International Journal Papers
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2025
8. Physics-guided machine learning for forming-limit assessments of advanced high-strength steels,
Nhat-Tam Nguyen, Minh Tien Tran, Xuan Minh Nguyen, Ho Won Lee, Seong-Hoon Kang, Young-Seok Oh, Hyunki Kim, Dong-Kyu Kim,
International Journal of Mechanical Sciences, 287, 2025, 109959 (IF 7.1, JCR 2.6%) https://doi.org/10.1016/j.ijmecsci.2025.109959
2024
7. Ductile fracture locus under various deformation modes with negative-to-positive stress triaxiality,
Jong-Hyeok Kwon, Jeong-Min Heo, Nhat-Tam Nguyen, Minh Tien Tran, Ho Won Lee, Seong-Hoon Kang, Ho Seon Joo, KiHo Rhee, Sung-Soo Park, Dong Wan Kim, Yong-Gyun Jeong, Dong-Kyu Kim,
International Journal of Mechanical Sciences, 279, 2024, 109615 (IF 7.1, JCR 2.6%) https://doi.org/10.1016/j.ijmecsci.2024.109615
6. Robust detection of ductile fracture by acoustic emission data-driven unsupervised learning,
Jong-Hyeok Kwon, Nhat-Tam Nguyen (1), Minh Tien Tran, Ho Won Lee, Ho Seon Joo, KiHo Rhee, Sung-Soo Park, Dong Wan Kim, Yong-Gyun Jeong, Dong-Kyu Kim,
International Journal of Mechanical Sciences, 277, 2024, 109420 (IF 7.1, JCR 2.6%) https://doi.org/10.1016/j.ijmecsci.2024.109420
2023
5. A novel approach to investigate the mechanical properties of the material for bridge health monitoring using convolutional neural network,
Toan Pham-Bao, Tam Nguyen-Nhat*, Nhi Ngo-Kieu,
Structure and Infrastructure Engineering, 20(6), 2023, 846-866 (IF 2.6, JCR Q1) https://doi.org/10.1080/15732479.2022.2127792
4. Inspecting Spectral Centroid and Relative Power of Allocated Spectra Using Artificial Neural Network for Damage Diagnosis in Beam Structures Under Moving Loads,
Tam Nguyen-Nhat, Luan Vuong-Cong, Vien Le-Ngoc, Toan Pham-Bao,
Journal of Vibration Engineering & Technologies, 12(3), 2023, 4617-4635 (IF 2.1, JCR Q2) https://doi.org/10.1007/s42417-023-01140-y
2022
3. Energy dissipation‐based material deterioration assessment using random decrement technique and convolutional neural network: A case study of Saigon bridge in Ho Chi Minh City, Vietnam,
Toan Pham‐Bao, Nhi Ngo‐Kieu, Luan Vuong‐Cong, Tam Nguyen‐Nhat*,
Structural Control and Health Monitoring, 29, 2022, e2956 (IF 4.6, JCR Q1) https://doi.org/10.1002/stc.2956
2021
2. Deep learning-based signal processing for evaluating energy dispersal in bridge structures,
Nhi Ngo-Kieu, Thao Nguyen-Da, Toan Pham-Bao, Tam Nguyen-Nhat, Hung Nguyen-Xuan,
Journal of Zhejiang University-SCIENCE A, 22(8), 2021, 672-680 (IF 3.4, JCR Q2) https://doi.org/10.1631/jzus.A2000414
2020
1. A novel approach based on viscoelastic parameters for bridge health monitoring: A case study of Saigon bridge in Ho Chi Minh City–Vietnam,
Thao D Nguyen, Thanh Q Nguyen, Tam N Nhat, H Nguyen-Xuan, Nhi K Ngo,
Mechanical Systems and Signal Processing, 141, 2020, 106728 (IF 7.9, JCR 2%) https://doi.org/10.1016/j.ymssp.2020.106728.
Physics-guided machine learning for forming-limit assessments of advanced high-strength steels,
This study employed physics-guided neural networks (PGNNs) that incorporate physical laws for deformation prediction to create forming limit diagrams (FLDs) for automotive advanced high-strength steels (AHSSs). These neural networks predict the deformation of dual- and complex-phase steels by using representative parameters from the constitutive phase and texture orientation distribution extracted from electron backscatter diffraction measurements. A novel learning mechanism, student-to-student learning, was introduced to incorporate texture evolution into the training processes. Additionally, the model was used to investigate the impact of microstructural characteristics on forming limits and to obtain valuable insights for developing strategies to enhance formability.
International Journal of Mechanical Sciences, 287, 2025, 109959 (IF 7.1, JCR 2.6%) https://doi.org/10.1016/j.ijmecsci.2025.109959
Robust detection of ductile fracture by acoustic emission data-driven unsupervised learning,
This study introduces a novel methodology for detecting the onset of ductile fracture by using acoustic emission signals. Unlike traditional methods that rely on standard characteristics, this approach utilizes acoustic emission signals that are depicted through vivid and direct feature images. These images represent short-term and relative energies, which are termed as energy allocation maps. These maps are generated utilizing a novel signal processing technique that employs the maximal overlap discrete wavelet transform for multi-resolution analysis. A set of stacked autoencoders is accordingly configured to transform this map into a singular value that represents the condition of the material. Consequently, a ductile fracture indicator is proposed to identify the onset of fracture.
International Journal of Mechanical Sciences, 277, 2024, 109420 (IF 7.1, JCR 2.6%) https://doi.org/10.1016/j.ijmecsci.2024.109420
Energy dissipation‐based material deterioration assessment using random decrement technique and convolutional neural network: A case study of Saigon bridge in Ho Chi Minh City, Vietnam
The vibration response of these structures includes two main components as a determining component and a stochastic component. Thus, using vibration data, the structural health monitoring (SHM) process for these structures requires eliminating random parts impact. The random decrement (RD) signature, a known technique to serve this requirement, is applied to analyze the bridge's vibrations under the ambient load (random excitation) in this study. Then, a new damage index, called the loss factor function (LF), determined from the power spectral density (PSD) of vibration modes, is used to assess material deterioration. In fact, only some vibration modes of structures that occur with large amplitude are considered to be determined.
Structural Control and Health Monitoring, 29, 2022, e2956 (IF 4.6, JCR Q1) https://doi.org/10.1002/stc.2956
A novel approach to investigate the mechanical properties of the material for bridge health monitoring using convolutional neural network
This study focuses on the general evaluation of mechanical factors consistent with reality. The viscoelastic model of material will be applied to set up and solve the governing differential equation of the beams with material characteristics involving the elastic modulus (E) and the viscous coefficient (C). The loss factor function (LF) distribution-based material investigation using Convolutional Neural Network (CNN) is proposed with high performance and accuracy.
Structure and Infrastructure Engineering, 20(6), 2023, 846-866 (IF 2.6, JCR Q1) https://doi.org/10.1080/15732479.2022.2127792
Inspecting Spectral Centroid and Relative Power of Allocated Spectra Using Artificial Neural Network for Damage Diagnosis in Beam Structures Under Moving Loads
This work designs a procedure for structural damage assessment in beams under moving loads. The proposal is based on the respective alterations of hybrid vibrational factors with respect to the structural changes, whereby matching them for accurate predictions. These hybrid factors are extracted from the vibrational responses and then are employed for an artificial neural network (ANN) to assess the structural condition.
Journal of Vibration Engineering & Technologies, 12(3), 2023, 4617-4635 (IF 2.1, JCR Q2) https://doi.org/10.1007/s42417-023-01140-y
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