Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach
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| הוצא לאור ב: | Fire vol. 8, no. 7 (2025), p. 280-295 |
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| מחבר ראשי: | |
| מחברים אחרים: | , , , |
| יצא לאור: |
MDPI AG
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| נושאים: | |
| גישה מקוונת: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3233189394 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2571-6255 | ||
| 024 | 7 | |a 10.3390/fire8070280 |2 doi | |
| 035 | |a 3233189394 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Luo, Jing |u College of Civil Engineering, Shanghai Normal University, Shanghai 201400, China; luojing94@shnu.edu.cn (J.L.); 1000567234@smail.shnu.edu.cn (G.T.); 1000531341@smail.shnu.edu.cn (S.Z.) | |
| 245 | 1 | |a Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Temperature distribution | ||
| 653 | |a Finite element method | ||
| 653 | |a Fire hazards | ||
| 653 | |a Wood | ||
| 653 | |a Accuracy | ||
| 653 | |a Beam-columns | ||
| 653 | |a Data processing | ||
| 653 | |a Timber | ||
| 653 | |a Structural engineers | ||
| 653 | |a Thermodynamic properties | ||
| 653 | |a Safety engineering | ||
| 653 | |a Thermal properties | ||
| 653 | |a Computer applications | ||
| 653 | |a Automation | ||
| 653 | |a Machine learning | ||
| 653 | |a Fire prevention | ||
| 653 | |a Heat conductivity | ||
| 653 | |a Deep learning | ||
| 653 | |a Radiation | ||
| 653 | |a Batch processing | ||
| 653 | |a Heat transfer | ||
| 653 | |a Simulation | ||
| 653 | |a Construction | ||
| 653 | |a Temperature dependence | ||
| 653 | |a Neural networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Temperature effects | ||
| 653 | |a Predictions | ||
| 653 | |a Computational efficiency | ||
| 653 | |a Glulam | ||
| 653 | |a Mathematical models | ||
| 653 | |a Engineering | ||
| 653 | |a Parametric analysis | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Tian Guangxin |u College of Civil Engineering, Shanghai Normal University, Shanghai 201400, China; luojing94@shnu.edu.cn (J.L.); 1000567234@smail.shnu.edu.cn (G.T.); 1000531341@smail.shnu.edu.cn (S.Z.) | |
| 700 | 1 | |a Chen, Xu |u Institute for Structural Mechanics, Ruhr University Bochum, 44801 Bochum, Germany; chen.xu@rub.de | |
| 700 | 1 | |a Zhang, Shijie |u College of Civil Engineering, Shanghai Normal University, Shanghai 201400, China; luojing94@shnu.edu.cn (J.L.); 1000567234@smail.shnu.edu.cn (G.T.); 1000531341@smail.shnu.edu.cn (S.Z.) | |
| 700 | 1 | |a Liu, Zhen |u Institute for Structural Mechanics, Ruhr University Bochum, 44801 Bochum, Germany; chen.xu@rub.de | |
| 773 | 0 | |t Fire |g vol. 8, no. 7 (2025), p. 280-295 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233189394/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233189394/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233189394/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |