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A platform for research: civil engineering, architecture and urbanism
Hybrid physics‐informed neural network with parametric identification for modeling bridge temperature distribution
AbstractThis paper introduces a novel hybrid multi‐model thermo‐temporal physics‐informed neural network (TT‐PINN) framework for thermal loading prediction in composite bridge decks. Unlike the existing PINN applications in heat transfer that focus on simple geometries, this framework uniquely addresses multi‐material domains and realistic boundary conditions through a dual‐network architecture designed for composite structures. The framework further incorporates the environmental boundary conditions of natural convection and solar radiation into the loss function and employs transfer learning for efficient adaptation to varying conditions. Moreover, a transfer learning mechanism enables rapid adaptation to new environmental states, thus markedly reducing the computations as compared to the conventional finite element method (FEM). Through noise‐augmented training and parameter identification, the TT‐PINN effectively handles the real‐world monitoring data uncertainties and allows material property calibration with limited sensor data. The framework's ability to capture complex thermal behavior is validated by studying a cable‐stayed bridge. It significantly reduces the computational costs as compared to the traditional FEM approaches.
Hybrid physics‐informed neural network with parametric identification for modeling bridge temperature distribution
AbstractThis paper introduces a novel hybrid multi‐model thermo‐temporal physics‐informed neural network (TT‐PINN) framework for thermal loading prediction in composite bridge decks. Unlike the existing PINN applications in heat transfer that focus on simple geometries, this framework uniquely addresses multi‐material domains and realistic boundary conditions through a dual‐network architecture designed for composite structures. The framework further incorporates the environmental boundary conditions of natural convection and solar radiation into the loss function and employs transfer learning for efficient adaptation to varying conditions. Moreover, a transfer learning mechanism enables rapid adaptation to new environmental states, thus markedly reducing the computations as compared to the conventional finite element method (FEM). Through noise‐augmented training and parameter identification, the TT‐PINN effectively handles the real‐world monitoring data uncertainties and allows material property calibration with limited sensor data. The framework's ability to capture complex thermal behavior is validated by studying a cable‐stayed bridge. It significantly reduces the computational costs as compared to the traditional FEM approaches.
Hybrid physics‐informed neural network with parametric identification for modeling bridge temperature distribution
Computer aided Civil Eng
Wang, Yanjia (author) / Yang, Dong (author) / Yuan, Ye (author) / Zhang, Jing (author) / Au, Francis T. K. (author)
2025-02-18
Article (Journal)
Electronic Resource
English
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