Abdalla, J. A., Elsanosi, A., & Abdelwahab, A. (2007). Modeling and simulation of shear resistance of R/C beams using artificial neural network. Journal of the Franklin Institute,
Al-Gohi, B. H. A. (2008). Time-dependent modeling of loss of flexural strength of corroding RC beams. Master Thesis, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Arora, S., & Barak, B. (2009). Computational complexity: a modern approach (1st ed.). Cambridge, UK: Cambridge University Press.
Azad, A., Ahmad, S., & Al-Gohi, B. (2010). Flexural strength of corroded reinforced concrete beams. Magazine of Concrete Research,
Azad, A., Ahmad, S., & Azher, S. A. (2007). Residual strength of corrosion-damaged reinforced concrete beams. ACI Material Journal,
Baughman, D. R. (1995). Neural networks in bioprocessing and chemical engineering. PhD Dissertation, Virginia Tech, Blacksburg, VA.
Beale, M., & Demuth, H. (2013). Neural network toolbox user’s guide. Natick, MA: The Mathworks Inc.
Bies, R. R., Muldoon, M. F., Pollock, B. G., Manuck, S., Smith, G., & Sale, M. E. (2006). A genetic algorithm-based hybrid machine learning approach to model selection. Journal of Pharmacokinetics and Pharmacodynamics,
Cabrera, J. (1996). Deterioration of concrete due to reinforcement steel corrosion. Cement & Concrete Composites,
Castillo, E., Gutiérrez, J. M., Hadi, A. S., & Lacruz, B. (2001). Some applications of functional networks in statistics and engineering. Technometrics,
Chen, H., Tsai, K., Qi, G., Yang, J., & Amini, F. (1995). Neural network for structure control. Journal of Computing in Civil Engineering,
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms. Cambridge, MA: MIT press.
Coronelli, D., & Gambarova, P. (2004). Structural assessment of corroded reinforced concrete beams: modeling guidelines. Journal of Structural Engineering,
Eskandari, H., Rezaee, M. R., & Mohammadnia, M. (2004). Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data fora carbonate reservoir, South-West Iran. CSEG Recorder,
Flood, I., & Kartam, N. (1994). Neural networks in civil engineering. II: Systems and application. Journal of Computing in Civil Engineering,
Guler, I. (2005). ECG beat classifier designed by combined neural network model. Pattern Recognition,
Hasancebi, O., & Dumlupınar, T. (2013). Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks. Computers & Structures,
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). Berlin, Germany: Springer.
Helmy, T., Anifowose, F. A., & Sallam, E. S. (2010). An efficient randomized algorithm for real-time process scheduling in PicOS operating system. In K. Elleithy (Ed.), Advanced techniques in computing sciences and software engineering (pp. 117–122). New York, NY: Springer.
Hsu, D. S., & Chung, H. T. (2002). Diagnosis of reinforced concrete structural damage base on displacement time history using the back-propagation neural network technique. Journal of Computing in civil engineering, 16(1), 49–58.
Huang, R., & Yang, C. (1997). Condition assessment of reinforced concrete beams relative to reinforcement corrosion. Cement & Concrete Composites,
Inan, O. T., Giovangrandi, L., & Kovacs, G. T. (2006). Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Transactions on Biomedical Engineering,
Inel, M. (2007). Modeling ultimate deformation capacity of RC columns using artificial neural networks. Engineering Structures,
Jefferys, W. H. & Berger, J. O. (1991). Sharpening Ockham’s Razor on a Bayesian Strop. Technical Report #91-44C, Department of Statistics, Purdue University, West Lafayette, IN.
Jin, W.-L., & Zhao, Y.-X. (2001). Effect of corrosion on bond behavior and bending strength of reinforced concrete beams. Journal of Zhejiang University (Science),
Kang, H. T., & Yoon, C. J. (1994). Neural network approaches to aid simple truss design problems. Computer-Aided Civil and Infrastructure Engineering,
Kirkegaard, P. H. & Rytter, A. (1994). Use of neural networks for damage assessment in a steel mast. In Proceedings of the 12th International Modal Analysis Conference of the
Society for Experimental Mechanics. Honolulu, HI.
Li, L., & Jiao, L. (2002). Prediction of the oilfield output under the effects of nonlinear factors by artificial neural network. Journal of Xi’an Petroleum Institute,
Mangat, P. S., & Elgarf, M. S. (1999). Flexural strength of concrete beams with corroding reinforcement. ACI Structural Journal,
Moghadassi, A., Parvizian, F., Hosseini, S. M., & Fazlali, A. (2009). A new approach for estimation of PVT properties of pure gases based on artificial neural network model. Brazilian Journal of Chemical Engineering,
Mohaghegh, S. (1995). Neural network: What it can do for petroleum engineers. Journal of Petroleum Technology,
Nascimento, C. A. O., Giudici, R., & Guardani, R. (2000). Neural network based approach for optimization of industrial chemical processes. Computers & Chemical Engineering,
Neaupane, K. M., & Adhikari, N. (2006). Prediction of tunneling-induced ground movement with the multi-layer perceptron. Tunnelling and Underground Space Technology,
Nokhasteh, M. A., & Eyre, J. R. (1992) The effect of reinforcement corrosion on the strength of reinforced concrete members. In Proceedings of Structural integrity assessment. London, UK: Elsevier Applied Science.
Ou, Y. C., Tsai, L. L., & Chen, H. H. (2012). Cyclic performance of large-scale corroded reinforced concrete beams. Earthquake Engineering and Structural Dynamics,
Pandey, P., & Barai, S. (1995). Multilayer perceptron in damage detection of bridge structures. Computers & Structures,
Petrus, J. B., Thuijsman, F., & Weijters, A. J. (1995). Artificial neural networks: An introduction to ANN theory and practice. Berlin, Germany: Springer.
Phung, S. L., & Bouzerdoum, A. (2007). A pyramidal neural network for visual pattern recognition. IEEE Transactions on Neural Networks,
Rafiq, M., Bugmann, G., & Easterbrook, D. (2001). Neural network design for engineering applications. Computers & Structures,
Ravindrarajah, R. S., & Ong, K. (1987). Corrosion of steel in concrete in relation to bar diameter and cover thickness. ACI Special Publication,
Revathy, J., Suguna, K., & Raghunath, P. N. (2009). Effect of corrosion damage on the ductility performance of concrete columns. American Journal of Engineering and Applied Sciences,
Rodriguez, J., Ortega, L., & Casal, J. (1997). Load carrying capacity of concrete structures with corroded reinforcement. Construction and Building Materials,
Tachibana, Y., Maeda, K.-I., Kajikawa, Y., & Kawamura, M. (1990). Mechanical behavior of RC beams damaged by corrosion of reinforcement. Elsevier Applied Science, 178–187.
Tsai, C.-H., & Hsu, D.-S. (2002). Diagnosis of reinforced concrete structural damage base on displacement time history using the back-propagation neural network technique. Journal of Computing in Civil Engineering,
Übeyli, E. D. (2009). Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing,
Uomoto, T., & Misra, S. (1988). Behavior of concrete beams and columns in marine environment when corrosion of reinforcing bars takes place. ACI Special Publication,
VanLuchene, R., & Sun, R. (1990). Neural networks in structural engineering. Computer-Aided Civil and Infrastructure Engineering,
Wang, X. H., & Liu, X. L. (2008). Modeling the flexural carrying capacity of corroded RC beam. Journal of Shanghai Jiaotong University (Science),
Waszczyszyn, Z., & Ziemiański, L. (2001). Neural networks in mechanics of structures and materials—new results and prospects of applications. Computers & Structures,
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation,
Wu, X., Ghaboussi, J., & Garrett, J. H. (1992). Use of neural networks in detection of structural damage. Computers & Structures,