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Title |
FDS Data-Driven Fire Time-Temperature Curve Evaluation for Individual Compartments in Office Buildings using ANN and Regression Analysis
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Authors |
에르덴바타르 아미나(Amina Erdenebaatar) ; 다르한바트 할리오나(Khaliunaa Darkhanbat) ; 허인욱(Inwook Heo) ; 최승호(Seung-Ho Choi) ; 김강수(Kang Su Kim) |
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DOI |
https://doi.org/10.11112/jksmi.2025.29.6.246 |
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Keywords |
업무시설; 화재 시간-온도; 인공신경망; 회귀분석; 성능기반 내화 설계 Office building; Fire Time-Temperature; Artificial Neural Network; Regression analysis; Performance-based Fire Design |
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Abstract |
This study conducted fire simulations for typical office buildings by considering heat release rate, compartment floor area, and fuel type as primary variables, and established a database of ceiling-level time-temperature histories. Based on the constructed dataset, an Artificial Neural Network (ANN) model and a regression-based simplified time-temperature prediction equation were developed. To examine the applicability of the proposed models, an additional fire simulation was performed for a new office building, and the predicted results were compared with the simulation outputs. The mean absolute percentage error (MAPE) of both models was found to be below approximately 23%, indicating a reasonable level of predictive performance. The developed models are expected to serve as practical tools for fire resistance evaluation and as foundational data for performance-based fire design in office buildings.
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