What is he best known for?
The fields of study he is best known for:
- Statistics
- Structural engineering
- Artificial intelligence
Heung-Fai Lam mainly investigates Structural health monitoring, Bayesian probability, Structural engineering, Probabilistic logic and Algorithm.
His Structural health monitoring study incorporates themes from Data mining and Benchmark.
His Bayesian probability research is included under the broader classification of Artificial intelligence.
His Structural system and Buckling study in the realm of Structural engineering interacts with subjects such as Noise, Field and Simple.
His Probabilistic logic study which covers System identification that intersects with Adaptive algorithm and Manifold.
His studies in Algorithm integrate themes in fields like Parameter space and Modal.
His most cited work include:
- Phase I IASC-ASCE Structural Health Monitoring Benchmark Problem Using Simulated Data (274 citations)
- A probabilistic approach to structural model updating (116 citations)
- Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study (91 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Structural engineering, Bayesian probability, Algorithm, Vibration and Modal.
His study explores the link between Structural engineering and topics such as Bayesian inference that cross with problems in Test data.
His Bayesian probability research incorporates elements of Probabilistic logic, Probability density function, Structural health monitoring and System identification.
His study looks at the relationship between Structural health monitoring and topics such as Mathematical optimization, which overlap with Entropy.
His work is dedicated to discovering how Algorithm, Markov chain Monte Carlo are connected with Time domain and Operational Modal Analysis and other disciplines.
The study incorporates disciplines such as Slab, Modal testing, Truss and Identification in addition to Modal.
He most often published in these fields:
- Structural engineering (37.75%)
- Bayesian probability (26.49%)
- Algorithm (23.18%)
What were the highlights of his more recent work (between 2015-2021)?
- Structural engineering (37.75%)
- Algorithm (23.18%)
- Markov chain Monte Carlo (13.91%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Structural engineering, Algorithm, Markov chain Monte Carlo, Bayesian inference and Bayesian probability.
His work on Structural engineering is being expanded to include thematically relevant topics such as Vibration.
Monte Carlo method is closely connected to Markov chain in his research, which is encompassed under the umbrella topic of Bayesian inference.
His Bayesian probability research is multidisciplinary, incorporating perspectives in Modal, Damage detection, Degrees of freedom, Simulation and System identification.
Heung-Fai Lam interconnects Mode, Identification, Structural health monitoring and Modal analysis, Modal testing in the investigation of issues within Modal.
As a part of the same scientific family, Heung-Fai Lam mostly works in the field of Structural health monitoring, focusing on Fast Fourier transform and, on occasion, Normal mode.
Between 2015 and 2021, his most popular works were:
- Study of wake characteristics of a vertical axis wind turbine by two- and three-dimensional computational fluid dynamics simulations (89 citations)
- Turbulence effects on the wake characteristics and aerodynamic performance of a straight-bladed vertical axis wind turbine by wind tunnel tests and large eddy simulations (36 citations)
- Bayesian operational modal analysis and Markov chain Monte Carlo-based model updating of a factory building (33 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Structural engineering
- Artificial intelligence
Heung-Fai Lam mainly focuses on Modal, Structural engineering, Bayesian probability, Algorithm and Markov chain Monte Carlo.
His studies deal with areas such as Modal testing and Identification as well as Modal.
Heung-Fai Lam works in the field of Structural engineering, focusing on Structural health monitoring in particular.
Heung-Fai Lam combines subjects such as Finite element method and System dynamics with his study of Algorithm.
Heung-Fai Lam has included themes like Entropy, Discrete optimization, Optimality criterion and System identification in his Finite element method study.
His study in Markov chain Monte Carlo is interdisciplinary in nature, drawing from both Damage detection, Bayesian inference, Markov chain, Track and Modal analysis.
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