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For: Le T. Probabilistic modeling of surface effects in nano-reinforced materials. Computational Materials Science 2021;186:109987. [DOI: 10.1016/j.commatsci.2020.109987] [Cited by in Crossref: 17] [Cited by in F6Publishing: 13] [Article Influence: 17.0] [Reference Citation Analysis]
Number Citing Articles
1 Qian D, Zou P, Zhang J, Chen M. Tunability of resonator with pre-compressed springs on thermo-magneto-mechanical coupling band gaps of locally resonant phononic crystal nanobeam with surface effects. Mechanical Systems and Signal Processing 2022;176:109184. [DOI: 10.1016/j.ymssp.2022.109184] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Duong T, Le T, Nguyen S, Le M. Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns. IFS 2022;43:1-19. [DOI: 10.3233/jifs-201628] [Reference Citation Analysis]
3 Denchik A, Kasenov A, Galinovsky A, Musina Z, Abishev K, Tkachuk A. The structure of the Simulation Model of the Probabilistic Process of Forming the Accuracy of the Required Dimensions. PoHEI MB 2022. [DOI: 10.18698/0536-1044-2022-6-36-44] [Reference Citation Analysis]
4 Duong HT, Phan HC, Le T. Critical Buckling Load Evaluation of Functionally Graded Material Plate Using Gaussian Process Regression. Advances in Engineering Research and Application 2022. [DOI: 10.1007/978-3-030-92574-1_30] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Ho NX, Le T, Le MV. Development of artificial intelligence based model for the prediction of Young’s modulus of polymer/carbon-nanotubes composites. Mechanics of Advanced Materials and Structures. [DOI: 10.1080/15376494.2021.1969709] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
6 Nguyen V, Le T, Truong H, Le MV, Ngo V, Nguyen AT, Nguyen HQ, Yousefi S. Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate. Mathematical Problems in Engineering 2021;2021:1-16. [DOI: 10.1155/2021/6815802] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 10.0] [Reference Citation Analysis]
7 Van Nguyen C, Chhuon C, Tangaramvong S, Bui TQ, Limkatanyu S, Rungamornrat J. SBFE analysis of surface loaded elastic layered media with influence of surface/interface energy. International Journal of Mechanical Sciences 2021;197:106302. [DOI: 10.1016/j.ijmecsci.2021.106302] [Reference Citation Analysis]
8 Ho NX, Le T. Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes. Measurement 2021;176:109198. [DOI: 10.1016/j.measurement.2021.109198] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 10.0] [Reference Citation Analysis]
9 Le T, Nguyen V, Le MV, Morabito FC. Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces. Applied Computational Intelligence and Soft Computing 2021;2021:1-10. [DOI: 10.1155/2021/8858545] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 11.0] [Reference Citation Analysis]
10 Le T, Le MV, Di Lorenzo ML. Nanoscale Effect Investigation for Effective Bulk Modulus of Particulate Polymer Nanocomposites Using Micromechanical Framework. Advances in Materials Science and Engineering 2021;2021:1-13. [DOI: 10.1155/2021/1563845] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 Le T, Le MV. Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members. Mater Struct 2021;54. [DOI: 10.1617/s11527-021-01646-5] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 13.0] [Reference Citation Analysis]
12 Le T, Phan HC, Rashid R. Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method. Advances in Civil Engineering 2020;2020:1-16. [DOI: 10.1155/2020/8855069] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
13 Le T. Multiscale Analysis of Elastic Properties of Nano-Reinforced Materials Exhibiting Surface Effects. Application for Determination of Effective Shear Modulus. J Compos Sci 2020;4:172. [DOI: 10.3390/jcs4040172] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
14 Le T. Practical machine learning-based prediction model for axial capacity of square CFST columns. Mechanics of Advanced Materials and Structures. [DOI: 10.1080/15376494.2020.1839608] [Cited by in Crossref: 19] [Cited by in F6Publishing: 15] [Article Influence: 9.5] [Reference Citation Analysis]
15 Folorunso O, Hamam Y, Sadiku R, Ray SS, Adekoya GJ. Statistical characterization and simulation of graphene-loaded polypyrrole composite electrical conductivity. Journal of Materials Research and Technology 2020;9:15788-801. [DOI: 10.1016/j.jmrt.2020.11.045] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
16 Le T, Kankal M. Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading. Advances in Civil Engineering 2020;2020:1-19. [DOI: 10.1155/2020/8832522] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 7.0] [Reference Citation Analysis]