Van Quan Tran 
- (2024): Interpretable machine learning model for evaluating mechanical properties of concrete made with recycled concrete aggregate. Dans: Structural Concrete, v. 25, n. 4 (août 2024).
- (2023): Interpretable machine learning model for autogenous shrinkage prediction of low-carbon cementitious materials. Dans: Construction and Building Materials, v. 396 (septembre 2023).
- (2023): Data‐driven approach for investigating and predicting of compressive strength of fly ash–slag geopolymer concrete. Dans: Structural Concrete, v. 24, n. 6 (2 novembre 2023).
- (2023): Data-driven approach for investigating and predicting rutting depth of asphalt concrete containing reclaimed asphalt pavement. Dans: Construction and Building Materials, v. 377 (mai 2023).
- (2018): Modelisation of chloride reactive transport in concrete including thermodynamic equilibrium, kinetic control and surface complexation. Dans: Cement and Concrete Research, v. 110 (août 2018).
- (2022): Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms. Dans: Frontiers of Structural and Civil Engineering, v. 16, n. 10 (novembre 2022).
- (2022): Application of machine learning technique for predicting and evaluating chloride ingress in concrete. Dans: Frontiers of Structural and Civil Engineering, v. 16, n. 9 (septembre 2022).
- (2022): Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach. Dans: Construction and Building Materials, v. 323 (mars 2022).
- (2022): Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials. Dans: Construction and Building Materials, v. 328 (avril 2022).
- (2022): Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials. Dans: Journal of Building Engineering, v. 49 (mai 2022).
- (2021): Development of deep neural network model to predict the compressive strength of rubber concrete. Dans: Construction and Building Materials, v. 301 (septembre 2021).
- (2022): Using geochemical model for predicting chloride ingress into saturated concrete. Dans: Magazine of Concrete Research, v. 74, n. 6 (mars 2022).
- (2021): Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model. Dans: Advances in Civil Engineering, v. 2021 (janvier 2021).
- (2021): Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network. Dans: Advances in Civil Engineering, v. 2021 (janvier 2021).
- (2021): Temperature effects on chloride binding capacity of cementitious materials. Dans: Magazine of Concrete Research, v. 73, n. 15 (août 2021).
- (2018): Requirements and possible simplifications for multi-ionic transport models – Case of concrete subjected to wetting-drying cycles in marine environment. Dans: Construction and Building Materials, v. 164 (mars 2018).
- (2018): A numerical model including thermodynamic equilibrium, kinetic control and surface complexation in order to explain cation type effect on chloride binding capability of concrete. Dans: Construction and Building Materials, v. 191 (décembre 2018).
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