Viet-Linh Tran 
- (2020): Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete. In: Thin-Walled Structures, v. 151 (Juni 2020).
- (2020): Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns. In: Thin-Walled Structures, v. 152 (Juli 2020).
- (2022): Novel hybrid WOA-GBM model for patch loading resistance prediction of longitudinally stiffened steel plate girders. In: Thin-Walled Structures, v. 177 (August 2022).
- (2024): Machine learning-based model for moment capacity prediction and reliability analysis of PSC beams. In: Structures, v. 62 (April 2024).
- (2024): Efficient framework for ultimate strength prediction and production-based CO2 emission optimization of CFST columns using categorical boosting algorithm and moth flame optimization. In: Composite Structures, v. 333 (April 2024).
- (2024): Predicting shear capacity of rectangular hollow RC columns using neural networks. In: Asian Journal of Civil Engineering, v. 25, n. 3 (Januar 2024).
- (2023): Failure Mode Identification and Shear Strength Prediction of Rectangular Hollow RC Columns Using Novel Hybrid Machine Learning Models. In: Buildings, v. 13, n. 12 (22 November 2023).
- (2024): Hybrid machine learning models for classifying failure modes of unstiffened steel plate girders subjected to patch loading. In: Structures, v. 59 (Januar 2024).
- (2023): Novel hybrid SCA-XGB model for compressive strength of concrete at elevated temperatures. In: Asian Journal of Civil Engineering, v. 25, n. 2 (Oktober 2023).
- (2023): Predicting and optimizing the concrete compressive strength using an explainable boosting machine learning model. In: Asian Journal of Civil Engineering, v. 25, n. 2 (Oktober 2023).
- (2023): One-dimensional convolutional neural network for damage detection of structures using time series data. In: Asian Journal of Civil Engineering, v. 25, n. 1 (Juli 2023).
- (2023): Development of Hybrid Machine Learning Models for Predicting Permanent Transverse Displacement of Circular Hollow Section Steel Members under Impact Loads. In: Buildings, v. 13, n. 6 (23 Mai 2023).
- (2023): Innovative formulas for reinforcing bar bonding failure stress of tension lap splice using ANN and TLBO. In: Construction and Building Materials, v. 369 (März 2023).
- (2022): Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns. In: Buildings, v. 12, n. 10 (20 September 2022).
- (2022): Investigating the Behavior of Steel Flush Endplate Connections at Elevated Temperatures Using FEM and ANN. In: International Journal of Steel Structures, v. 22, n. 5 (September 2022).
- (2022): Revealing the nonlinear behavior of steel flush endplate connections using ANN-based hybrid models. In: Journal of Building Engineering, v. 57 (Oktober 2022).
- (2021): Axial compressive behavior of circular concrete-filled double steel tubular short columns. In: Advances in Structural Engineering, v. 25, n. 2 (Dezember 2021).
- (2021): Computational analysis of axially loaded thin-walled rectangular concrete-filled stainless steel tubular short columns incorporating local buckling effects. In: Structures, v. 34 (Dezember 2021).
- (2021): Evaluation of Seismic Site Amplification Using 1D Site Response Analyses at Ba Dinh Square Area, Vietnam. In: Advances in Civil Engineering, v. 2021 (Januar 2021).
- (2021): A machine learning-based formulation for predicting shear capacity of squat flanged RC walls. In: Structures, v. 29 (Februar 2021).
- (2019): Application of ANN in predicting ACC of SCFST column. In: Composite Structures, v. 228 (November 2019).
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