This article examines how the time of exposure (0, 10, 20 and 30 min) to fire affects the optimal design of Howe timber trusses. The study integrates experimental characterization, thermal modeling (Eurocode 5 1995-1-2), and the bio-inspired Firefly Algorithm (FA). Five Brazilian species
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This article examines how the time of exposure (0, 10, 20 and 30 min) to fire affects the optimal design of Howe timber trusses. The study integrates experimental characterization, thermal modeling (Eurocode 5 1995-1-2), and the bio-inspired Firefly Algorithm (FA). Five Brazilian species (Cambará-rosa, Cupiúba, Angelim-pedra, Garapa, and
Jatobá) were assessed in spans of 6, 9, 12, and 15 m. Each configuration was optimized 30 times with 120 agents, 600 iterations, and penalty treatments. In ambient conditions,
Angelim-pedra and
Garapa produced the lightest trusses, while under fire, simulated trusses with
Jatobá wood properties provided the best performances, resulting in up to 35% mass reduction compared to trusses optimized with denser species under equivalent fire scenarios. Safety margins, defined through the Gross Mass Increase (GMI) index, quantify the additional structural mass required under fire in relation to the ambient design. GMI values ranged between 22% and 140% across the analyzed cases, quantifying the additional section demand under fire conditions relative to ambient design. To predict overdesign, regression equations were fitted using symbolic regression for the Index of Gross Area Correction Index (GACI), based on fire exposure time and resistant parameters, achieving R
2 above 0.85. The study provides guidelines for species selection, span sizing, and fire safety design. Overall, combining thermal analysis, bio-inspired optimization, and symbolic regression highlights the potential of timber trusses for efficient, safe, and sustainable roof structures. In addition, this study demonstrates the scientific novelty of integrating experimental characterization, Eurocode 5 thermal modeling, and metaheuristic optimization with symbolic regression, providing analytical indices such as the Gross Mass Increase (GMI) and Gross Area Correction Index (GACI). These results also offer practical guidelines for species selection, span sizing, and fire safety design, reinforcing the applicability of the methodology for engineers and designers of timber roof systems.
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