Mohammad AlHamaydeh

Civil and Structural Engineering · 2013–2021

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Civil and Structural Engineering — 43 citations into it
• Mechanics of Materials — 11 citations into it
• Artificial Intelligence — 9 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Mechanical Engineering — 8 citations into it
• Control and Systems Engineering — 7 citations into it

Whom Mohammad AlHamaydeh trusts overall

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

1. Charles R. Farrar
2. Keith Worden
3. Radford M. Neal
4. Sandeep Sony
5. Vin de Silva
6. Dongming Feng
7. T.G. Ritto
8. M. Mitra

Papers from Mohammad AlHamaydeh's perspective

Ordered by how strongly the researchers Mohammad AlHamaydeh trusts endorse each paper. Same corpus, a different order for every researcher.

1. Vibration–based structural damage identification (Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences)
2. The application of machine learning to structural health monitoring (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
3. A review of structural health monitoring literature 1996-2001
4. Pattern Recognition and Machine Learning
5. Effects of environmental and operational variability on structural health monitoring (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
6. An introduction to structural health monitoring (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
7. Emerging artificial intelligence methods in structural engineering (Engineering structures)
8. Damage identification using inverse methods (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
9. Structural Health Monitoring: A Machine Learning Perspective
10. Global Versus Local Methods in Nonlinear Dimensionality Reduction

Their papers

• Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021) — 524 citations
• Experimental and numerical investigations of the compressive behavior of concrete filled steel tubes (CFSTs) (2013) — 217 citations