Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network
Tóm tắt
In this paper, a prediction method based on artificial neural network was developed to rapidly determine the residual shear resistance of reinforced concrete (RC) beams after fire. Firstly, the temperature distribution along the beam section was determined through finite element analysis using software ABAQUS. A residual shear strength calculation model was developed and validated using the test data. Using this model, 384 data entries were derived for training and testing. The input layer of neural network involved parameters of beam height, beam width, fire exposure time, cross-sectional area of stirrup, stirrup spacing, concrete strength, and concrete cover thickness. The output was the shear resistance of RC beams. It was found that use of BP neural network could precisely predict the post-fire shear resistance of RC beams. The predicted data were highly consistent with the target data. Thus, this is a novel method for computing post-fire shear resistance of RC beams. Using this new method, further investigation was also made on the effects of different parameters on the shear resistance of the beams.
Tài liệu tham khảo
Abbasi, A., & Hogg, P. J. (2005). Prediction of the failure time of glass fiber reinforced plastic reinforced concrete beams under fire conditions. Journal of Composites for Construction, 9(5), 450–457.
Annerel, E., & Taerwe, L. (2011). Evolution of the strains of traditional and self-compacting concrete during and after fire. Materials and Structures, 44(8), 1369–1380.
Bengar, H. A., Abdollahtabar, M., & Shayanfar, J. (2016). Predicting the ductility of RC beams using nonlinear regression and ANN. Iranian Journal of Science & Technology Transactions of Civil Engineering, 40(4), 1–14.
BSI. (2004). BS EN 1992-1-2:2004, Eurocode 2—Design of concrete structures-part 1–2: General rules-structural fire design. Structural fire design. London: BSI.
BSI. (2013). BS ENV 1994-1-2, Eurocode 4—Design of composite steel and concrete structures—part 1–2: General rules. Structural fire design. London: BSI.
Chowdhury, E. U., & Bisby, L. A. (2008). Residual behavior of fire-exposed reinforced concrete beams prestrengthened in flexure with fiber-reinforced polymer sheets. Journal of Composites for Construction, 12(1), 61–68.
El-Fitiany, S. F., & Youssef, M. A. (2017). Fire performance of reinforced concrete frames using sectional analysis. Engineering Structures, 142, 165–181.
Erdem, H. (2010). Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks. Advances in Engineering Software, 41(2), 270–276.
Erdem, H. (2015). Predicting the moment capacity of RC beams exposed to fire using ANNs. Construction and Building Materials, 101, 30–38.
Felicetti, R., Gambarova, P. G., & Meda, A. (2009). Residual behavior of steel rebars and R/C sections after a fire. Construction and Building Materials, 23(12), 3546–3555.
Fu, F. (2016). Structural analysis and design to prevent disproportionate collapse. Boca Raton: CRC Press.
GB. (2010). GB 50010-2010: Code for design of concrete structures. GB, China, Beijing.
Gupta, G. P., Nair, R. R., & Jeyanthi, R. (2017). An ANN based SpO2 measurement for clinical management systems. Energy Procedia, 117, 393–400.
Hu, C., Xu, Y., Luo, Y., Zheng, Y., & Lin, B. (2014). Experimental study on the tensile strength of concrete after high temperature. Journal of Huaqiao University (Natural Science), 35(2), 196–201.
ISO (International Organisation for Standardisation). (1999). ISO 834-1: Fire resistance tests—elements of building construction. Part 1: General requirements. Geneva: ISO.
Keskin, R. S. O., & Arslan, G. (2013). Predicting diagonal cracking strength of RC slender beams without stirrups using ANNs. Computers & Concrete, 12(5), 697–715.
Kurtgoz, Y., Karagoz, M., & Deniz, E. (2018). Biogas engine performance estimation using ANN. Engineering Science and Technology, an International Journal, 20(6), 1563–1570.
Leśniak, A., & Juszczyk, M. (2018). Prediction of site overhead costs with the use of artificial neural network based model. Archives of Civil & Mechanical Engineering, 18(3), 973–982.
Liu, Y., Li, H., Wang, X., & Wang, Q. (2000). TFIEILD-a software package for temperature field analysis of reinforced concrete members exposed to fire. Journal of Shenyang Architectural and Civil Engineering Institute, 16(4), 251–253.
Lou, Y., Wu, W., & Zhuang, J. (2017). The prediction to the flexural capacity of self-compacting concrete beam base on BP neural network. Journal of Changchun Institute of Technology (Natural Science), 18(1), 34–36.
Mar, B., Md, R., & Fumo, N. (2016). Prediction of residential building energy consumption: A neural network approach, Energy. Amsterdam: Elsevier.
Marugán, A. P., Márquez, F. P. G., Perez, J. M. P., & Ruiz-Hernández, D. (2018). A survey of artificial neural network in wind energy systems. Applied Energy, 228, 1822–1836.
Mashrei, M. A., Abdulrazzaq, N., Abdalla, T. Y., & Rahman, M. S. (2010). Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members. Engineering Structures, 32(6), 1723–1734.
Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, 21, 89–93.
Molkens, T., Coile, R. V., & Gernay, T. (2017). Assessment of damage and residual load bearing capacity of a concrete slab after fire: applied reliability-based methodology. Engineering Structures, 150, 969–985.
Mossalam, A., & Arafa, M. (2017). Using artificial neural networks (ANN) in projects monitoring dashboards’ formulation. HBRC Journal, 14, 385–392.
Naser, M., Abulebdeh, G., & Hawileh, R. (2012). Analysis of RC T-beams strengthened with CFRP plates under fire loading using ANN. Construction and Building Materials, 37(12), 301–309.
Ožbolt, J., Bošnjak, J., Periškić, G., & Sharma, A. (2014). 3D numerical analysis of reinforced concrete beams exposed to elevated temperature. Engineering Structures, 58, 166–174.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning Internal Representations by Error Propagation. In D. E. Rumenhart & J. L. McCelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (pp. 318–362). Cambridge: MIT Press.
Sarabakha, A., Imanberdiyev, N., Kayacan, E., Khanesar, M., & Hagras, H. (2017). Novel levenberg–marquardt based learning algorithm for unmanned aerial vehicles. Information Sciences. https://doi.org/10.1016/j.ins.2017.07.020.
Sarro, L. M., Fernández, C. S., Giménez, A., & Marín, D. L. (2003). Automated classification of light curves using ANNs, highlights of Spanish astrophysics III (pp. 511–540). Netherlands: Springer.
Staub, S., Karaman, E., Kaya, S., Karapınar, H., & Güven, E. (2015). Artificial neural network and agility. Procedia-Social and Behavioral Sciences, 195, 1477–1485.
Viotti, P., Liuti, G., & Genova, P. D. (2002). Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of perugia. Ecological Modelling, 148(1), 27–46.
Xu, Y., Peng, X., Dong, Y., Luo, Y., & Lin, B. (2015). Experimental study on shear behavior of reinforced concrete beams strengthened with CFRP sheet after fire. Journal of Building Structures, 36(2), 123–132.
Xu, Y., Wu, B., Wang, R., Jiang, M., & Luo, Y. (2013). Experimental study on residual performance of reinforced concrete beams after fire. Journal of Building Structures, 34(8), 20–29.
Yang, Z. (2008). Numerical simulation of temperature field of reinforced concrete members under fire. (Master dissertation. Central South University).
Yu, Z., Zi, W., Kuang, Y., & Zhang, L. (2012). Influences of temperature and time on concrete cubic compressive strength. Fire Science and Technology, 2, 111–114.
Zhou, M., & Ke, G. (2016). Experiment and its prediction artificial neural networks model study on the compressive strength of waste glass concrete. Concrete, 4, 54–56.
Zhu, D. (2011). Analysis of thermo-elastic-plastic on reinforced concrete structures subjected to fire. (Doctoral dissertation, Harbin Engineering University).