Title: Artificial Intelligence Model for Fire Resistance of FRP-Strengthened Beams

Authors: Nima Khodadadi, M.Z Naser, Francisco De Caso, Antonio Nanni

DOI: 10.33599/nasampe/c.24.0257

Abstract: Fiber-reinforced polymers (FRP) have become increasingly popular as both primary and secondary reinforcement materials in concrete structures due to their significant advantages over traditional steel reinforcement. However, the fire resistance of concrete structures reinforced with Fiber-Reinforced Polymers (FRP) remains a significant concern, exacerbated by the lack of reliable fire test data and specific testing protocols. This issue is further complicated by the substantial time and cost required to conduct these tests. Additionally, no AI-based model currently utilizes this data to predict outcomes, which could save significant time and costs in assessing fire resistance of FRP-incorporated structures. Addressing this gap, this study develops the AI model for a comprehensive database of over 21,000 numerical data points on the fire performance of FRP-strengthened reinforced concrete beams. This database includes a wide range of parameters such as geometric dimensions, FRP-strengthening levels, steel reinforcement ratios, insulation configurations, material properties, and load levels, aiming to facilitate applying machine learning (ML) techniques in predicting total load applied on beams. Specifically, the study explores the enhancement of Artificial Neural Networks (ANNs) by integrating metaheuristic algorithm for optimizing network structures. The proposed approach in this paper optimizes parameters across a feed-forward backpropagation network, employing the Mountain Gazelle Optimization algorithm in tandem with ANN. The experimental results are subsequently evaluated using R-squared, MSE, RMSE, and MAPE metrics. The refined MGO-ANN approach boasts an impressive R-squared value of 0.8175. To make the findings accessible to practitioners, a graphical user interface (GUI) was developed, enabling users to input features and obtain predictions on the total load applied on beams in a user-friendly manner. This research not only fills a significant knowledge gap regarding the fire resistance of FRP-strengthened beams but also advances the use of ML in structural engineering by providing a reliable method for performance prediction.

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Conference: CAMX 2024 | San Diego CA

Publication Date: 2024/9/9

SKU: TP24-0000000257

Pages: 15

Price: $30.00