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For: Ly H, Le LM, Duong HT, Nguyen TC, Pham TA, Le T, Le VM, Nguyen-ngoc L, Pham BT. Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections. Applied Sciences 2019;9:2258. [DOI: 10.3390/app9112258] [Cited by in Crossref: 46] [Cited by in F6Publishing: 11] [Article Influence: 15.3] [Reference Citation Analysis]
Number Citing Articles
1 Yariyan P, Omidvar E, Minaei F, Ali Abbaspour R, Tiefenbacher JP. An optimization on machine learning algorithms for mapping snow avalanche susceptibility. Nat Hazards 2022;111:79-114. [DOI: 10.1007/s11069-021-05045-5] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Sammen SS, Ghorbani MA, Malik A, Tikhamarine Y, Amirrahmani M, Al-ansari N, Chau K. Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway. Applied Sciences 2020;10:5160. [DOI: 10.3390/app10155160] [Cited by in Crossref: 19] [Cited by in F6Publishing: 8] [Article Influence: 9.5] [Reference Citation Analysis]
3 Dao DV, Adeli H, Ly H, Le LM, Le VM, Le T, Pham BT. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability 2020;12:830. [DOI: 10.3390/su12030830] [Cited by in Crossref: 49] [Article Influence: 24.5] [Reference Citation Analysis]
4 Le T, Nguyen H, Pham BT, Nguyen MH, Pham C, Nguyen N, Le T, Ly H. Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt. Applied Sciences 2020;10:5242. [DOI: 10.3390/app10155242] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
5 Ly H, Pham BT. Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model. TOBCTJ 2020;14:268-77. [DOI: 10.2174/1874836802014010268] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
6 Ly H, Le T, Le LM, Tran VQ, Le VM, Vu HT, Nguyen QH, Pham BT. Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams. Applied Sciences 2019;9:5458. [DOI: 10.3390/app9245458] [Cited by in Crossref: 33] [Cited by in F6Publishing: 3] [Article Influence: 11.0] [Reference Citation Analysis]
7 Nguyen T, Ly H, Mai HT, Tran VQ. Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network. Advances in Materials Science and Engineering 2020;2020:1-8. [DOI: 10.1155/2020/9682740] [Cited by in Crossref: 9] [Cited by in F6Publishing: 2] [Article Influence: 4.5] [Reference Citation Analysis]
8 Qi C, Ly H, Minh Le L, Yang X, Guo L, Thai Pham B. Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony. Construction and Building Materials 2021;284:122857. [DOI: 10.1016/j.conbuildmat.2021.122857] [Cited by in Crossref: 15] [Cited by in F6Publishing: 9] [Article Influence: 15.0] [Reference Citation Analysis]
9 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: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 Pham BT, Le LM, Le T, Bui KT, Le VM, Ly H, Prakash I. Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research 2020;237:104845. [DOI: 10.1016/j.atmosres.2020.104845] [Cited by in Crossref: 43] [Article Influence: 21.5] [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: 7] [Cited by in F6Publishing: 1] [Article Influence: 7.0] [Reference Citation Analysis]
12 Qi C, Ly HB, Chen Q, Le TT, Le VM, Pham BT. Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach. Chemosphere 2020;244:125450. [PMID: 31816548 DOI: 10.1016/j.chemosphere.2019.125450] [Cited by in Crossref: 37] [Cited by in F6Publishing: 10] [Article Influence: 12.3] [Reference Citation Analysis]
13 Jiao P, Alavi AH. Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends. International Materials Reviews 2021;66:365-93. [DOI: 10.1080/09506608.2020.1815394] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
14 Tran VQ, Liu W. Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network. Advances in Civil Engineering 2021;2021:1-8. [DOI: 10.1155/2021/6656084] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
15 Nguyen T, Tran N, Nguyen D. Prediction of Critical Buckling Load of Web Tapered I-Section Steel Columns Using Artificial Neural Networks. Int J Steel Struct 2021;21:1159-81. [DOI: 10.1007/s13296-021-00498-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
16 Tran QA, Ho LS, Le HV, Prakash I, Pham BT. Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system. Neural Comput & Applic. [DOI: 10.1007/s00521-022-06891-5] [Reference Citation Analysis]
17 Ly H, Le T, Vu HT, Tran VQ, Le LM, Pham BT. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability 2020;12:2709. [DOI: 10.3390/su12072709] [Cited by in Crossref: 25] [Article Influence: 12.5] [Reference Citation Analysis]
18 Pham BT, Nguyen-thoi T, Ly H, Nguyen MD, Al-ansari N, Tran V, Le T. Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination. Sustainability 2020;12:2339. [DOI: 10.3390/su12062339] [Cited by in Crossref: 31] [Cited by in F6Publishing: 4] [Article Influence: 15.5] [Reference Citation Analysis]
19 Merayo Fernández D, Rodríguez-prieto A, Camacho AM. Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data. Metals 2020;10:904. [DOI: 10.3390/met10070904] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 4.5] [Reference Citation Analysis]
20 Ly H, Pham BT, Le LM, Le T, Le VM, Asteris PG. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Comput & Applic 2021;33:3437-58. [DOI: 10.1007/s00521-020-05214-w] [Cited by in Crossref: 27] [Cited by in F6Publishing: 9] [Article Influence: 13.5] [Reference Citation Analysis]
21 Nguyen T, Ly H, Tran VQ, Afan H. Investigation of ANN Architecture for Predicting Load-Carrying Capacity of Castellated Steel Beams. Complexity 2021;2021:1-14. [DOI: 10.1155/2021/6697923] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Ly, Pham, Dao, Le, Le, Le. Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete. Applied Sciences 2019;9:3841. [DOI: 10.3390/app9183841] [Cited by in Crossref: 46] [Cited by in F6Publishing: 7] [Article Influence: 15.3] [Reference Citation Analysis]
23 Nguyen H, Le T, Pham C, Le T, Ho LS, Le VM, Pham BT, Ly H. Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Applied Sciences 2019;9:3172. [DOI: 10.3390/app9153172] [Cited by in Crossref: 31] [Cited by in F6Publishing: 10] [Article Influence: 10.3] [Reference Citation Analysis]
24 Nguyen T, Tran N, Nguyen D. Prediction of Axial Compression Capacity of Cold-Formed Steel Oval Hollow Section Columns Using ANN and ANFIS Models. Int J Steel Struct. [DOI: 10.1007/s13296-021-00557-z] [Reference Citation Analysis]
25 Pham BT, Phong TV, Nguyen HD, Qi C, Al-ansari N, Amini A, Ho LS, Tuyen TT, Yen HPH, Ly H, Prakash I, Tien Bui D. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water 2020;12:239. [DOI: 10.3390/w12010239] [Cited by in Crossref: 25] [Cited by in F6Publishing: 7] [Article Influence: 12.5] [Reference Citation Analysis]
26 Pham TA, Ly H, Tran VQ, Giap LV, Vu HT, Duong HT. Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest. Applied Sciences 2020;10:1871. [DOI: 10.3390/app10051871] [Cited by in Crossref: 15] [Cited by in F6Publishing: 1] [Article Influence: 7.5] [Reference Citation Analysis]
27 Cavaleri L, Asteris PG, Psyllaki PP, Douvika MG, Skentou AD, Vaxevanidis NM. Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks. Applied Sciences 2019;9:2788. [DOI: 10.3390/app9142788] [Cited by in Crossref: 45] [Cited by in F6Publishing: 9] [Article Influence: 15.0] [Reference Citation Analysis]
28 Tran VQ. Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction. Journal of Cleaner Production 2022;355:131683. [DOI: 10.1016/j.jclepro.2022.131683] [Reference Citation Analysis]
29 Nguyen H, Pham BT, Son LH, Thang NT, Ly H, Le T, Ho LS, Le T, Tien Bui D. Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction. Applied Sciences 2019;9:4715. [DOI: 10.3390/app9214715] [Cited by in Crossref: 36] [Cited by in F6Publishing: 10] [Article Influence: 12.0] [Reference Citation Analysis]
30 Ly H, Thai Pham B. Soil Unconfined Compressive Strength Prediction Using Random Forest (RF) Machine Learning Model. TOBCTJ 2020;14:278-85. [DOI: 10.2174/1874836802014010278] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
31 Ly H, Nguyen T, Pham BT, Fu G. Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach. Advances in Civil Engineering 2021;2021:1-14. [DOI: 10.1155/2021/8873993] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
32 Ly H, Nguyen MH, Pham BT. Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Comput & Applic 2021;33:17331-51. [DOI: 10.1007/s00521-021-06321-y] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
33 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: 12] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
34 Alam MS, Sultana N, Hossain SMZ, Islam MS. Hybrid intelligence modeling for estimating shear strength of FRP reinforced concrete members. Neural Comput & Applic. [DOI: 10.1007/s00521-021-06791-0] [Reference Citation Analysis]
35 Ren Q, Li M, Zhang M, Shen Y, Si W. Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm. Applied Sciences 2019;9:2802. [DOI: 10.3390/app9142802] [Cited by in Crossref: 21] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
36 Ly H, Pham BT. Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model. TOBCTJ 2020;14:41-50. [DOI: 10.2174/1874836802014010041] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]