Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach
Abstract
1. Introduction
2. Heat Transfer Analysis
2.1. Model Detail
2.2. Validation
3. Automated Abaqus Model Modification and Temperature Field Data Processing System
3.1. Automated Model Modification and ODB File Generation
3.2. Temperature Field Data Extraction and Processing
3.3. Data Cleaning and Normalization
3.4. Data Consolidation and Excel Report Generation
4. Temperature Field Prediction Using DeepONet
4.1. Model Architecture
4.2. Data Preparation and Model Training
4.3. Performance Evaluation and Validation
5. Conclusions
- (1)
- The heat transfer analysis component successfully developed a robust finite element model that accurately simulates thermal behavior in timber–steel connections. By incorporating the anisotropic thermal properties of wood and temperature-dependent material characteristics, validation against experimental data confirmed the model’s ability to predict temperature progression with high fidelity. This analysis provides fundamental insights into the heat transfer mechanisms that govern connection performance during fire exposure;
- (2)
- The automated Abaqus model modification and data processing system represents a significant advancement in parametric thermal analysis methodology. By implementing automated model generation and result extraction algorithms, the system achieves a reduction in processing time compared to conventional manual approaches while maintaining rigorous quality control standards. The system’s efficiency enables the generation of comprehensive datasets that would be impractical to obtain through traditional methods, thereby facilitating a more thorough investigation of parameter influences on thermal performance;
- (3)
- The DeepONet architecture demonstrates remarkable capabilities in temperature field prediction, achieving prediction accuracy with L2 relative error of 1.5689% and an R2 score of 0.9991 while operating faster than conventional finite element analysis. The network’s specialized architecture successfully captures complex thermal phenomena, including through-thickness temperature gradients and localized heating effects. This computational efficiency makes the framework particularly valuable for design optimization and probabilistic analysis applications where numerous evaluations are required. The model’s performance represents a significant advancement in surrogate modeling techniques for structural fire engineering applications. This study bridges the gap between high-fidelity simulations and rapid-fire performance evaluation, providing a scalable surrogate modeling framework for advancing the fire safety design of timber structures. Future work will extend the framework to include different fire scenarios and composite timber systems to further generalize the model.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Luo, J.; Tian, G.; Xu, C.; Zhang, S.; Liu, Z. Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach. Fire 2025, 8, 280. https://doi.org/10.3390/fire8070280
Luo J, Tian G, Xu C, Zhang S, Liu Z. Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach. Fire. 2025; 8(7):280. https://doi.org/10.3390/fire8070280
Chicago/Turabian StyleLuo, Jing, Guangxin Tian, Chen Xu, Shijie Zhang, and Zhen Liu. 2025. "Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach" Fire 8, no. 7: 280. https://doi.org/10.3390/fire8070280
APA StyleLuo, J., Tian, G., Xu, C., Zhang, S., & Liu, Z. (2025). Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach. Fire, 8(7), 280. https://doi.org/10.3390/fire8070280