparisons was similar to the one described in the work of Abidoye et al (2018).…”

Section: Ann Modelsmentioning

confidence: 99%

Machine learning of the mineralogical and chemical constituents’ influences on carbonation efficiency

Abidoye

Oladipo

2021

Preprint

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Cheap and efficient computational tool is needed to understand the influences of rock’s mineralogical and chemical constituents on the mineral carbonation efficiency. This work employs machine learning technique to learn some subtle contributions of minerals and technical parameters to carbonation yields in earthly materials. After training different configurations of Artificial Neural Network models, statistical appraisal shows that ANN[17-4-1] possesses the best performance criteria. Using this best-performing network, it was found that carbonation efficiency becomes lower as the particle size increases with maximum effects of size change giving up to 88, 180 and 289% reductions in carbonation efficiency corresponding to 10, 20 and 30% particle size increases, respectively. The simulation further highlights that the effects of particle size on carbonation efficiency can be overcome or, at least, reduced, by prolonging the duration of the carbonation. The study shows that there is optimum level of increase in the calcium amount to yield increased carbonation efficiency, following which the marginal increase in % of calcium continue to result in decrease of efficiency. There is gradual rise in efficiency as the % of silica and iron increase in the minerals, with up to 1541 % rise in efficiency at 30% increase in iron content. This is connected to the relatively higher level of magnesium in the sample considered. It was found that the high temperature (> 100oC) combined with high pressure (> 10 bars) favours increased efficiency with rise in pH value. Similarly, low temperature (< 100oC) with low pressure (< 10 bars) increased efficiency with rise in pH value. On the contrary, low pressure with high temperature yields reverse trends in efficiency for increase in pH. Pressure, carbonation time and temperature, all have positive effects on carbonation efficiency. But, curiously, at very high original carbonation efficiency, e.g., above 50%, reverse trend was predicted by the ANN model for temperature rise. This may mean that at very high carbonation level, it is of no benefit to raise the temperature further, as this may lead to decarbonation or desorption of CO2 in the medium. This study is among the first set of publications, in the open literature, to utilize the concept of machine learning to predict and forecast the carbonation efficiency of rock materials based on mineralogical contents and experimental conditions.

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ANN-derived equation and ITS application in the prediction of dielectric properties of pure and impure CO2

Abstract: This is a repository copy of ANN-derived equation and ITS application in the prediction of dielectric properties of pure and impure CO₂.

Cited by 26 publications

References 29 publications

In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches

In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches

A data-driven multi-criteria decision-making approach for assessing new product conceptual designs

Machine learning of the mineralogical and chemical constituents’ influences on carbonation efficiency

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