Xuan-Nam Bui

Civil and Structural Engineering · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 6 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Civil and Structural Engineering — 24 citations into it
• Building and Construction — 16 citations into it
• Global and Planetary Change — 7 citations into it
• Management, Monitoring, Policy and Law — 6 citations into it
• Mechanics of Materials — 6 citations into it; #6,108 of 6,291 by trust here, #5,193 by citations
• Statistics and Probability — 5 citations into it

Whom Xuan-Nam Bui trusts overall

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

1. Hoang Nguyen
2. Jerome H. Friedman

Who trusts Xuan-Nam Bui overall

1. Hoang Nguyen

Papers from Xuan-Nam Bui's perspective

Ordered by how strongly the researchers Xuan-Nam Bui trusts endorse each paper. Same corpus, a different order for every researcher.

1. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic… (Landslides)
2. Stochastic gradient boosting
3. 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…
4. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition (IEEE Congress on Evolutionary Computation)
5. Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam (SN Applied Sciences)
6. Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm
7. A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods (Journal of Hydrology)
8. Compressive strength prediction of recycled concrete based on deep learning (Construction and Building Materials)
9. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves (Journal of Cleaner Production)
10. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories (Safety Science)

Their papers

• Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization (2020) — 67 citations
• A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020) — 322 citations
• Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach (2019) — 67 citations
• Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest (2018) — 187 citations