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Concrete Spalling Identification and Fire Resistance Prediction for Fired RC Columns Using Machine Learning-Based Approaches

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Abstract

This study aims at utilizing machine learning (ML) in predicting the fire resistance and spalling degree of reinforced concrete (RC) columns with improved accuracy and reliability. A database with 119 test specimens was created for the development of ML-based regression models, and a database with 101 test specimens was created for the development of ML-based classification models. Six ML algorithms—support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and light gradient boosting machine (LightGBM). The hyperparameters of the ML-based models were optimized through Bayes optimization search (BayesSearchCV) with ten-fold cross-validation. The results indicated that the AdaBoost not only accurately predicted the spalling degree of RC columns with an accuracy of 87%, but also performed best in predicting the fire resistance of RC columns with R2 = 0.96 and RMSE = 16.58. The AdaBoost model achieved high accuracy without significant bias, surpassing existing design equations. SHAP method was utilized to produce global explanations for the predictions. The results revealed that concrete compressive strength, loading ratio, slenderness ratio, and column width were the most critical features for spalling degree identification. Meanwhile, those were slenderness ratio, concrete cover, loading ratio, part of the fired column, and longitudinal reinforcement for fire resistance prediction. The parametric study demonstrated that the fire resistance of RC columns is positively affected by only concrete cover.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam

    Thuan N.-T. Ho & Trong-Phuoc Nguyen

  2. Faculty of Civil Engineering and Technology, Dong A University, 33 Xo Viet Nghe Tinh, Hai Chau, Da Nang, 550000, Vietnam

    Gia Toai Truong

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  1. Thuan N.-T. Ho
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  3. Gia Toai Truong
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Correspondence to Gia Toai Truong.

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Appendices

Appendix A

See Table A1.

Table A1 Complete Database of Fired RC Columns
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Appendix B: Graphical User Interface (GUI) for ML-Based Models

Creating a user-friendly interface can promote the utilization of ML models in engineering practice by making it easier for engineers to access and use them. In this study, a standalone application program was developed for the AdaBoost-based model using Tkinter to estimate the fire resistance of RC columns. The graphical user interface (GUI) of the application is presented in Figure B1, allowing users to easily obtain the fire resistance as well as to identify spalling degree of RC columns by inputting some certain variables and clicking the Predict button. However, it is important to note that the application program is only applicable for the ranges of variables shown in Table 1, as the hyperparameters of the AdaBoost-based model were defined within these ranges. See Figure B1.

Figure  B1
figure 20

Graphical user interface (GUI) for AdaBoost-based model

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Ho, T.NT., Nguyen, TP. & Truong, G.T. Concrete Spalling Identification and Fire Resistance Prediction for Fired RC Columns Using Machine Learning-Based Approaches. Fire Technol 60, 1823–1866 (2024). https://doi.org/10.1007/s10694-024-01550-8

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