arXiv (All)
open access
Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines
Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is introduced for modelling aerodynamic self-excited forces in bridge-deck cross-sections using extreme learning machines (ELMs). ELMs, as single-layer feedfo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Deflection and bending moment dynamic load allowance of runway bridges: Theoretical and numerical studies#471678
- Microstructure-based machine learning of damage models including anisotropy, irreversibility and evolution#280532
- Fatigue Life Assessment of Roadway Bridges based on Actual Traffic Loads#320096
- Analytical study of dynamic loads on elastically supported slabs#964224
- Genetic Architecture and Sample Size Impact Relative Performance of Nonlinear Machine Learning and Standard Polygenic Risk Scores#676340
Record · ID 1117986
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive