Hai-Bang Ly 
- (2024): Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide modified asphalt at medium and high temperatures. In: Frontiers of Structural and Civil Engineering, v. 18, n. 6 (Juni 2024).
- (2024): Enhanced bond strength prediction in corroded reinforced concrete using optimized ML models. In: Structures, v. 63 (Mai 2024).
- (2024): Hybrid Machine learning Techniques-Aided design of corroded reinforced concrete beams. In: Computers & Structures, v. 298 (Juli 2024).
- (2023): Novel approaches to predict the Marshall parameters of basalt fiber asphalt concrete. In: Construction and Building Materials, v. 400 (Oktober 2023).
- (2024): Predicting axial compression capacity of CFDST columns and design optimization using advanced machine learning techniques. In: Structures, v. 59 (Januar 2024).
- (2023): Advancing basalt fiber asphalt concrete design: A novel approach using gradient boosting and metaheuristic algorithms. In: Case Studies in Construction Materials, v. 19 (Dezember 2023).
- (2023): Ensemble XGBoost schemes for improved compressive strength prediction of UHPC. In: Structures, v. 57 (November 2023).
- (2023): Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete. In: Frontiers of Structural and Civil Engineering, v. 17, n. 2 (Februar 2023).
- (2023): Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models. In: Construction and Building Materials, v. 369 (März 2023).
- (2023): Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis. In: Construction and Building Materials, v. 367 (Februar 2023).
- (2022): Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms. In: Frontiers of Structural and Civil Engineering, v. 16, n. 10 (November 2022).
- (2022): Neural network approach for GO-modified asphalt properties estimation. In: Case Studies in Construction Materials, v. 17 (Dezember 2022).
- (2022): Ensemble Tree-Based Approach to Predict the Rotation Capacity of Wide-Flange Beams. In: Advances in Civil Engineering, v. 2022 (Januar 2022).
- (2023): Novel ensemble approach to predict the ultimate axial load of CFST columns with different cross-sections. In: Structures, v. 47 (Januar 2023).
- (2021): Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony. In: Construction and Building Materials, v. 284 (Mai 2021).
- (2021): Development of deep neural network model to predict the compressive strength of rubber concrete. In: Construction and Building Materials, v. 301 (September 2021).
- (2021): Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model. In: Advances in Civil Engineering, v. 2021 (Januar 2021).
- (2021): Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach. In: Advances in Civil Engineering, v. 2021 (Januar 2021).
- (2020): Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model. In: The Open Construction and Building Technology Journal, v. 14, n. 1 (18 Februar 2020).
- (2020): Soil Unconfined Compressive Strength Prediction Using Random Forest (RF) Machine Learning Model. In: The Open Construction and Building Technology Journal, v. 14, n. 1 (18 Februar 2020).
- (2020): Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model. In: The Open Construction and Building Technology Journal, v. 14, n. 1 (18 Februar 2020).
- (2021): Temperature effects on chloride binding capacity of cementitious materials. In: Magazine of Concrete Research, v. 73, n. 15 (August 2021).
Structurae kooperiert mit
