Anticipation of Bending Reinforcement Based on Iranian Concrete Code Using Meta-Heuristic Tools
In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131828Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 574
 Badde, Dheeraj S., Anil K. Gupta, and Vinayak K. Patki "Comparison of Fuzzy Logic and ANFIS for Prediction of Compressive Strength of RMC." Journal of Mechanical and Civil Engineering 3 (2013): 07-15.
 Zain, M. F. M., and S. M. Abd "Multiple regression model for compressive of strength prediction of high performance concrete." Journal of Applied Sciences 9 (2009): 155-160.
 Atici, U. "Prediction of the strength of mineral admixture concrete using multi variable regression analysis and an artificial neural network." Journal of Expert Systems with Applications 38 (2011): 9609-9618.
 Deshpande, Neela, Shreenivas Londhe, and Sushma Kulkami "Modeling compressive strength of recycled aggregate concrete by artificial neural network model tree and non-linear regression." Journal of Sustainable Built Environment 3 (2014): 187-198.
 Islam, Mohammad S., and Shahria Alam."Principle component and multiple regression analysis for steel fiber reinforced concrete (SFRC) beams." Journal of Concrete Structures and Materials 7 (2013): 303-317.
 Özcan, Fatih, Cengiz D. Atiş, Okan Karahan, Erdal Uncuoğlu, and Harun Tanyildizi."Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete." Journal of Advances in Engineering Software 40 (2009): 856-863.
 Tayfur, G., Erdem, T., and Kırca, Ö. "Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks." Journal of Materials in Civil Engineering 26 (2014).
 Chen, Li. "linear regression prediction of concrete compressive strength based on physical properties of electric arc furnace oxidizing slag." Journal of Applied Science and Engineering 7 (2010): 153-158.
 Zain, M. F. M., Suhad M. Abd, K. Sopian, M. Jamil, and Che-Ani A. I, Mathematical regression model for the prediction of concrete strength: 10th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, 2008.
 Aggarwal, Paratibha, Yogesh Aggarwal, Rafat Siddique, Sakshi Gupta, and Harshit Garg. "Fuzzy logic modeling of compressive strength of high strength concrete (HSC) with supplementary cementitious material " Journal of Sustainable Cement-Based Materials 2 (2013): 128-143.
 Kaveh, Ali, and Omid Sabzi. "A comparative study of two meta-heuristic algorithms for optimum design of reinforced concrete frames." International Journal of Civil Engineering 9 (2011): 193-206.
 Alizadeh, Ali R., M. Chini, P. Ghods, and Rouhollah Alizadeh, Assimilation of Iranian concrete code (ABA) compared to ACI code about the mechanical properties of HSC beams subjected to pure bending moment: proceedings of International Conference of Construction Materials: Performance, Innovations and Structural Implications, 2005.
 Mostofinejad, D. Reinforced Concrete Structures Based on ACI 318-05 and Iranian Concrete Code, volume 1, Arkan-e-Danesh press, 2013, (in Persian).