Title:
Deep Neural Network to Predict Fire Resistance of FRP-Strengthened Beams
Author(s):
Bhatt, P.P. and Sharma, N.
Publication:
Symposium Paper
Volume:
350
Issue:
Appears on pages(s):
69-80
Keywords:
artificial neural network (ANN); carbon fiber-reinforced polymer strengthening; deep neural network (DNN); fire resistance; machine learning (ML)
DOI:
10.14359/51734313
Date:
11/1/2021
Abstract:
This paper presents the development of a data-driven deep neural network (DNN) for evaluating the fire resistance time of fiber-reinforced polymer (FRP) strengthened concrete beams. The model was trained for a scaled and unscaled dataset. For this, a comprehensive dataset of FRP-strengthened concrete beams with different geometry, insulation configuration, applied loading, and material characteristics was compiled. The DNN structure was selected after an extensive hyperparameter tuning in conjunction with ten-fold cross-validation scheme. The effect of different input parameters on the fire resistance prediction was analyzed. The DNN model developed using scaled data provides a reasonably accurate estimate, of the fire resistance of FRP-strengthened concrete beams with an R2 value of almost 92%. The developed model is further utilized to evaluate the impact of different parameters on fire resistance prediction for FRP-strengthened concrete beams. Results from the analysis indicate the thermal properties of insulation play an important role in determining the fire resistance of FRP-strengthened concrete beams.
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