What is he best known for?
The fields of study he is best known for:
- Statistics
- Geotechnical engineering
- Composite material
His primary scientific interests are in Geotechnical engineering, Multivariate adaptive regression splines, Mars Exploration Program, Structural engineering and Nonparametric regression.
Wengang Zhang connects Geotechnical engineering with Relative density in his study.
His Multivariate adaptive regression splines research is multidisciplinary, incorporating perspectives in Artificial neural network, Soft computing and Support vector machine.
The concepts of his Support vector machine study are interwoven with issues in Settlement and Quantum tunnelling.
He has included themes like Excavation and Polynomial regression in his Structural engineering study.
His Nonparametric regression research includes themes of Liquefaction, Soil liquefaction and Variables.
His most cited work include:
- Multivariate adaptive regression splines and neural network models for prediction of pile drivability (187 citations)
- Multivariate adaptive regression splines for analysis of geotechnical engineering systems (160 citations)
- Influence of Particle Breakage on Critical State Line of Rockfill Material (83 citations)
What are the main themes of his work throughout his whole career to date?
Wengang Zhang mainly investigates Geotechnical engineering, Excavation, Structural engineering, Multivariate adaptive regression splines and Finite element method.
The concepts of his Geotechnical engineering study are interwoven with issues in Soil water, Stiffness and Soft computing.
His Excavation research integrates issues from Settlement, Deflection, Soft clay and Groundwater.
His work on Serviceability as part of general Structural engineering study is frequently linked to Bracing, bridging the gap between disciplines.
Wengang Zhang has researched Multivariate adaptive regression splines in several fields, including Pile, Liquefaction and Artificial intelligence.
His work on Plane stress as part of general Finite element method research is frequently linked to Numerical analysis, bridging the gap between disciplines.
He most often published in these fields:
- Geotechnical engineering (62.31%)
- Excavation (29.23%)
- Structural engineering (26.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Geotechnical engineering (62.31%)
- Excavation (29.23%)
- Pile (10.77%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Geotechnical engineering, Excavation, Pile, Reliability and Stability.
His research in Geotechnical engineering is mostly focused on Factor of safety.
His work carried out in the field of Excavation brings together such families of science as Visualization, Deflection, Soft clay and Groundwater.
The various areas that he examines in his Pile study include Penetration, Dilatant and Displacement.
Wengang Zhang combines subjects such as Homogeneity, Monte Carlo method and Data mining with his study of Reliability.
His Stability research incorporates elements of Slope stability, Probabilistic analysis of algorithms, Safety factor and Design charts.
Between 2019 and 2021, his most popular works were:
- State-of-the-art review of soft computing applications in underground excavations (62 citations)
- Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization (30 citations)
- Probabilistic stability analysis of earth dam slope under transient seepage using multivariate adaptive regression splines (30 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Geotechnical engineering
- Composite material
His primary scientific interests are in Geotechnical engineering, Multivariate adaptive regression splines, Soft computing, Random forest and Probabilistic logic.
Wengang Zhang is involved in the study of Geotechnical engineering that focuses on Factor of safety in particular.
His studies deal with areas such as Finite element method and Artificial intelligence as well as Multivariate adaptive regression splines.
In his research on the topic of Finite element method, Excavation is strongly related with Weathering.
His research investigates the connection between Soft computing and topics such as Deflection that intersect with problems in Extreme gradient boosting, Support vector machine and Parametric statistics.
He interconnects Bayesian optimization, Effective stress, Atterberg limits, Ensemble learning and Structural engineering in the investigation of issues within Random forest.
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