Abstract
To estimate the cost of a building prior to the detail design phase, engineers and project managers need suitable tools and guidelines. Steel is an important construction material that is used in high volumes in buildings and has a significant role in the total cost of projects. In this paper, the application of the artificial neural network (ANN) method to predict the quantity of steel used in the steel moment-resisting frame (MRF) structures is presented. First, more than 1100 steel MRF structures were designed applying the changes in the influenced parameters, then these models were transferred to the ANN, and finally, the results of the performed parametric study were analyzed. The obtained results demonstrate that by using the proposed ANN method, the weights of the structures can be estimated with an acceptable accuracy prior to the starting of the design process. Based on the performed parametric study, several sets of required inputs in terms of the parameters of the story height, the span length, the number of stories, the seismicity rate of the construction site, ductility, the class of soil site and column cross section type influenced on the weight per unit area of the structure are submitted.
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References
Adeli, H., & Park, H. S. (1995). A neural dynamic model for structural optimization theory. Computers & Structures, 57(3), 383–390.
Ahmadi, M., Naderpour, H., & Kheyroddin, A. (2017). ANN model for predicting the compressive strength of circular steel-confined concrete. International Journal of Civil Engineering, 15(2), 213–221.
AISC360-05. (2010). Specification for structural steel buildings. Washington: An American National Standard, Fifth Printing.
Al-Jabri, K. S., & Al-Alawi, S. M. (2010). An advanced ANN model for predicting the rotational behaviour of semi-rigid composite joints in fire using the back-propagation paradigm. International Journal of Steel Structures, 10(4), 337–347. https://doi.org/10.1007/BF03215842.
Arslan, M. H. (2010). An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks. Engineering Structures, 32(7), 1888–1898.
Arslan, A., & Ince, R. (1996a). Neural network-based design of edge-supported reinforced concrete slabs. Structural Engineering Review, 8(4), 329–335.
Arslan, A., & Ince, R. (1996b). The neural network approximation to the size effect in fracture of cementitious materials. Engineering Fracture Mechanics, 54(2), 249–261.
ASCE/SEI-7-10. (2010). Minimum design loads for buildings and other structures. Reston, VA: American society of civil engineers, Structural Engineering Institute.
Cabalar, A. F., & Cevik, A. (2009). A modelling damping ratio and shear modulus of sand–mica mixtures using neural networks. Engineering Geology, 104(1), 31–40.
Chojaczyk, A. A., Teixeira, A. P., Neves, L. C., Cardoso, J. B., & Guedes Soares, C. (2015). Review and application of Artificial Neural Networks models in reliability analysis of steel structures. Structural Safety, 52(Part A), 78–89.
Computers and Structures Inc. (2010). SAP2000 advanced 14.2.0. Berkeley, CA.
Dahou, Z., Sbartai, Z. M., Castelc, A., & Ghomari, F. (2009). Artificial neural network model for steel–concrete bond prediction. Engineering Structures, 31(8), 1724–1733.
Hakim, S. J. S., Abdul Razak, H., & Ravanfar, S. A. (2015). Fault diagnosis on beam-like structures from modal parameters using artificial neural networks. Measurement, 76, 45–61.
Hamburger, R. O., & Malley, J. O. (2016). Seismic design of steel special moment frames: A guide for practicing engineers, NEHRP seismic design technical brief no. 2, Second Edition, National Earthquake Hazards Reduction Program (NEHRP) Technical Briefs, National Institute of Standards and Technology (NIST), U.S. Department of Commerce, NIST GCR 16-917-41.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79(8), 2554–2558.
IBC. (2015). International building code. New Jersey Edition.
Iranian National Building Code, Part 6. (2013). Loading on structures. Tehran: Ministry of Road and Urban Development. (In Persian).
Jiang, G., Keller, J., Bond, Ph L, & Yuan, Zh. (2016). Predicting concrete corrosion of sewers using artificial neural network. Water Research, 92, 52–60.
Jørgensen, C., Grastveit, R., Roca, J. G., Payá-Zaforteza, I., & Adam, J. M. (2013). Bearing capacity of steel-caged RC columns under combined bending and axial loads: Estimation based on Artificial Neural Networks. Engineering Structures, 56, 1262–1270.
Karimi, S., Bonakdari, H., Karami, H., et al. (2017). Effects of width ratios and deviation angles on the mean velocity in inlet channels using numerical modeling and artificial neural network modeling. International Journal of Civil Engineering, 15(2), 149–161.
Khajeh, M., Moghaddam, M Gh, & Shakeri, M. (2012). Application of artificial neural network in predicting the extraction yield of essential oils of Diplotaenia cachrydifolia by supercritical fluid extraction. Supercritical Fluids, 69, 91–96.
Kohonen, T. (1989). Self-organization and associative memory (Vol. 8)., Springer Series in Information Sciences Berlin: Springer.
Lee, S., & Lee, C. (2014). Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks. Engineering Structures, 61, 99–112.
Marquardt, C., & Zenner, H. (2015). Lifetime calculation under variable amplitude loading with the application of artificial neural networks. International Journal of Fatigue, 27(8), 920–927.
MathWorks, MATLAB. (2014). The language of technical computing. Version 7.11.0. (R2014a).
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.
Nili, M., Azarioon, A., Danesh, A., et al. (2016). Experimental study and modeling of fiber volume effects on frost resistance of fiber reinforced concrete. International Journal of Civil Engineering. https://doi.org/10.1007/s40999-016-0122-2.
Oh, J. W., Lee, I. W., Kim, J. T., & Lee, G. W. (1999). Application of neural networks for proportioning of concrete mixes. ACI Material Journal, 96(1), 61–67.
Pu, Y., & Mesbahi, E. (2006). Application of artificial neural networks to the evaluation of the ultimate strength of steel panels. Engineering Structures, 28(8), 1190–1196.
Road, Housing and Urban Development Research Center. (2013). Iranian code of practice for seismic-resistant design of buildings. Iranian Building Code, Standard No. 2800, 4th Edn (In Persian).
Rosenblatt, F. (1962). Principles of Neurodynamics. Perceptrons and the theory of brain mechanisms. Washington DC: Spartan Books.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1, pp. 318–362). Cambridge: MIT Press.
Shouling, H., & Jiang, L. (2009). Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks. Computers & Structures, 9(3), 954–961.
Tohidi, S., & Sharifi, Y. (2015). Neural networks for inelastic distortional buckling capacity assessment of steel I-beams. Thin-Walled Structures, 94, 359–371.
Vanluchene, R. D., & Roufai, S. (1990). Neural networks in structural engineering. Computer-Aided Civil and Infrastructure Engineering, 5(3), 207–215.
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Hashemi, S.S., Sadeghi, K., Fazeli, A. et al. Predicting the Weight of the Steel Moment-Resisting Frame Structures Using Artificial Neural Networks. Int J Steel Struct 19, 168–180 (2019). https://doi.org/10.1007/s13296-018-0105-z
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DOI: https://doi.org/10.1007/s13296-018-0105-z