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Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach

Sparad:
I publikationen:Fire vol. 8, no. 7 (2025), p. 280-295
Huvudupphov: Luo, Jing
Övriga upphov: Tian Guangxin, Chen, Xu, Zhang, Shijie, Liu, Zhen
Utgiven:
MDPI AG
Ämnen:
Temperature distribution
Finite element method
Fire hazards
Wood
Accuracy
Beam-columns
Data processing
Timber
Structural engineers
Thermodynamic properties
Safety engineering
Thermal properties
Computer applications
Automation
Machine learning
Fire prevention
Heat conductivity
Deep learning
Radiation
Batch processing
Heat transfer
Simulation
Construction
Temperature dependence
Neural networks
Artificial intelligence
Temperature effects
Predictions
Computational efficiency
Glulam
Mathematical models
Engineering
Parametric analysis
Environmental
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Abstrakt:This paper presents an integrated computational framework for predicting temperature fields in glulam beam–column connections under fire conditions, combining finite element modeling, automated parametric analysis, and deep learning techniques. A high-fidelity heat transfer finite element model was developed, incorporating the anisotropic thermal properties of wood and temperature-dependent material behavior, validated against experimental data with strong agreement. To enable large-scale parametric studies, an automated Abaqus model modification and data processing system was implemented, improving computational efficiency through the batch processing of geometric and material parameters. The extracted temperature field data was used to train a DeepONet neural network, which achieved accurate temperature predictions (with a L2 relative error of 1.5689% and an R2 score of 0.9991) while operating faster than conventional finite element analysis. This research establishes a complete workflow from fundamental heat transfer analysis to efficient data generation and machine learning prediction, providing structural engineers with practical tools for the performance-based fire safety design of timber connections. The framework’s computational efficiency enables comprehensive parametric studies and design optimizations that were previously impractical, offering significant advancements for structural fire engineering applications.
ISSN:2571-6255
DOI:10.3390/fire8070280
Källa:Agriculture Science Database