Jin Duan
Civil and Structural Engineering · with Asteris · 2020
A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Civil and Structural Engineering — 21 citations into it
- Building and Construction — 14 citations into it
- Global and Planetary Change — 6 citations into it
- Management, Monitoring, Policy and Law — 5 citations into it
Whom Jin Duan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Hoang Nguyen
- Jerome H. Friedman
- Khabat Khosravi
- N. Deshpande
- Khaleel H. Younis
- D. Tien Bui
- Mahdi Panahi
- Jian Zhou
- Wei Chen
- D. Basak
Papers from Jin Duan's perspective
Ordered by how strongly the researchers Jin Duan trusts endorse each paper. Same corpus, a different order for every researcher.
- 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)
- 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…
- Stochastic gradient boosting
- Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm
- Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition (IEEE Congress on Evolutionary Computation)
- 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)
- A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods (Journal of Hydrology)
- Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves (Journal of Cleaner Production)
- Compressive strength prediction of recycled concrete based on deep learning (Construction and Building Materials)
- Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories (Safety Science)