Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Fire

DOI: 10.3390/fire8070280

Authors: Jing Luo, Guangxin Tian, Chen Xu, Shijie Zhang, Zhen Liu

Institutions: Shanghai Normal University, Ruhr University Bochum, Tongji University

Language: English

Topic: Fire effects on concrete materials

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896