Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks. (English) Zbl 1180.80060
Summary: The application of artificial neural networks (ANN) to predict the ultimate moment capacity of reinforced concrete (RC) slabs in fire is investigated. An ANN model is built, trained and tested using 294 data for slabs exposed to fire. The data used in the ANN model consists of seven input parameters, which are the distance from the extreme fiber in tension to the centroid of the steel on the tension side of the slab (\(d^{\prime}\)), the effective depth \((d)\), the ratio of previous parameters (\(d^{\prime}/d\)), the area of reinforcement on the tension face of the slab \((A_{s})\), the fire exposure time \((t)\), the compressive strength of the concrete \((f_{cd})\), and the yield strength of the reinforcement \((f_{yd})\). It is shown that ANN model predicts the ultimate moment capacity \((M_{u})\) of RC slabs in fire with high degree of accuracy within the range of input parameters considered. The moment capacities predicted by ANN are in line with the results provided by the ultimate moment capacity equation. These results are important as ANN model alleviates the problem of computational complexity in determining \(M_{u}\).
References:
| [1] | Yeh, I. C.: Modeling slump flow of concrete using second-order regressions and artificial neural networks, Cem concr compos 29, 474-480 (2007) |
| [2] | Karahan, O.; Tanyıldızı, H.; Atış, C. D.: An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash, J zhejiang univ sci A 9, No. 11, 1514-1523 (2008) · Zbl 1422.74007 |
| [3] | Hola, J.; Schabowicz, K.: Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests, J civ eng manage 1, 23-32 (2005) |
| [4] | &idot; Topçu, B; Sarıdemir, M.: Prediction of properties of waste AAC aggregate concrete using artificial neural network, Comput mater sci 41, 117-125 (2007) |
| [5] | &idot; Topçu, B; Karakurt, C.; Sarıdemir, M.: Predicting the strength development of cements produced with different pozzolans by neural network and fuzzy logic, Mater des 29, 1986-1991 (2008) |
| [6] | Akkurt, S.; Özdemir, S.; Tayfur, G.; Akyol, B.: The use of GA-anns in the modelling of compressive strength of cement mortar, Cem concr res 33, 973-979 (2003) |
| [7] | Ukrainczyk, N.; Pecur, I. B.; Bolf, N.: Evaluating rebar corrosion damage in RC structures exposed to marine environment using neural network, Civ eng environ syst 24, No. 1, 15-32 (2007) |
| [8] | Flood, I.; Muszynski, L.; Nandy, S.: Rapid analysis of externally reinforced concrete beams using neural networks, Comput struct 79, 1553-1559 (2001) |
| [9] | Pannirselvam, N.; Raghunath, P. N.; Suguna, K.: Neural network for performance of Glass fibre reinforced polymer plated RC beams, Am J eng appl sci 1, 83-88 (2008) |
| [10] | Bisby, L. A.; Kodur, V. K. R.: Evaluating the fire endurance of concrete slabs reinforced with FRP bars: considerations for a holistic approach, Compos part B: eng 38, 547-558 (2007) |
| [11] | Rao, H. S.; Babu, B. R.: Hybrid neural network model for the design of beam subjected to bending and shear, Sadhana 32, No. 5, 577-586 (2007) |
| [12] | Mansour, M. Y.; Dicleli, M.; Lee, J. Y.; Zhang, J.: Predicting the shear strength of reinforced concrete beams using artificial neural networks, Eng struct 26, 781-799 (2004) |
| [13] | Ahn, N.; Jang, H.; Park, D. K.: Presumption of shear strength of steel fiber reinforced concrete beam using artificial neural network model, J appl polym sci 103, 2351-2358 (2007) |
| [14] | Cladera, A.; Mari, A. R.: Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part I: Beams without stirrups, Eng struct 26, 917-926 (2004) |
| [15] | Keleşo&gbreve, Ö; Lu: The analyses of the reinforced concrete beam sections by artificial neural networks, Tech J turk chamber civ eng, 3935-3942 (2006) |
| [16] | Inel, M.: Modeling ultimate deformation capacity of RC columns using artificial neural networks, Eng struct 29, 329-335 (2007) |
| [17] | Ahmadkhanlou, F.; Adeli, H.: Optimum cost design of reinforced concrete slabs using neural dynamics model, Eng appl artif intell 18, 65-72 (2005) |
| [18] | Rafıq, M. Y.; Bugmann, G.; Easterbrook, D. J.: Neural network design for engineering applications, Comput struct 79, 1541-1552 (2001) |
| [19] | Al-Khaleefi, A. M.; Terro, M. J.; Alex, P. A.; Wang, Y.: Prediction of fire resistance of concrete filled tubular steel columns using neural networks, Fire safety J 37, 339-352 (2002) |
| [20] | Iso-834: Fire resistance tests-elements of building construction. Part 1 – 9, (1975) |
| [21] | Eurocode 2: Design of concrete structures. ENV 1992. Part 1 – 2: General rules-structural fire design. European Committee for Standardization, Brussels; 1995. |
| [22] | ç, Y. H.; Engel: Heat transfer: a practical approach, (1998) |
| [23] | Erdem H. Effect to reinforced concrete slab moment capacity of high temperature. In: The 30th anniversary of Çukurova University, engineering and architecture faculty symposium, Adana; 2008 [in Turkish]. |
| [24] | Kahraman, S.; Altun, H.; Tezekeci, B. S.; Fener, M.: Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks, Int J rock mech MIN 43, 157-164 (2006) |
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