Physical properties of Ordinary Portland Cements

Ahmmad, Shaik Kareem; Hamraj, Mohd.; Ahmed, Syed Taqi Uddin; Jabeen, Nameera

doi:10.1063/5.0001386

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…The binary glass prediction to possess more accuracy than other glass refractive index prediction. The results of this distinctive study also produced the best results as compared to the previous literature available employing large glass datasets, thus enabling the findings of this work to future glass research [13][14][15][16] also proved ANNs to perform better than other algorithms for prediction using glass datasets.…”

Section: Artificial Neural Networksupporting

confidence: 60%

“…According to many researchers, mostly the glass densities have been measured from the composition of the particular glasses only [11][12][13]. To understand the elastic behavior of the glasses by using composition of the network like ceramic-materials were utilized to understand the glass pattern [14]. Several ML-models to measure the density precisely through composition-based parameters such as packing fraction and ionic radii to get least error were also reported [15].…”

Section: Introductionmentioning

confidence: 99%

“…In accordance with recent studies on machine learning models to predict glass properties, they have used huge datasets making them "data-driven" models that successfully derive a correlation between the input and the output variables [11][12][13][14][15]. These models directly map the chemical composition of the glass to their respective properties.…”

Section: Introductionmentioning

confidence: 99%

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Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques

Ahmed

Rajiya

Samee

et al. 2022

J Inorg Organomet Polym

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Machine learning techniques have been employed to predict the glass densities of xBi 2 O 3 –(70 − x)B 2 O 3 –20Li 2 O–5Sb 2 O 3 –5ZnO glasses using a data set of 2000 various B 2 O 3 rich glasses using their chemical composition and ionic radius. The experimental density of present glasses strongly depends on Bi 2 O 3 content which is increasing with bismuth content. The increasing density in bismuth doped glasses because the BO 3 are converted into BO 4 units, and besides BO 3 units are less heavy than the BO 4 units. The FTIR studies also confirm that the intensity of B–O–B bond decreasing with increasing Bi 2 O 3 content which suggested that B–O–B bond in bond ring isolated to BO 3 units transformed into BO 4 units. In Raman Spectra the stretching vibrations of BO 4 units shifting towards higher wavelengths with increasing Bi 2 O 3 content. This shifting conforms that there is a structural changes in the glass-matrix and borate units converting from BO 3 to BO 4 units. The prepared glasses along with B 2 O 3 rich glass data set train on various AI model such as gradient descent, Random Forest regression and Neural Networks to predict present density of glasses. Among the various models RF regression analysis model is successfully acceptable for the glass data with the highest R 2 value 0.983 which end result conform that the predicted and experimental values correlated. ANNs stood the effective technique in prediction of glass density with the optimum performance resulting with Tanh as the activation function (R 2 = 0.950). The minimum cost 0.018 obtained in the case of gradient decent function which also shows the better performance of regression model.

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Section: Artificial Neural Networksupporting

confidence: 60%