Buckling response of CNT based hybrid FG plates using finite element method and machine learning method

Kumar, Ravi; Kumar, Ajay; Kumar, Divesh Ranjan

doi:10.1016/j.compstruct.2023.117204

Cited by 18 publications

(3 citation statements)

References 35 publications

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“…The coefficient of R 2 , Root Mean Square Error (RMSE), and Mean absolute Percentage Error (MAPE) are the criteria chosen to evaluate the performance of the soft computing methods [40]. R 2 shows how well the measured dependent variable account for the independent variables and it can be calculated as follows, [Eq.…”

Section: Evaluation Of the Accuracy Of The Computing Methodsmentioning

confidence: 99%

ANN- PSO modelling for predicting buckling of self-compacting concrete column containing RHA properties

Jabar

Pradeep

2023

Matéria (Rio J.)

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In recent decades, concrete technology has reached the broad-based areas of operations through the implementation of Self Compacting Concrete to increase the concrete performance. Due to high silica content, the pozzolonic characteristic of RHA makes it as a supplementary material for cement. In this paper, Cement was partially replaced with Rice Hush Ash of 5%, 10% 15%, 20%, 30% and 40% influencing the properties of SCC. The aim of this report is to explore the effect of cement replacement by RHA on the fresh and mechanical properties of SCC. In addition, the bucking behaviour of the axial loaded reinforced concrete column was predicted using Artificial Neural Network. Experimental data are collected and 100 experimental data is used for training the ANN model and 20 sets of data is utilized for testing. From the results it is observed that SCC blended with RHA shows the positive relationship between 30% replacement of RHA with an increase in the strength of compression and tensile strength of around 6. The buckling behaviour of the 70% Cement + 30% RHA SCC reinforced column was predicted by ANN-PSO is a precision and efficient model.

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Section: Evaluation Of the Accuracy Of The Computing Methodsmentioning

confidence: 99%

ANN- PSO modelling for predicting buckling of self-compacting concrete column containing RHA properties

Jabar

Pradeep

2023

Matéria (Rio J.)

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“…First, they chose as features the spectral variables of the nanotube arrays, which were computed from data on the response of the nanotube arrays to serum samples. They then tested five commonly used machine learning algorithms, including logistic regression ( LaValley, 2008 ), decision trees ( Quinlan, 1996 ), artificial neural networks ( Zhang et al, 2019b ), stochastic sen ( Kumar et al, 2023 ), and support vector machines, for binary classification with the task of distinguishing patients from patients with other diseases or healthy individuals with ovarian cancer. They then used Bayesian optimization to optimize the hyperparameters of each algorithm ( Greenhill et al, 2020 ).…”

Section: The Role Of Carbon Nanotubes In Cancer Diagnosismentioning

confidence: 99%

Nanotechnological advances in cancer: therapy a comprehensive review of carbon nanotube applications

Gao,

Xu,

Sun

et al. 2024

Front. Bioeng. Biotechnol.

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Nanotechnology is revolutionising different areas from manufacturing to therapeutics in the health field. Carbon nanotubes (CNTs), a promising drug candidate in nanomedicine, have attracted attention due to their excellent and unique mechanical, electronic, and physicochemical properties. This emerging nanomaterial has attracted a wide range of scientific interest in the last decade. Carbon nanotubes have many potential applications in cancer therapy, such as imaging, drug delivery, and combination therapy. Carbon nanotubes can be used as carriers for drug delivery systems by carrying anticancer drugs and enabling targeted release to improve therapeutic efficacy and reduce adverse effects on healthy tissues. In addition, carbon nanotubes can be combined with other therapeutic approaches, such as photothermal and photodynamic therapies, to work synergistically to destroy cancer cells. Carbon nanotubes have great potential as promising nanomaterials in the field of nanomedicine, offering new opportunities and properties for future cancer treatments. In this paper, the main focus is on the application of carbon nanotubes in cancer diagnostics, targeted therapies, and toxicity evaluation of carbon nanotubes at the biological level to ensure the safety and real-life and clinical applications of carbon nanotubes.

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“…After formulating the buckling problem of functionally graded nanocomposite cylindrical shells under separate external pressures in thermal environments in Shen's study [23,24], the buckling problem of nanocomposite cylindrical shells under combined loads was investigated by Shen and Xiang [25] using boundary layer theory and a singular perturbation procedure. In the literature, in most studies devoted to solving the problem of buckling of nanocomposite circular shells, separate action loads were considered, and the number of studies is limited due to the difficulties of mathematically modeling the combined loads and solving their problems [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Analytical Solution of Stability Problem of Nanocomposite Cylindrical Shells under Combined Loadings in Thermal Environments

Avey,

Fantuzzi,

Sofiyev

2023

Mathematics

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The mathematical modeling of the stability problem of nanocomposite cylindrical shells is one of the applications of partial differential equations (PDEs). In this study, the stability behavior of inhomogeneous nanocomposite cylindrical shells (INH-NCCSs), under combined axial compression and hydrostatic pressure in the thermal environment, is investigated by means of the first-order shear deformation theory (FSDT). The nanocomposite material is modeled as homogeneous and heterogeneous and is based on a carbon nanotube (CNT)-reinforced polymer with the linear variation of the mechanical properties throughout the thickness. In the heterogeneous case, the mechanical properties are modeled as the linear function of the thickness coordinate. The basic equations are derived as partial differential equations and solved in a closed form, using the Galerkin procedure, to determine the critical combined loads for the selected structure in thermal environments. To test the reliability of the proposed formulation, comparisons with the results obtained by finite element and numerical methods in the literature are accompanied by a systematic study aimed at testing the sensitivity of the design response to the loading parameters, CNT models, and thermal environment.

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Buckling response of CNT based hybrid FG plates using finite element method and machine learning method

Cited by 18 publications

References 35 publications

ANN- PSO modelling for predicting buckling of self-compacting concrete column containing RHA properties

ANN- PSO modelling for predicting buckling of self-compacting concrete column containing RHA properties

Nanotechnological advances in cancer: therapy a comprehensive review of carbon nanotube applications

Analytical Solution of Stability Problem of Nanocomposite Cylindrical Shells under Combined Loadings in Thermal Environments

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