Izvorni znanstveni članak

https://doi.org/10.17559/TV-20250312002461

Neural Network Identification of the Parameters of Ultra-High-Performance Concrete Bridges

Zhaofeng Liu ; School of Smart Construction and Energy Engineering, Hunan Institute of Engineering, Hunan, China
Yonglei Jiang ; Guizhou Shunkang Testing Co., Ltd., Guizhou, China
Tengwen Wang ; Guizhou Shunkang Testing Co., Ltd., Guizhou, China
Jianqiu He ; Guizhou Jinxing Technology Engineering Co., Ltd., Guizhou, China *

* Dopisni autor.

Sažetak

This study focuses on an ultra-high performance concrete bridge, utilizing the natural fundamental frequency and mid-span deflection as input data to construct a backpropagation neural network prediction model. The model inversely identifies two critical material parameters of ultra-high performance concrete - elastic modulus and unit weight - to minimize discrepancies between actual values and design values, thereby establishing a computational model that better approximates the real bridge structure. The results demonstrate the feasibility of the backpropagation neural network prediction model for identifying and correcting structural parameters, with the model exhibiting high prediction accuracy. This approach provides a valuable reference for acquiring actual structural parameters in bridge engineering.

Ključne riječi

backpropagation neural network; bridge engineering; deflection; elastic modulus; natural fundamental frequency; parameter identification; unit weight

Hrčak ID:

332852

URI

https://hrcak.srce.hr/332852

Datum izdavanja:

29.6.2025.

Posjeta: 616 *