A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain
He Zhang, Ruihong Shen, Yuhui Zhou, Cun Zhang, Zhicheng Zhang, Hesham A Rakha · 2024
Read A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain by He Zhang, Ruihong Shen, Yuhui Zhou, Cun Zhang, Zhicheng Zhang, Hesham A Rakha (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11645099.txt.
Description
He Zhang and co-authors present a neural network method to identify traffic loads on bridges in a 2024 paper. Knowing the loads a bridge bears helps managers control traffic and keep it safe. Older methods suffer from ill-posed math, and plain neural methods lack a clear physical meaning.
Their network is built on Legendre functions and reads loads across the full span and time. It gives both a formula for the load and its history, and it avoids the weak spots at bridge supports. Reported accuracy for speed and force size was near ninety-nine percent in tests. Bridge teams get a faster, clearer load tool.
Catalog record
| CREATOR | He Zhang, Ruihong Shen, Yuhui Zhou, Cun Zhang, Zhicheng Zhang, Hesham A Rakha |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 34 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |