Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Abstract

This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

Graphical abstract

Keywords

deep learning / fire resistance / generative adversarial networks / machine learning

References

[1] Abedi M, Naser M Z. RAI: Rapid, Autonomous and Intelligent machine learning approach to identify fire-vulnerable bridges. Applied Soft Computing, 2021, 113: 107896

[2] Khalilpourazari S, Khalilpourazary S, Özyüksel Çiftçioğlu A, Weber G W. Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence. Journal of Intelligent Manufacturing, 2021, 32(6): 1621–1647

[3] Khalilpourazari S, Hashemi Doulabi H. A flexible robust model for blood supply chain network design problem. Annals of Operations Research, 2023, 328(1): 701–726

[4] ÖzyükselÇiftçioğlu ANaserM Z. Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns? Structures, 2022, 40: 920–935

[5] Chakraborty S, Adhikari S. Machine learning based digital twin for dynamical systems with multiple time-scales. Computers & Structures, 2021, 243: 106410

[6] Liang Y, Izzuddin B A. Locking-free 6-noded triangular shell elements based on hierarchic optimisation. Finite Elements in Analysis and Design, 2022, 204: 103741

[7] Bahaghighat M, Abedini F, Xin Q, Zanjireh M M, Mirjalili S. Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely. Energy Reports, 2021, 7: 8561–8576

[8] MoslemiSMirzazadehAWeberG-WSobhanallahiM A. Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain. Opsearch. 2021: 1–42

[9] Kaveh A, Biabani Hamedani K, Kamalinejad M. Improved slime mould algorithm with elitist strategy and its application to structural optimization with natural frequency constraints. Computers & Structures, 2022, 264: 106760

[10] Kaveh A, Zaerreza A. Reliability-based design optimization of the frame structures using the force method and SORA-DM framework. Structures, 2022, 45: 814–827

[11] Lin S, Zheng H, Han B, Li Y, Han C, Li W. Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotechnica, 2022, 17(4): 1477–1502

[12] NaserM ZAlaviA H. Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences. Architecture, Structures and Construction, 2021: 1–19

[13] Abueidda D W, Koric S, Sobh N A. Topology optimization of 2D structures with nonlinearities using deep learning. Computers & Structures, 2020, 237: 106283

[14] Leite J P B, Topping B H V. Improved genetic operators for structural engineering optimization. Advances in Engineering Software, 1998, 29(7-9): 529–562

[15] Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

[16] Tapeh A, Naser M Z. Artificial intelligence, machine learning, and deep learning in structural engineering: A scientometrics review of trends and best practices. Archives of Computational Methods in Engineering, 2023, 30(1): 115–159

[17] Guo H, Zhuang X, Fu X, Zhu Y, Rabczuk T. Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials. Computational Mechanics, 2023, 72(3): 513–524

[18] Guo H, Zhuang X, Chen P, Alajlan N, Rabczuk T. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 38(6): 5173–5198

[19] Zhuang X, Guo H, Alajlan N, Zhu H, Rabczuk T. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics. A, Solids, 2021, 87: 104225

[20] GuoHZhuangXRabczukT. A deep collocation method for the bending analysis of Kirchhoff plate. 2021, arXiv: 2102.02617

[21] Varone G, Ieracitano C, Çiftçioğlu A Ö, Hussain T, Gogate M, Dashtipour K, Al-Tamimi B N, Almoamari H, Akkurt I, Hussain A. A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting. Entropy, 2023, 25(2): 1–19

[22] de Rosa G H, Papa J P. A survey on text generation using generative adversarial networks. Pattern Recognition, 2021, 119: 108098

[23] Elakkiya R, Vijayakumar P, Kumar N. An optimized generative adversarial network based continuous sign language classification. Expert Systems with Applications, 2021, 182: 115276

[24] XuLSkoularidouMCuesta-InfanteAVeeramachaneniK. Modeling Tabular data using Conditional GAN. Advances in Neural Information Processing Systems, 2019, 32

[25] WangHWeiW. Machine learning for synthetic data generation: A review. 2023, arXiv: 2302.04062

[26] Shahriar S. GAN computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network. Displays, 2022, 73: 102237

[27] Zhang R, Chen Z, Chen S, Zheng J, Büyüköztürk O, Sun H. Deep long short-term memory networks for nonlinear structural seismic response prediction. Computers & Structures, 2019, 220: 55–68

[28] Khalilpourazari S, Hashemi Doulabi H. Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. Annals of Operations Research, 2022, 312(2): 1261–1305

[29] Ma Q, Sun C, Cui B, Jin X. A novel model for anomaly detection in network traffic based on kernel support vector machine. Computers & Security, 2021, 104: 102215

[30] Naser M Z, Kodur V, Thai H T, Hawileh R, Abdalla J, Degtyarev V V. StructuresNet and FireNet: Benchmarking databases and machine learning algorithms in structural and fire engineering domains. Journal of Building Engineering, 2021, 44: 102977

[31] Thai H T. Machine learning for structural engineering: A state-of-the-art review. Structures, 2022, 38: 448–491

[32] Banerji S, Kodur V. Numerical model for tracing the response of Ultra-High performance concrete beams exposed to fire. Fire and Materials, 2022, 47(3): 322–340

[33] McNamee R, Sjöström J, Boström L. Reduction of fire spalling of concrete with small doses of polypropylene fibres. Fire and Materials, 2021, 45(7): 943–951

[34] Mohaine S, Boström L, Lion M, McNamee R, Robert F. Cross-comparison of screening tests for fire spalling of concrete. Fire and Materials, 2021, 45(7): 929–942

[35] Van Coile R, Hopkin D, Elhami-Khorasani N, Gernay T. Demonstrating adequate safety for a concrete column exposed to fire, using probabilistic methods. Fire and Materials, 2021, 45(7): 918–928

[36] GoodfellowI. NIPS 2016 Tutorial: Generative Adversarial Networks. 2017, arXiv: 1701.00160

[37] HukkelåsHMesterRLindsethF. DeepPrivacy: A generative adversarial network for face anonymization. In: 14th International Symposium on Visual Computing. Cham: Springer International Publishing, 2019: 565–578

[38] Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial networks. Communications of the ACM, 2020, 63(11): 139–144

[39] Kullback S, Leibler R A. On Information and sufficiency. Annals of Mathematical Statistics, 1951, 22(1): 79–86

[40] ChenTGuestrinC. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY: Association for Computing Machinery, 2016: 785–794

[41] Feng D C, Wang W J, Mangalathu S, Hu G, Wu T. Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements. Engineering Structures, 2021, 235: 111979

[42] Nguyen H, Vu T, Vo T P, Thai H T. Efficient machine learning models for prediction of concrete strengths. Construction & Building Materials, 2021, 266: 120950

[43] Nguyen-Sy T, Wakim J, To Q D, Vu M N, Nguyen T D, Nguyen T T. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Construction & Building Materials, 2020, 260: 119757

[44] Wang Y, Sun S, Chen X, Zeng X, Kong Y, Chen J, Guo Y, Wang T. Short-term load forecasting of industrial customers based on SVMD and XGBoost. International Journal of Electrical Power & Energy Systems, 2021, 129: 106830

[45] Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273–297

[46] Smola A J, Scholkopf B. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3): 199–222

[47] Quinlan J R. Induction of decision trees. Machine Learning, 1986, 1(1): 81–106

[48] Shorabeh S N, Samany N N, Minaei F, Firozjaei H K, Homaee M, Boloorani A D. A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran. Renewable Energy, 2022, 187: 56–67

[49] Breiman L. Random Forests. Machine Learning, 2001, 45(1): 5–32

[50] Lin W, Wu Z, Lin L, Wen A, Li J. An Ensemble Random Forest Algorithm for insurance Big Data analysis. IEEE Access: Practical Innovations, Open Solutions, 2017, 5: 16568–16575

[51] Harrison J W, Lucius M A, Farrell J L, Eichler L W, Relyea R A. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment, 2021, 763: 143005

[52] Júnior A M G, Silva V V R, Baccarini L M R, Mendes L F S. The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors. Applied Soft Computing, 2018, 63: 50–58

[53] Bradley R A, Srivastava S S. Correlation in polynomial regression. American Statistician, 1979, 33(1): 11–14

[54] Ostertagová E. Modelling using polynomial regression. Procedia Engineering, 2012, 48: 500–506

[55] Bayes T. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 1763, 53: 370–418

[56] Khajenezhad A, Bashiri M A, Beigy H. A distributed density estimation algorithm and its application to naive Bayes classification. Applied Soft Computing, 2021, 98: 106837

[57] Farid D M, Zhang L, Rahman C M, Hossain M A, Strachan R. Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Systems with Applications, 2014, 41(4): 1937–1946

[58] Fix E, Hodges J L. Discriminatory analysis. Nonparametric discrimination: Consistency properties. International Statistical Review/Revue Internationale de Statistique, 1989, 57(3): 238–247

[59] Pandya D H, Upadhyay S H, Harsha S P. Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Systems with Applications, 2013, 40(10): 4137–4145

[60] KeGMengQFinleyTWangTChenWMaWQYeTYLiu. LightGBM: A highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 3149–3157

[61] DorogushA VErshovVGulinA. CatBoost: Gradient boosting with categorical features support. 2018, arXiv:1810.11363

[62] Naser M Z. Heuristic machine cognition to predict fire-induced spalling and fire resistance of concrete structures. Automation in Construction, 2019, 106: 102916

[63] Hertz K D D. Limits of spalling of fire-exposed concrete. Fire Safety Journal, 2003, 38(2): 103–116

[64] Shah A H, Sharma U K. Fire resistance and spalling performance of confined concrete columns. Construction & Building Materials, 2017, 156: 161–174

[65] KodurVChengFWangTLatourJLerouxP. Fire Resistance of High-Performance Concrete Columns. Ottawa: National Research Council Canada, 2001

[66] KlingschE W H. Explosive spalling of concrete in fire. Dissertation for the Doctoral Degree. Gifhorn: ETH Zurich, 2014

[67] KodurVMcGrathRLerouxPLatourJ. Experimental studies for evaluating the fire endurance of high-strength concrete columns. National Research Council Canada, Internal Report, 2005, 197

[68] Liu J C C, Tan K H, Yao Y. A new perspective on nature of fire-induced spalling in concrete. Construction & Building Materials, 2018, 184: 581–590

[69] PhanL TCarinoN J. Fire Performance of High Strength Concrete: Research Needs. Advanced Technology in Structural Engineering. Reston, VA: American Society of Civil Engineers, 2000, 1–8

[70] RautNKodurV. Response of reinforced concrete columns under fire-induced biaxial bending. ACI Structural Journal, 2011, 108(5).

[71] Harmathy T Z. Effect of mositure on the fire endurance of building elements. ASTM Special Technical Publication, 1965, 385: 74–95

[72] BažantZ PKaplanM FBazantZ P. Concrete at High Temperatures: Material Properties and Mathematical Models. London: Addison-Wesley, 1996

[73] Ulm F J, Coussy O, Bažant Z P. The “Chunnel” fire. I: Chemoplastic softening in rapidly heated concrete. Journal of Engineering Mechanics, 1999, 125(3): 272–282

[74] Song T Y, Han L H, Tao Z. Structural behavior of SRC beam-to-column joints subjected to simulated fire including cooling phase. Journal of Structural Engineering, 2015, 141(9): 04014234

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Competing interests

The authors declare that they have no competing interests.

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