Hai-Bang Ly

Civil and Structural Engineering · 2019–2021

A researcher in the Science corpus: 6 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Civil and Structural Engineering — 42 citations into it
• Global and Planetary Change — 29 citations into it
• Artificial Intelligence — 24 citations into it
• Management, Monitoring, Policy and Law — 24 citations into it
• Materials Chemistry — 11 citations into it
• Building and Construction — 9 citations into it
• Statistics and Probability — 9 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Hai-Bang Ly trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. G. De’ath
2. S. Menard
3. Cort J. Willmott
4. Jane Elith
5. Maciej Ziȩba
6. Tianfeng Chai
7. Yanru Zhang
8. Trevor J. Hastie
9. Binh Thai Pham
10. Ali Behnood

Who trusts Hai-Bang Ly overall

1. Octavio Loyola-González

Papers from Hai-Bang Ly's perspective

Ordered by how strongly the researchers Hai-Bang Ly trusts endorse each paper. Same corpus, a different order for every researcher.

1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
2. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
3. Random Forests
4. A working guide to boosted regression trees.
5. Hardening mechanisms of an alkaline-activated class F fly ash
6. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm…
7. Extreme learning machine: Theory and applications
8. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
9. Engineering Properties of Alkali-Activated Fly Ash Concrete
10. Machine learning of molecular electronic properties in chemical compound space

Their papers

• Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees (2019) — 79 citations
• Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil (2021) — 583 citations
• Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model (2021) — 83 citations
• A spatially explicit deep learning neural network model for the prediction of landslide susceptibility (2020) — 294 citations
• Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches (2019) — 136 citations
• A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods (2019) — 629 citations