Hai-Bang Ly 
- (2024): Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide modified asphalt at medium and high temperatures. Dans: Frontiers of Structural and Civil Engineering, v. 18, n. 6 (juin 2024).
- (2024): Enhanced bond strength prediction in corroded reinforced concrete using optimized ML models. Dans: Structures, v. 63 (mai 2024).
- (2024): Hybrid Machine learning Techniques-Aided design of corroded reinforced concrete beams. Dans: Computers & Structures, v. 298 (juillet 2024).
- (2023): Novel approaches to predict the Marshall parameters of basalt fiber asphalt concrete. Dans: Construction and Building Materials, v. 400 (octobre 2023).
- (2024): Predicting axial compression capacity of CFDST columns and design optimization using advanced machine learning techniques. Dans: Structures, v. 59 (janvier 2024).
- (2023): Advancing basalt fiber asphalt concrete design: A novel approach using gradient boosting and metaheuristic algorithms. Dans: Case Studies in Construction Materials, v. 19 (décembre 2023).
- (2023): Ensemble XGBoost schemes for improved compressive strength prediction of UHPC. Dans: Structures, v. 57 (novembre 2023).
- (2023): Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete. Dans: Frontiers of Structural and Civil Engineering, v. 17, n. 2 (février 2023).
- (2023): Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models. Dans: Construction and Building Materials, v. 369 (mars 2023).
- (2023): Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis. Dans: Construction and Building Materials, v. 367 (février 2023).
- (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): Neural network approach for GO-modified asphalt properties estimation. Dans: Case Studies in Construction Materials, v. 17 (décembre 2022).
- (2022): Ensemble Tree-Based Approach to Predict the Rotation Capacity of Wide-Flange Beams. Dans: Advances in Civil Engineering, v. 2022 (janvier 2022).
- (2023): Novel ensemble approach to predict the ultimate axial load of CFST columns with different cross-sections. Dans: Structures, v. 47 (janvier 2023).
- (2021): Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony. Dans: Construction and Building Materials, v. 284 (mai 2021).
- (2021): Development of deep neural network model to predict the compressive strength of rubber concrete. Dans: Construction and Building Materials, v. 301 (septembre 2021).
- (2021): Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model. Dans: Advances in Civil Engineering, v. 2021 (janvier 2021).
- (2021): Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach. Dans: Advances in Civil Engineering, v. 2021 (janvier 2021).
- (2020): Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model. Dans: The Open Construction and Building Technology Journal, v. 14, n. 1 (18 février 2020).
- (2020): Soil Unconfined Compressive Strength Prediction Using Random Forest (RF) Machine Learning Model. Dans: The Open Construction and Building Technology Journal, v. 14, n. 1 (18 février 2020).
- (2020): Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model. Dans: The Open Construction and Building Technology Journal, v. 14, n. 1 (18 février 2020).
- (2021): Temperature effects on chloride binding capacity of cementitious materials. Dans: Magazine of Concrete Research, v. 73, n. 15 (août 2021).
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