| Classification of fatigue crack damage in polycrystalline alloy structures using convolutional neural networks H Alqahtani, S Bharadwaj, A Ray Engineering Failure Analysis 119, 104908, 2021 | 34 | 2021 |
| Appealing perspectives of the structural, electronic, elastic and optical properties of LiRCl 3 (R= Be and Mg) halide perovskites: a DFT study N Rahman, M Husain, V Tirth, A Algahtani, H Alqahtani, T Al-Mughanam, ... RSC advances 13 (27), 18934-18945, 2023 | 24 | 2023 |
| Neural network-based automated assessment of fatigue damage in mechanical structures H Alqahtani, A Ray Machines 8 (4), 85, 2020 | 10 | 2020 |
| Fatigue damage detection and risk assessment via neural network modeling of ultrasonic signals H Alqahtani, A Ray Fatigue & Fracture of Engineering Materials & Structures 45 (6), 1587-1604, 2022 | 4 | 2022 |
| Forecasting and detection of fatigue cracks in polycrystalline alloys with ultrasonic testing via discrete wavelet transform H Alqahtani, A Ray Journal of Nondestructive Evaluation, Diagnostics and Prognostics of …, 2021 | 4 | 2021 |
| Feature selection of surface topography parameters for fatigue‐damage detection using Pearson correlation method and neural network analysis H Alqahtani, A Ray Fatigue & Fracture of Engineering Materials & Structures 46 (5), 1810-1820, 2023 | 2 | 2023 |
| Feature extraction and neural network-based fatigue damage detection and classification H Alqahtani, A Ray Neural Computing and Applications 34 (23), 21253-21273, 2022 | 2 | 2022 |
| Deep Learning Approach to Real-Time Health Monitoring for Fatigue Damage Detection and Classification H Alqahtani The Pennsylvania State University, 2021 | 2 | 2021 |
| Correction: Appealing perspectives of the structural, electronic, elastic and optical properties of LiRCl 3 (R= Be and Mg) halide perovskites: a DFT study N Rahman, M Husain, V Tirth, A Algahtani, H Alqahtani, T Al-Mughanam, ... RSC advances 13 (40), 27964-27964, 2023 | 1 | 2023 |
| Detection of fatigue damage via neural network analysis of surface topography measurements H Alqahtani, A Ray Current Perspectives and New Directions in Mechanics, Modelling and Design …, 2022 | 1 | 2022 |
| Computational insights of double perovskite X2CaCdH6 (X= Rb and Cs) hydride materials for hydrogen storage applications: A DFT analysis W Azeem, S Hussain, MK Shahzad, F Azad, G Khan, V Tirth, H Alqahtani, ... International Journal of Hydrogen Energy 79, 514-524, 2024 | | 2024 |
| Surface texture analysis in polycrystalline alloys via an artificial neural network H Alqahtani, A Ray Measurement 227, 114328, 2024 | | 2024 |
| Convolutional neural network for risk assessment in polycrystalline alloy structures via ultrasonic testing H Alqahtani, A Ray Fatigue & Fracture of Engineering Materials & Structures 47 (1), 140-152, 2024 | | 2024 |
| An Automated System for Surface Damage Detection Using Support Vector Machine. H Alqahtani Journal of Engineering Research (2307-1877), 2023 | | 2023 |
| Artificial Neural Network for Automatic Prediction of the Surface Finishing via Classification of the Surface Texture H Alqahtani, A Ray ASME International Mechanical Engineering Congress and Exposition 86717 …, 2022 | | 2022 |
| Fatigue Damage Detection and Risk Assessment via Wavelet Transform and Neural Network Analysis of Ultrasonic Signals H ALQAHTANI Authorea Preprints, 2021 | | 2021 |
| Analysis of Fatigue Crack Evolution Using In-Situ Testing H Alqahtani, E Keller, A Ray, A Basak University of Texas at Austin, 2019 | | 2019 |
| Analysis of Fatigue Crack Evolution using In-Situ Testing Hassan Alqahtani1, 2, Eric Keller1, Asok Ray1, 3, 4, Amrita Basak1, 4 H Alqahtani, E Keller, A Ray, A Basak | | |
