Tien-Thinh Le

Nhà nghiên cứu Hồ sơ 6/10

120 công bố · 4.334 trích dẫn · h-index 38

Thông tin

🏛️Nơi công tác: Đại học Phenikaa
🔬Lĩnh vực: Toán học
🆔ORCID: 0000-0002-1603-5000
✍️Hồ sơ chưa đầy đủ — bổ sung thông tin

Chỉ số h-index 38 xếp học giả này vào lĩnh vực Toán học, thuộc nhóm 5% dẫn đầu trong 1.789 hồ sơ cùng lĩnh vực của danh bạ này.

Công bố tiêu biểu

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete346 trích dẫn
Materials · 2019 · DOI

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation232 trích dẫn
Sustainability · 2020 · DOI

Development of advanced artificial intelligence models for daily rainfall prediction226 trích dẫn
Atmospheric Research · 2020 · DOI

Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis169 trích dẫn
The Science of The Total Environment · 2019 · DOI

Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques127 trích dẫn
Engineering With Computers · 2021 · DOI

Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models119 trích dẫn
Neural Computing and Applications · 2020 · DOI

Correlating the Unconfined Compressive Strength of Rock with the Compressional Wave Velocity Effective Porosity and Schmidt Hammer Rebound Number Using Artificial Neural Networks117 trích dẫn
Rock Mechanics and Rock Engineering · 2022 · DOI

AI-powered GUI for prediction of axial compression capacity in concrete-filled steel tube columns117 trích dẫn
Neural Computing and Applications · 2024 · DOI

Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks104 trích dẫn
Transportation Geotechnics · 2021 · DOI

Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete104 trích dẫn
Materials · 2020 · DOI

Về lĩnh vực Toán học

1.789học giả

30.127công bố

381.053trích dẫn

4.2h-index trung bình

Hướng nghiên cứu phổ biến trong lĩnh vực:

Composite Structure Analysis and OptimizationAdvanced machining processes and optimizationAdvanced Mathematical Modeling in EngineeringOptimization and Variational AnalysisNonlinear Differential Equations AnalysisStability and Controllability of Differential Equations

Nơi có nhiều học giả lĩnh vực này nhất: