References
- 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, 344(5), 741-756. https://doi.org/10.1016/j.jfranklin.2005.12.005
- 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, 62(6), 405-414. https://doi.org/10.1680/macr.2010.62.6.405
- Azad, A., Ahmad, S., & Azher, S. A. (2007). Residual strength of corrosion-damaged reinforced concrete beams. ACI Material Journal, 104(1), 40-47.
- 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, 33(2), 195-221. https://doi.org/10.1007/s10928-006-9004-6
- Cabrera, J. (1996). Deterioration of concrete due to reinforcement steel corrosion. Cement & Concrete Composites, 18(1), 47-59. https://doi.org/10.1016/0958-9465(95)00043-7
- Castillo, E., Gutierrez, J. M., Hadi, A. S., & Lacruz, B. (2001). Some applications of functional networks in statistics and engineering. Technometrics, 43, 10-24. https://doi.org/10.1198/00401700152404282
- Chen, H., Tsai, K., Qi, G., Yang, J., & Amini, F. (1995). Neural network for structure control. Journal of Computing in Civil Engineering, 9(2), 168-176. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:2(168)
- 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, 130(8), 1214-1224. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:8(1214)
- 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, 42, 48.
- Flood, I., & Kartam, N. (1994). Neural networks in civil engineering. II: Systems and application. Journal of Computing in Civil Engineering, 8(2), 149-162. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(149)
- Guler, I. (2005). ECG beat classifier designed by combined neural network model. Pattern Recognition, 38(2), 199-208. https://doi.org/10.1016/S0031-3203(04)00276-6
- Hasancebi, O., & Dumlupinar, T. (2013). Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks. Computers & Structures, 119, 1-11. https://doi.org/10.1016/j.compstruc.2012.12.017
- 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. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:1(49)
- Huang, R., & Yang, C. (1997). Condition assessment of reinforced concrete beams relative to reinforcement corrosion. Cement & Concrete Composites, 19(2), 131-137. https://doi.org/10.1016/S0958-9465(96)00050-9
- 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, 53(12), 2507-2515. https://doi.org/10.1109/TBME.2006.880879
- Inel, M. (2007). Modeling ultimate deformation capacity of RC columns using artificial neural networks. Engineering Structures, 29(3), 329-335. https://doi.org/10.1016/j.engstruct.2006.05.001
- 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), 2(3), 298-308. https://doi.org/10.1631/jzus.2001.0298
- Kang, H. T., & Yoon, C. J. (1994). Neural network approaches to aid simple truss design problems. Computer-Aided Civil and Infrastructure Engineering, 9(3), 211-218. https://doi.org/10.1111/j.1467-8667.1994.tb00374.x
- 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, 17(4), 42-44.
- Mangat, P. S., & Elgarf, M. S. (1999). Flexural strength of concrete beams with corroding reinforcement. ACI Structural Journal, 96(1), 149-158.
- 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, 26(1), 199-206. https://doi.org/10.1590/S0104-66322009000100019
- Mohaghegh, S. (1995). Neural network: What it can do for petroleum engineers. Journal of Petroleum Technology, 47(1), 42-42. https://doi.org/10.2118/29219-PA
- Nascimento, C. A. O., Giudici, R., & Guardani, R. (2000). Neural network based approach for optimization of industrial chemical processes. Computers & Chemical Engineering, 24(9), 2303-2314. https://doi.org/10.1016/S0098-1354(00)00587-1
- Neaupane, K. M., & Adhikari, N. (2006). Prediction of tunneling-induced ground movement with the multi-layer perceptron. Tunnelling and Underground Space Technology, 21(2), 151-159. https://doi.org/10.1016/j.tust.2005.07.001
- 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, 41(4), 593-604. https://doi.org/10.1002/eqe.1145
- Pandey, P., & Barai, S. (1995). Multilayer perceptron in damage detection of bridge structures. Computers & Structures, 54(4), 597-608. https://doi.org/10.1016/0045-7949(94)00377-F
- 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, 18(2), 329-343. https://doi.org/10.1109/TNN.2006.884677
- Rafiq, M., Bugmann, G., & Easterbrook, D. (2001). Neural network design for engineering applications. Computers & Structures, 79(17), 1541-1552. https://doi.org/10.1016/S0045-7949(01)00039-6
- Ravindrarajah, R. S., & Ong, K. (1987). Corrosion of steel in concrete in relation to bar diameter and cover thickness. ACI Special Publication, 100, 1667-1678.
- 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, 2(2), 324-327. https://doi.org/10.3844/ajeassp.2009.324.327
- Rodriguez, J., Ortega, L., & Casal, J. (1997). Load carrying capacity of concrete structures with corroded reinforcement. Construction and Building Materials, 11(4), 239-248. https://doi.org/10.1016/S0950-0618(97)00043-3
- 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, 16(1), 49-58. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:1(49)
- Ubeyli, E. D. (2009). Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing, 19(2), 297-308. https://doi.org/10.1016/j.dsp.2008.07.004
- Uomoto, T., & Misra, S. (1988). Behavior of concrete beams and columns in marine environment when corrosion of reinforcing bars takes place. ACI Special Publication, 109, 127-146.
- VanLuchene, R., & Sun, R. (1990). Neural networks in structural engineering. Computer-Aided Civil and Infrastructure Engineering, 5(3), 207-215. https://doi.org/10.1111/j.1467-8667.1990.tb00377.x
- Wang, X. H., & Liu, X. L. (2008). Modeling the flexural carrying capacity of corroded RC beam. Journal of Shanghai Jiaotong University (Science), 13(2), 129-135. https://doi.org/10.1007/s12204-008-0129-1
- Waszczyszyn, Z., & Ziemianski, L. (2001). Neural networks in mechanics of structures and materials-new results and prospects of applications. Computers & Structures, 79(22), 2261-2276. https://doi.org/10.1016/S0045-7949(01)00083-9
- Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. https://doi.org/10.1109/4235.585893
- Wu, X., Ghaboussi, J., & Garrett, J. H. (1992). Use of neural networks in detection of structural damage. Computers & Structures, 42(4), 649-659. https://doi.org/10.1016/0045-7949(92)90132-J
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